بررسی استفاده از انواع روش‌های یادگیری عمیق در امنیت شبکه‌های اینترنت اشیا
محورهای موضوعی : مهندسی کامپیوتر
هادی مهدوی نیا
1
,
محمدرضا سلطان آقایی
2
,
مهدی اسماعیلی
3
1 - دانشکده فنی مهندسي، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، خوراسگان، اصفهان، ايران
2 - دانشکده فنی مهندسي، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، خوراسگان، اصفهان، ايران
3 - دانشکده برق و کامپیوتر، واحد کاشان، دانشگاه آزاد اسلامی، کاشان، اصفهان، ايران
کلید واژه: روش‌های یادگیری عمیق, امنیت شبکه, اینترنت اشیا, امنیت اینترنت اشیا, رویکردهای یادگیری عمیق,
چکیده مقاله :
گسترش مداوم دستگاه‌های هوشمند در بسیاری از جنبه‌های زندگی روزمره ما همراه با تقاضای روزافزون برای مکانیسم‌های مناسب برای اطمینان از مقاومت آنها در برابر انواع مختلف تهدیدات و حملات در محیط اینترنت اشیا است. در این زمینه، یادگیری عمیق به عنوان یکی از موفق‌ترین و مناسب‌ترین تکنیک‌ها برای استفاده در جنبه‌های مختلف امنیت اینترنت اشیا در حال ظهور است. هدف این پژوهش، بررسی و تحلیل سیستماتیک چشم‌انداز تحقیقاتی در مورد رویکردهای یادگیری عمیق اعمال شده در سناریوهای مختلف امنیت اینترنت اشیا است. تحقیقات بررسی شده، بر اساس دیدگاه‌های مختلف در یک طبقه‌بندی منسجم و ساختاریافته به منظور شناسایی شکاف در این حوزه تحقیقاتی محوری طبقه‌بندی می‌شوند. این تحقیق بر روی مقالات مرتبط با کلمات کلیدی "یادگیری عمیق"، "امنیت" و "اینترنت اشیا" در چهار پایگاه داده اصلی IEEEXplore، ScienceDirect، SpringerLink و کتابخانه دیجیتال ACM متمرکز شده است. در پایان، 90 مقاله، انتخاب و بررسی شده است. این مطالعات با توجه به سه سؤال اصلی تحقیق، یعنی جنبه‌های امنیتی درگیر، معماری‌های شبکه یادگیری عمیق مورد استفاده و مجموعه داده‌های مورد استفاده در زمینه امنیت اینترنت اشیا انجام می‌شود. بحث نهایی، شکاف‌های تحقیقاتی را که باید بررسی شوند و اشکالات و آسیب‌پذیری‌های رویکردهای یادگیری عمیق در سناریوی امنیت اینترنت اشیا را برجسته می‌کند.
چکیده انگلیسی:
The development of smart devices in many aspects of our daily lives is accompanied by the increasing use of appropriate mechanisms to counter them against various attacks and applications in the Internet of Things environment. In this context, it is emerging as one of the most successful and suitable techniques for use in various aspects of IoT security. The aim of this is to systematically review and analyze research studies on research eyes conducted in different Internet of Things security scenarios. The reviewed researches are classified according to different perspectives in a coherent and structured classification to identify the gap in this research area. This research has been published on articles related to the keywords "concept learning", "security" and "Internet of Things" in the four main databases IEEEXplore, ScienceDirect, SpringerLink, and ACM Digital Library. In the end, 90 articles have been selected and reviewed. These studies are conducted according to three main research questions, i.e. the security aspects involved, the network architectures used, and the datasets used in IoT security. The final discussion explores the research gaps and acknowledges the outstanding flaws and vulnerabilities in the IoT security scenario.
منابع و مأخذ:
[1] H. Tahaei, F. Afifi, A. Asemi, F. Zaki, and N. B. Anuar, “The rise of traffic classification in IoT networks: A survey,” Journal of Network and Computer Applications, vol. 154, p. 102538, 2020. doi: 10.1016/j.jnca.2020.102538.
[2] R. Pecori, “A PKI-free key agreement protocol for P2P VoIP applications,” in 2012 IEEE International Conference on Communications (ICC), 2012, pp. 6748–6752. doi: 10.1109/ICC.2012.6364948.
[3] R. Bonetto, I. Sychev, O. Zhdanenko, A. Abdelkader, and F. H. P. Fitzek, “Smart Grids for Smarter Cities,” in 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, pp. 1–2. doi: 10.1109/CCNC46108.2020.9045309.
[4] R. Pecori, “Augmenting Quality of Experience in Distance Learning Using Fog Computing,” IEEE Internet Comput, vol. 23, no. 5, pp. 49–58, 2019. doi: 10.1109/MIC.2019.2936754.
[5] M. Calabretta, R. Pecori, and L. Veltri, A Token-based Protocol for Securing MQTT Communications. 2018. doi: 10.23919/SOFTCOM.2018.8555834.
[6] M. Calabretta, R. Pecori, M. Vecchio, and L. Veltri, “MQTT-Auth: a Token-based Solution to Endow MQTT with Authentication and Authorization Capabilities,” Journal of Communications Software and Systems, vol. 14, Dec. 2018. doi: 10.24138/jcomss.v14i4.604.
[7] A. Tayebi, L. Veltri, R. Pecori, and A. Vannucci, IoT Attack Detection with Deep Learning Analysis. 2020. doi: 10.1109/IJCNN48605.2020.9207171.
[8] G. Perrone, M. Vecchio, R. Pecori, and R. Giaffreda, The Day After Mirai: A Survey on MQTT Security Solutions After the Largest Cyber-attack Carried Out through an Army of IoT Devices, 2017. doi: 10.5220/0006287302460253.
[9] I. goodfellow, Y. bengio, A. courville, “Machine Learning basics”, Deep Learning, MIT Press, 2016, pp. 95–151. doi: 10.1007/s10710-017-9314-z.
[10] L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and trends® in signal processing, vol. 7, no. 3–4, pp. 197–387, 2014. doi: 10.1561/2000000039.
[11] A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z Med Phys, vol. 29, no. 2, pp. 102–127, 2019. doi: 10.1016/j.zemedi.2018.11.002.
[12] H. M. Fayek, M. Lech, and L. Cavedon, “Evaluating deep learning architectures for speech emotion recognition,” Neural Networks, vol. 92, pp. 60–68, 2017. doi: 10.1016/j.neunet.2017.02.013.
[13] G. H.-J. Kwak and P. Hui, “DeepHealth: Review and challenges of artificial intelligence in health informatics,” arXiv preprint arXiv:1909.00384, 2019. doi:10.48550/arXiv.1909.00384.
[14] M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
[15] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IoT security: Current solutions and future challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020. doi: 10.1109/COMST.2020.2986444.
[16] L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?,” IEEE Signal Process Mag, vol. 35, no. 5, pp. 41–49, 2018. doi: 10.1109/MSP.2018.2825478.
[17] A. M. Aleesa, B. B. Zaidan, A. A. Zaidan, and N. M. Sahar, “Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions,” Neural Comput Appl, vol. 32, pp. 9827–9858, 2020. doi: 10.1007/s00521-019-04557-3.
[18] D. S. Berman, A. L. Buczak, J. S. Chavis, and C. L. Corbett, “A survey of deep learning methods for cyber security,” Information, vol. 10, no. 4, p. 122, 2019. doi: 10.3390/info10040122.
[19] F. Liang, W. G. Hatcher, W. Liao, W. Gao, and W. Yu, “Machine learning for security and the internet of things: the good, the bad, and the ugly,” Ieee Access, vol. 7, pp. 158126–158147, 2019. doi: 10.1109/ACCESS.2019.2948912.
[20] L. Xiao, D. Jiang, D. Xu, W. Su, N. An, and D. Wang, “Secure mobile crowdsensing based on deep learning,” China Communications, vol. 15, no. 10, pp. 1–11, 2018, doi: 10.1109/CC.2018.8485464.
[21] W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE access, vol. 6, pp. 24411–24432, 2018. doi: 10.1109/ACCESS.2018.2830661.
[22] M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications, vol. 50, p. 102419, 2020. doi: 10.1016/j.jisa.2019.102419.
[23] R. Vishwakarma and A. K. Jain, “A survey of DDoS attacking techniques and defence mechanisms in the IoT network,” Telecommun Syst, vol. 73, no. 1, pp. 3–25, 2020. doi: 10.1007/s11235-019-00599-z.
[24] M. M. Salim, S. Rathore, and J. H. Park, “Distributed denial of service attacks and its defenses in IoT: a survey,” J Supercomput, vol. 76, pp. 5320–5363, 2020. doi: 10.1007/s11227-019-02945-z.
[25] N. Koroniotis, N. Moustafa, and E. Sitnikova, “Forensics and deep learning mechanisms for botnets in internet of things: A survey of challenges and solutions,” IEEE Access, vol. 7, pp. 61764–61785, 2019. doi: 10.1109/ACCESS.2019.2916717.
[26] Ö. A. Aslan and R. Samet, “A comprehensive review on malware detection approaches,” IEEE access, vol. 8, pp. 6249–6271, 2020. doi: 10.1109/ACCESS.2019.2963724.
[27] Z. Ullah, F. Al-Turjman, L. Mostarda, and R. Gagliardi, “Applications of artificial intelligence and machine learning in smart cities,” Computer Communications, vol. 154, pp. 313–323, 2020. doi: 10.1016/j.comcom.2020.02.069.
[28] M. A. Amanullah, R. A. A. Habeeb, F. H. Nasaruddin, A. Gani, E. Ahmed, A. S. M. Nainar, N. M. Akim and M. Imran, “Deep learning and big data technologies for IoT security,” Computer Communications, vol. 151, pp. 495–517, 2020. doi: 10.1016/j.comcom.2020.01.016.
[29] M. Aly, F. Khomh, M. Haoues, A. Quintero, and S. Yacout, “Enforcing security in Internet of Things frameworks: A systematic literature review,” Internet of Things, vol. 6, p. 100050, 2019. doi: 10.1016/j.iot.2019.100050.
[30] B. Kitchenham, “Procedures for performing systematic reviews,” Keele University, vol. 33, no. 2004, pp. 1–26, 2004.
[31] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE communications surveys & tutorials, vol. 17, no. 4, pp. 2347–2376, 2015. doi: 10.1109/COMST.2015.2444095.
[32] F. A. Alaba, M. Othman, I. A. T. Hashem, and F. Alotaibi, “Internet of Things security: A survey,” Journal of Network and Computer Applications, vol. 88, pp. 10–28, 2017. doi: 10.1016/j.jnca.2017.04.002.
[33] Z. Yang, Y. Yue, Y. Yang, Y. Peng, X. Wang, and W. Liu, “Study and application on the architecture and key technologies for IOT,” in 2011 international conference on multimedia technology, IEEE, 2011, pp. 747–751. doi: 10.1109/ICMT.2011.6002149.
[34] R. Pecori, P. Ducange, and F. Marcelloni, “Incremental learning of fuzzy decision trees for streaming data classification,” in 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), Atlantis Press, 2019, pp. 748–755. doi: 0.2991/eusflat-19.2019.102.
[35] P. Sethi and S. R. Sarangi, “Internet of things: architectures, protocols, and applications,” Journal of electrical and computer engineering, vol. 2017, no. 1, p. 9324035, 2017. doi: 10.1155/2017/9324035.
[36] P. Thubert, C. Bormann, L. Toutain, and R. Cragie, “IPv6 over low-power wireless personal area network (6LoWPAN) routing header,” 2017.
[37] T. Winter et al., “RPL: IPv6 routing protocol for low-power and lossy networks,” 2012.
[38] M. A. Razzaque, M. Milojevic-Jevric, A. Palade, and S. Clarke, “Middleware for internet of things: a survey,” IEEE Internet of Things Journal, vol. 3, no. 1, pp. 70–95, 2015. doi: 10.1109/JIOT.2015.2498900.
[39] E. Ahmed, I. Yaqoob, I. A. T. Hashem, I. Khan, A. I. A. Ahmed, M. Imran, A. V. Vasilakos, “The role of big data analytics in Internet of Things,” Computer Networks, vol. 129, pp. 459–471, 2017. doi: 10.1016/j.comnet.2017.06.013.
[40] P. Ducange, R. Pecori, and P. Mezzina, “A glimpse on big data analytics in the framework of marketing strategies,” Soft Computing, vol. 22, no. 1, pp. 325–342, 2018. doi: 10.1007/s00500-017-2536-4.
[41] I. Andrea, C. Chrysostomou, and G. Hadjichristofi, “Internet of Things: Security vulnerabilities and challenges,” in 2015 IEEE symposium on computers and communication (ISCC), IEEE, 2015, pp. 180–187. doi: 10.1109/ISCC.2015.7405513.
[42] M. Anirudh, S. A. Thileeban, and D. J. Nallathambi, “Use of honeypots for mitigating DoS attacks targeted on IoT networks,” in 2017 International conference on computer, communication and signal processing (ICCCSP), IEEE, 2017, pp. 1–4. doi: 10.1109/ICCCSP.2017.7944057.
[43] G. Han, L. Xiao, and H. V Poor, “Two-dimensional anti-jamming communication based on deep reinforcement learning,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2087–2091. doi: 10.1109/ICASSP.2017.7952524.
[44] A. Merlo, M. Migliardi, and P. Fontanelli, “Measuring and estimating power consumption in Android to support energy-based intrusion detection,” Journal of Computer Security, vol. 23, pp. 611–637, 2015, doi: 10.3233/JCS-150530.
[45] M. Migliardi and A. Merlo, “Improving energy efficiency in distributed intrusion detection systems,” Journal of High Speed Networks, vol. 19, pp. 251–264, 2013, doi: 10.3233/JHS-130476.
[46] L. Xiao, Y. Li, G. Han, G. Liu, and W. Zhuang, “PHY-layer spoofing detection with reinforcement learning in wireless networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 10037–10047, 2016. doi: 10.1109/TVT.2016.2524258.
[47] R. Pecori and L. Veltri, “A key agreement protocol for P2P VoIP applications,” in SoftCOM 2009 - 17th International Conference on Software, Telecommunications & Computer Networks, 2009, pp. 276–280.
[48] L. Xiao, Y. Li, X. Huang, and X. Du, “Cloud-Based Malware Detection Game for Mobile Devices with Offloading,” IEEE Transactions on Mobile Computing, vol. 16, no. 10, pp. 2742–2750, 2017, doi: 10.1109/TMC.2017.2687918.
[49] P. Srivastava, H. Peng, J. Li, H. Okhravi, H. Shrobe, and M. Payer, “Firmfuzz: Automated iot firmware introspection and analysis,” in Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things, 2019, pp. 15–21. doi: 10.1145/3338507.3358616.
[50] N. Dejon, D. Caputo, L. Verderame, A. Armando, and A. Merlo, “Automated security analysis of IoT software updates,” in IFIP International Conference on Information Security Theory and Practice, Springer, 2019, pp. 223–239. doi: 10.1007/978-3-030-41702-4_14.
[51] Z. Yan, P. Zhang, and A. V Vasilakos, “A survey on trust management for Internet of Things,” Journal of network and computer applications, vol. 42, pp. 120–134, 2014. doi: 10.1016/j.jnca.2014.01.014.
[52] M. W. Gardner and S. R. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmospheric Environment, vol. 32, no. 14–15, pp. 2627–2636, 1998. doi: 10.1016/S1352-2310(97)00447-0.
[53] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (1979), vol. 349, no. 6245, pp. 255–260, 2015. doi: 10.1126/science.aaa8415.
[54] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer, 2009. doi: 10.1007/978-0-387-21606-5.
[55] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science (1979), vol. 313, no. 5786, pp. 504–507, 2006. doi: 10.1126/science.1127647.
[56] F. Hussain, A. Anpalagan, A. S. Khwaja, and M. Naeem, “Resource allocation and congestion control in clustered M2M communication using Q‐learning,” Transactions on Emerging Telecommunications Technologies, vol. 28, no. 4, p. e3039, 2017. doi:10.1002/ett.3039.
[57] X.-W. Chen and X. Lin, “Big data deep learning: challenges and perspectives,” IEEE access, vol. 2, pp. 514–525, 2014. doi: 10.1109/ACCESS.2014.2325029.
[58] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539.
[59] H. F. Nweke, Y. W. Teh, M. A. Al-Garadi, and U. R. Alo, “Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges,” Expert Systems with Applications, vol. 105, pp. 233–261, 2018. doi: 10.1016/j.eswa.2018.03.056.
[60] R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International conference on machine learning, Pmlr, 2013, pp. 1310–1318.
[61] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018. doi: 10.1109/COMST.2018.2844341.
[62] G. E. Hinton, “A Practical Guide to Training Restricted Boltzmann Machines,” in Neural Networks: Tricks of the Trade: Second Edition, G. Montavon, G. B. Orr, and K.-R. Müller, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 599–619. doi: 10.1007/978-3-642-35289-8_32.
[63] Q. Zhang, L. T. Yang, Z. Chen, and P. Li, “A survey on deep learning for big data,” Information Fusion, vol. 42, pp. 146–157, 2018, doi: https://doi.org/10.1016/j.inffus.2017.10.006.
[64] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, SH. Ozair, A. Courville, Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems, vol. 27, 2014. doi: 10.48550/arXiv.1406.2661.
[65] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in Neural Information Processing Systems, vol. 29, 2016. doi: 10.48550/arXiv.1606.03498.
[66] L. I. Kuncheva, Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014. doi:10.1002/0471660264.
[67] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S.Legg and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015. doi: 10.1038/nature14236.
[68] Y. Liu, H. Wang, M. Peng, J. Guan, J. Xu, and Y. Wang, “DeePGA: A privacy-preserving data aggregation game in crowdsensing via deep reinforcement learning,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4113–4127, 2019. doi: 10.1109/JIOT.2019.2957400. [69] X. He, R. Jin, and H. Dai, “Deep PDS-learning for privacy-aware offloading in MEC-enabled IoT,” IEEE Internet Things Journal, vol. 6, no. 3, pp. 4547–4555, 2018. doi: 10.1109/JIOT.2018.2878718. [70] R. Meng, Q. Cui, Z. Zhou, Z. Fu, and X. Sun, “A steganography algorithm based on CycleGAN for covert communication in the Internet of Things,” IEEE Access, vol. 7, pp. 90574–90584, 2019. doi: 10.1109/ACCESS.2019.2920956. [71] Y. Yan, Q. Pei, and H. Li, “Privacy-preserving compressive model for enhanced deep-learning-based service provision system in edge computing,” IEEE Access, vol. 7, pp. 92921–92937, 2019. doi: 10.1109/ACCESS.2019.2927163. [72] S. A. Osia, A. S. Shamsabadi, A. Taheri, H. R. Rabiee, and H. Haddadi, “Private and scalable personal data analytics using hybrid edge-to-cloud deep learning,” Computer (Long Beach Calif), vol. 51, no. 5, pp. 42–49, 2018. doi: 10.1109/MC.2018.2381113.
[73] S. Jiang, D. Ye, J. Huang, Y. Shang, and Z. Zheng, “SmartSteganogaphy: Light-weight generative audio steganography model for smart embedding application,” Journal of Network and Computer Applications, vol. 165, p. 102689, 2020. doi: 10.1016/j.jnca.2020.102689.
[74] A. Dawoud, S. Shahristani, and C. Raun, “Deep learning and software-defined networks: Towards secure IoT architecture,” Internet of Things, vol. 3, pp. 82–89, 2018. doi: 10.1016/j.iot.2018.09.003.
[75] X. Yan, Y. Xu, X. Xing, B. Cui, Z. Guo, and T. Guo, “Trustworthy network anomaly detection based on an adaptive learning rate and momentum in IIoT,” IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6182–6192, 2020. doi: 10.1109/TII.2020.2975227.
[76] H. Yao, P. Gao, P. Zhang, J. Wang, C. Jiang, and L. Lu, “Hybrid intrusion detection system for edge-based IIoT relying on machine-learning-aided detection,” IEEE Network, vol. 33, no. 5, pp. 75–81, 2019. doi: 10.1109/MNET.001.1800479.
[77] S. Dou, K. Yang, and H. V. Poor, “Pc2a: predicting collective contextual anomalies via LSTM with deep generative model,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9645–9655, 2019. doi: 10.1109/JIOT.2019.2930202.
[78] H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, and Z. Han, “Capsule network assisted IoT traffic classification mechanism for smart cities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7515–7525, 2019. doi: 10.1109/JIOT.2019.2901348.
[79] A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning,” IEEE transactions on sustainable computing, vol. 4, no. 1, pp. 88–95, 2018. doi: 10.1109/TSUSC.2018.2809665.
[80] H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems, vol. 85, pp. 88–96, 2018. doi: 10.1016/j.future.2018.03.007.
[81] Z. Ren, H. Wu, Q. Ning, I. Hussain, and B. Chen, “End-to-end malware detection for android IoT devices using deep learning,” Ad Hoc Networks, vol. 101, p. 102098, 2020. doi: 10.1016/j.adhoc.2020.102098.
[82] S. Huda, S. Miah, J. Yearwood, S. Alyahya, H. Al-Dossari, and R. Doss, “A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network,” Journal of Parallel and Distributed Computing, vol. 120, pp. 23–31, 2018. doi: 10.1016/j.jpdc.2018.04.005.
[83] E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “MalDozer: Automatic framework for android malware detection using deep learning,” Digital Investigation, vol. 24, pp. S48–S59, 2018, doi: 10.1016/j.diin.2018.01.007.
[84] R. Taheri, R. Javidan, and Z. Pooranian, “Adversarial android malware detection for mobile multimedia applications in IoT environments,” Multimedia Tools and Applications, vol. 80, pp. 16713–16729, 2021. doi: 10.1007/s11042-020-08804-x.
[85] R. Taheri, R. Javidan, M. Shojafar, Z. Pooranian, A. Miri, and M. Conti, “On defending against label flipping attacks on malware detection systems,” Neural Computing and Applications, vol. 32, pp. 14781–14800, 2020. doi: 10.1007/s00521-020-04831-9.
[86] P. Mohamed Shakeel, S. Baskar, V. R. Sarma Dhulipala, S. Mishra, and M. M. Jaber, “Retracted article: maintaining security and privacy in health care system using learning based deep-Q-networks,” Journal of Medical Systems, vol. 42, no. 10, p. 186, 2018. doi: 10.1007/s10916-018-1045-z.
[87] H. Naeem, “Detection of malicious activities in internet of things environment based on binary visualization and machine intelligence,” Wireless Personal Communications, vol. 108, no. 4, pp. 2609–2629, 2019. doi: 10.1007/s11277-019-06540-6.
[88] S. Liu, M. Dibaei, Y. Tai, C. Chen, J. Zhang, and Y. Xiang, “Cyber vulnerability intelligence for internet of things binary,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2154–2163, 2019. doi: 10.1109/TII.2019.2942800.
[89] H. Wu, H. Shu, F. Kang, and X. Xiong, “BiN: A two-level learning-based bug search for cross-architecture binary,” IEEE Access, vol. 7, pp. 169548–169564, 2019. doi: 10.1109/ACCESS.2019.2953173.
[90] W. Niu, X. Zhang, X. Du, L. Zhao, R. Cao, and M. Guizani, “A deep learning based static taint analysis approach for IoT software vulnerability location,” Measurement, vol. 152, p. 107139, 2020. doi.org/10.1016/j.measurement.2019.107139. doi: 10.1016/j.measurement.2019.107139.
[91] W. Liang, W. Huang, J. Long, K. Zhang, K.-C. Li, and D. Zhang, “Deep reinforcement learning for resource protection and real-time detection in IoT environment,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6392–6401, 2020. doi: 10.1109/JIOT.2020.2974281.
[92] F. Ullah, H. Naeem, S. Jabbar, Sh. Khalid, M. A. Latif, F. Al-turjman, L. Mostarda , “Cyber security threats detection in internet of things using deep learning approach,” IEEE access, vol. 7, pp. 124379–124389, 2019. doi: 10.1109/ACCESS.2019.2937347.
[93] L. Peng, J. Zhang, M. Liu, and A. Hu, “Deep learning based RF fingerprint identification using differential constellation trace figure,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1091–1095, 2019. doi: 10.1109/TVT.2019.2950670.
[94] K. Sankhe, M. Belgiovine, F. Zhou, L. Angioloni, F. Restuccia, S. D’Oro, T. Melodia, S. Ioannidis, K. Chowdhury, “No radio left behind: Radio fingerprinting through deep learning of physical-layer hardware impairments,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 165–178, 2019. doi: 10.1109/TCCN.2019.2949308.
[95] R.-F. Liao et al., “Security enhancement for mobile edge computing through physical layer authentication,” IEEE Access, vol. 7, pp. 116390–116401, 2019. doi: 10.1109/ACCESS.2019.2934122.
[96] A. Ferdowsi and W. Saad, “Deep learning for signal authentication and security in massive internet-of-things systems,” IEEE Transactions on Communications, vol. 67, no. 2, pp. 1371–1387, 2018. doi: 10.1109/TCOMM.2018.2878025.
[97] B. Chatterjee, D. Das, S. Maity, and S. Sen, “RF-PUF: Enhancing IoT security through authentication of wireless nodes using in-situ machine learning,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 388–398, 2018. doi: 10.1109/JIOT.2018.2849324.
[98] K. Youssef, L. Bouchard, K. Haigh, J. Silovsky, B. Thapa, and C. Vander Valk, “Machine learning approach to RF transmitter identification,” IEEE Journal of Radio Frequency Identification, vol. 2, no. 4, pp. 197–205, 2018. doi: 10.1109/JRFID.2018.2880457.
[99] J. Chauhan, S. Seneviratne, Y. Hu, A. Misra, A. Seneviratne, and Y. Lee, “Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks,” Computer (Long Beach Calif), vol. 51, no. 5, pp. 60–67, 2018. doi: 10.1109/MC.2018.2381119.
[100] S. H. Oh, G.-W. Kim, and K.-S. Lim, “Compact deep learned feature-based face recognition for Visual Internet of Things,” The Journal of Supercomputing, vol. 74, no. 12, pp. 6729–6741, 2018. doi: 10.1007/s11227-017-2198-0.
[101] Y. Zhang et al., “Cyber physical security analytics for transactive energy systems,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 931–941, 2019. doi: 10.1109/TSG.2019.2928168.
[102] F. Li, Y. Shi, A. Shinde, J. Ye, and W. Song, “Enhanced cyber-physical security in internet of things through energy auditing,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5224–5231, 2019. doi: 10.1109/JIOT.2019.2899492.
[103] W. Jung, H. Zhao, M. Sun, and G. Zhou, “IoT botnet detection via power consumption modeling,” Smart Health, vol. 15, p. 100103, 2020. doi: 10.1016/j.smhl.2019.100103.
[104] F. Li, A. Shinde, Y. Shi, J. Ye, X.-Y. Li, and W. Song, “System statistics learning-based IoT security: Feasibility and suitability,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6396–6403, 2019. doi: 10.1109/JIOT.2019.2897063.
[105] M. E. Aminanto, R. Choi, H. C. Tanuwidjaja, P. D. Yoo, and K. Kim, “Deep abstraction and weighted feature selection for Wi-Fi impersonation detection,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 3, pp. 621–636, 2017. doi: 10.1109/TIFS.2017.2762828.
[106] X. Guo, H. Lin, Z. Li, and M. Peng, “Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6242–6251, 2019. doi: 10.1109/JIOT.2019.2960033.
[107] R.-H. Hwang, M.-C. Peng, C.-W. Huang, P.-C. Lin, and V.-L. Nguyen, “An unsupervised deep learning model for early network traffic anomaly detection,” IEEE Access, vol. 8, pp. 30387–30399, 2020. doi: 10.1109/ACCESS.2020.2973023.
[108] M. AL-Hawawreh, N. Moustafa, and E. Sitnikova, “Identification of malicious activities in industrial internet of things based on deep learning models,” Journal of information security and applications, vol. 41, pp. 1–11, 2018. doi: 10.1016/j.jisa.2018.05.002.
[109] P. K. Sharma, S. Singh, and J. H. Park, “OpCloudSec: Open cloud software defined wireless network security for the Internet of Things,” Computer Communications, vol. 122, pp. 1–8, 2018. doi: 10.1016/j.comcom.2018.03.008.
[110] R. M. A. Ujjan, Z. Pervez, K. Dahal, A. K. Bashir, R. Mumtaz, and J. González, “Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN,” Future Generation Computer Systems, vol. 111, pp. 763–779, 2020. doi: 10.1016/j.future.2019.10.015.
[111] Q. Tian, J. Li, and H. Liu, “A method for guaranteeing wireless communication based on a combination of deep and shallow learning,” IEEE Access, vol. 7, pp. 38688–38695, 2019. doi: 10.1109/ACCESS.2019.2905754.
[112] Y. Zhang, P. Li, and X. Wang, “Intrusion detection for IoT based on improved genetic algorithm and deep belief network,” IEEE Access, vol. 7, pp. 31711–31722, 2019. doi: 10.1109/ACCESS.2019.2903723.
[113] A. Telikani and A. H. Gandomi, “Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things,” Internet of Things, vol. 14, p. 100122, 2021. doi: 10.1016/j.iot.2019.100122.
[114] N. Balakrishnan, A. Rajendran, D. Pelusi, and V. Ponnusamy, “Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things,” Internet of things, vol. 14, p. 100112, 2021. doi: 10.1016/j.iot.2019.100112.
[115] A. S. Almogren, “Intrusion detection in Edge-of-Things computing,” Journal of Parallel and Distributed Computing, vol. 137, pp. 259–265, 2020. doi: 10.1016/j.jpdc.2019.12.008.
[116] Y. Li et al., “Robust detection for network intrusion of industrial IoT based on multi-CNN fusion,” Measurement, vol. 154, p. 107450, 2020. doi: 10.1016/j.measurement.2019.107450.
[117] D. Li, L. Deng, M. Lee, and H. Wang, “IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning,” International Journal of Information Management, vol. 49, pp. 533–545, 2019. doi: 10.1016/j.ijinfomgt.2019.04.006.
[118] J. Li and B. Sun, “A network attack detection method using SDA and deep neural network based on internet of things,” International Journal of Wireless Information Networks, vol. 27, no. 2, pp. 209–214, 2020. doi: 10.1007/s10776-019-00462-7.
[119] S. Manimurugan, S. Al-Mutairi, M. M. Aborokbah, N. Chilamkurti, S. Ganesan, and R. Patan, “Effective attack detection in internet of medical things smart environment using a deep belief neural network,” IEEE Access, vol. 8, pp. 77396–77404, 2020. doi: 10.1109/ACCESS.2020.2986013.
[120] X. Wang and X. Zhang, “Wireless network attack defense algorithm using deep neural network in internet of things environment,” International Journal of Wireless Information Networks, vol. 26, pp. 143–151, 2019. doi: 10.1007/s10776-019-00430-1.
[121] A. Abeshu and N. Chilamkurti, “Deep learning: The frontier for distributed attack detection in fog-to-things computing,” IEEE Communications Magazine, vol. 56, no. 2, pp. 169–175, 2018. doi: 10.1109/MCOM.2018.1700332.
[122] A. Diro and N. Chilamkurti, “Leveraging LSTM networks for attack detection in fog-to-things communications,” IEEE Communications Magazine, vol. 56, no. 9, pp. 124–130, 2018. doi: 10.1109/MCOM.2018.1701270
[123] A. A. Diro and N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Future Generation Computer Systems, vol. 82, pp. 761–768, 2018. doi:10.1016/j.future.2017.08.043.
[124] R. Kozik, M. Choraś, M. Ficco, and F. Palmieri, “A scalable distributed machine learning approach for attack detection in edge computing environments,” Journal of Parallel and Distributed Computing, vol. 119, pp. 18–26, 2018. doi: 10.1016/j.jpdc.2018.03.006.
[125] V. M. Krundyshev, “Identifying cyberthreats in modern industrial systems by means of deep-learning networks,” Automatic Control and Computer Sciences, vol. 53, no. 8, pp. 1006–1011, 2019. doi: 10.3103/S014641161908011X.
[126] C. U. Om Kumar and P. R. K. Sathia Bhama, “Detecting and confronting flash attacks from IoT botnets,” The Journal of Supercomputing, vol. 75, pp. 8312–8338, 2019. doi: 10.1007/s11227-019-03005-2.
[127] G. D. L. T. Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting Internet of Things attacks using distributed deep learning,” Journal of Network and Computer Applications, vol. 163, p. 102662, 2020. doi: 10.1016/j.jnca.2020.102662.
[128] M. S. Pour et al., “On data-driven curation, learning, and analysis for inferring evolving internet-of-Things (IoT) botnets in the wild,” Computers & Security, vol. 91, p. 101707, 2020. doi: 10.1016/j.cose.2019.101707
[129] N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,” Future Generation Computer Systems, vol. 100, pp. 779–796, 2019. doi: 10.48550/arXiv.1811.00701.
[130] A. Samy, H. Yu, and H. Zhang, “Fog-based attack detection framework for internet of things using deep learning,” Ieee Access, vol. 8, pp. 74571–74585, 2020. doi: 10.1109/ACCESS.2020.2988854.
[131] Z. Tian, C. Luo, J. Qiu, X. Du, and M. Guizani, “A distributed deep learning system for web attack detection on edge devices,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1963–1971, 2019. doi: 10.1109/TII.2019.2938778.
[132] M. A. Ferrag and L. Maglaras, “DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids,” IEEE Transactions on Engineering Management, vol. 67, no. 4, pp. 1285–1297, 2019. doi: 10.1109/TEM.2019.2922936.
[133] S. Wang and Z. Qiao, “Robust pervasive detection for adversarial samples of artificial intelligence in IoT environments,” IEEE Access, vol. 7, pp. 88693–88704, 2019. doi: 10.1109/ACCESS.2019.2919695.
[134] S. Kim, W. Jo, and T. Shon, “APAD: Autoencoder-based payload anomaly detection for industrial IoE,” Applied Soft Computing, vol. 88, p. 106017, 2020. doi: 10.1016/j.asoc.2019.106017.
[135] M. Li, Y. Sun, H. Lu, S. Maharjan, and Z. Tian, “Deep reinforcement learning for partially observable data poisoning attack in crowdsensing systems,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6266–6278, 2019. doi: 10.1109/JIOT.2019.2962914.
[136] Y. Xiao, Y. Jia, X. Cheng, J. Yu, Z. Liang, and Z. Tian, “I can see your brain: Investigating home-use electroencephalography system security,” IEEE Internet Things J, vol. 6, no. 4, pp. 6681–6691, 2019. doi: 10.1109/JIOT.2019.2910115.
[137] J. Bergstra, D. Yamins, and D. Cox, “Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,” in International conference on machine learning, PMLR, 2013, pp. 115–123.
[1] H. Tahaei, F. Afifi, A. Asemi, F. Zaki, and N. B. Anuar, “The rise of traffic classification in IoT networks: A survey,” Journal of Network and Computer Applications, vol. 154, p. 102538, 2020. doi: 10.1016/j.jnca.2020.102538.
[2] R. Pecori, “A PKI-free key agreement protocol for P2P VoIP applications,” in 2012 IEEE International Conference on Communications (ICC), 2012, pp. 6748–6752. doi: 10.1109/ICC.2012.6364948.
[3] R. Bonetto, I. Sychev, O. Zhdanenko, A. Abdelkader, and F. H. P. Fitzek, “Smart Grids for Smarter Cities,” in 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, pp. 1–2. doi: 10.1109/CCNC46108.2020.9045309.
[4] R. Pecori, “Augmenting Quality of Experience in Distance Learning Using Fog Computing,” IEEE Internet Comput, vol. 23, no. 5, pp. 49–58, 2019. doi: 10.1109/MIC.2019.2936754.
[5] M. Calabretta, R. Pecori, and L. Veltri, A Token-based Protocol for Securing MQTT Communications. 2018. doi: 10.23919/SOFTCOM.2018.8555834.
[6] M. Calabretta, R. Pecori, M. Vecchio, and L. Veltri, “MQTT-Auth: a Token-based Solution to Endow MQTT with Authentication and Authorization Capabilities,” Journal of Communications Software and Systems, vol. 14, Dec. 2018. doi: 10.24138/jcomss.v14i4.604.
[7] A. Tayebi, L. Veltri, R. Pecori, and A. Vannucci, IoT Attack Detection with Deep Learning Analysis. 2020. doi: 10.1109/IJCNN48605.2020.9207171.
[8] G. Perrone, M. Vecchio, R. Pecori, and R. Giaffreda, The Day After Mirai: A Survey on MQTT Security Solutions After the Largest Cyber-attack Carried Out through an Army of IoT Devices, 2017. doi: 10.5220/0006287302460253.
[9] I. goodfellow, Y. bengio, A. courville, “Machine Learning basics”, Deep Learning, MIT Press, 2016, pp. 95–151. doi: 10.1007/s10710-017-9314-z.
[10] L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and trends® in signal processing, vol. 7, no. 3–4, pp. 197–387, 2014. doi: 10.1561/2000000039.
[11] A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z Med Phys, vol. 29, no. 2, pp. 102–127, 2019. doi: 10.1016/j.zemedi.2018.11.002.
[12] H. M. Fayek, M. Lech, and L. Cavedon, “Evaluating deep learning architectures for speech emotion recognition,” Neural Networks, vol. 92, pp. 60–68, 2017. doi: 10.1016/j.neunet.2017.02.013.
[13] G. H.-J. Kwak and P. Hui, “DeepHealth: Review and challenges of artificial intelligence in health informatics,” arXiv preprint arXiv:1909.00384, 2019. doi:10.48550/arXiv.1909.00384.
[14] M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
[15] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IoT security: Current solutions and future challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020. doi: 10.1109/COMST.2020.2986444.
[16] L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?,” IEEE Signal Process Mag, vol. 35, no. 5, pp. 41–49, 2018. doi: 10.1109/MSP.2018.2825478.
[17] A. M. Aleesa, B. B. Zaidan, A. A. Zaidan, and N. M. Sahar, “Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions,” Neural Comput Appl, vol. 32, pp. 9827–9858, 2020. doi: 10.1007/s00521-019-04557-3.
[18] D. S. Berman, A. L. Buczak, J. S. Chavis, and C. L. Corbett, “A survey of deep learning methods for cyber security,” Information, vol. 10, no. 4, p. 122, 2019. doi: 10.3390/info10040122.
[19] F. Liang, W. G. Hatcher, W. Liao, W. Gao, and W. Yu, “Machine learning for security and the internet of things: the good, the bad, and the ugly,” Ieee Access, vol. 7, pp. 158126–158147, 2019. doi: 10.1109/ACCESS.2019.2948912.
[20] L. Xiao, D. Jiang, D. Xu, W. Su, N. An, and D. Wang, “Secure mobile crowdsensing based on deep learning,” China Communications, vol. 15, no. 10, pp. 1–11, 2018, doi: 10.1109/CC.2018.8485464.
[21] W. G. Hatcher and W. Yu, “A survey of deep learning: Platforms, applications and emerging research trends,” IEEE access, vol. 6, pp. 24411–24432, 2018. doi: 10.1109/ACCESS.2018.2830661.
[22] M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications, vol. 50, p. 102419, 2020. doi: 10.1016/j.jisa.2019.102419.
[23] R. Vishwakarma and A. K. Jain, “A survey of DDoS attacking techniques and defence mechanisms in the IoT network,” Telecommun Syst, vol. 73, no. 1, pp. 3–25, 2020. doi: 10.1007/s11235-019-00599-z.
[24] M. M. Salim, S. Rathore, and J. H. Park, “Distributed denial of service attacks and its defenses in IoT: a survey,” J Supercomput, vol. 76, pp. 5320–5363, 2020. doi: 10.1007/s11227-019-02945-z.
[25] N. Koroniotis, N. Moustafa, and E. Sitnikova, “Forensics and deep learning mechanisms for botnets in internet of things: A survey of challenges and solutions,” IEEE Access, vol. 7, pp. 61764–61785, 2019. doi: 10.1109/ACCESS.2019.2916717.
[26] Ö. A. Aslan and R. Samet, “A comprehensive review on malware detection approaches,” IEEE access, vol. 8, pp. 6249–6271, 2020. doi: 10.1109/ACCESS.2019.2963724.
[27] Z. Ullah, F. Al-Turjman, L. Mostarda, and R. Gagliardi, “Applications of artificial intelligence and machine learning in smart cities,” Computer Communications, vol. 154, pp. 313–323, 2020. doi: 10.1016/j.comcom.2020.02.069.
[28] M. A. Amanullah, R. A. A. Habeeb, F. H. Nasaruddin, A. Gani, E. Ahmed, A. S. M. Nainar, N. M. Akim and M. Imran, “Deep learning and big data technologies for IoT security,” Computer Communications, vol. 151, pp. 495–517, 2020. doi: 10.1016/j.comcom.2020.01.016.
[29] M. Aly, F. Khomh, M. Haoues, A. Quintero, and S. Yacout, “Enforcing security in Internet of Things frameworks: A systematic literature review,” Internet of Things, vol. 6, p. 100050, 2019. doi: 10.1016/j.iot.2019.100050.
[30] B. Kitchenham, “Procedures for performing systematic reviews,” Keele University, vol. 33, no. 2004, pp. 1–26, 2004.
[31] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE communications surveys & tutorials, vol. 17, no. 4, pp. 2347–2376, 2015. doi: 10.1109/COMST.2015.2444095.
[32] F. A. Alaba, M. Othman, I. A. T. Hashem, and F. Alotaibi, “Internet of Things security: A survey,” Journal of Network and Computer Applications, vol. 88, pp. 10–28, 2017. doi: 10.1016/j.jnca.2017.04.002.
[33] Z. Yang, Y. Yue, Y. Yang, Y. Peng, X. Wang, and W. Liu, “Study and application on the architecture and key technologies for IOT,” in 2011 international conference on multimedia technology, IEEE, 2011, pp. 747–751. doi: 10.1109/ICMT.2011.6002149.
[34] R. Pecori, P. Ducange, and F. Marcelloni, “Incremental learning of fuzzy decision trees for streaming data classification,” in 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), Atlantis Press, 2019, pp. 748–755. doi: 0.2991/eusflat-19.2019.102.
[35] P. Sethi and S. R. Sarangi, “Internet of things: architectures, protocols, and applications,” Journal of electrical and computer engineering, vol. 2017, no. 1, p. 9324035, 2017. doi: 10.1155/2017/9324035.
[36] P. Thubert, C. Bormann, L. Toutain, and R. Cragie, “IPv6 over low-power wireless personal area network (6LoWPAN) routing header,” 2017.
[37] T. Winter et al., “RPL: IPv6 routing protocol for low-power and lossy networks,” 2012.
[38] M. A. Razzaque, M. Milojevic-Jevric, A. Palade, and S. Clarke, “Middleware for internet of things: a survey,” IEEE Internet of Things Journal, vol. 3, no. 1, pp. 70–95, 2015. doi: 10.1109/JIOT.2015.2498900.
[39] E. Ahmed, I. Yaqoob, I. A. T. Hashem, I. Khan, A. I. A. Ahmed, M. Imran, A. V. Vasilakos, “The role of big data analytics in Internet of Things,” Computer Networks, vol. 129, pp. 459–471, 2017. doi: 10.1016/j.comnet.2017.06.013.
[40] P. Ducange, R. Pecori, and P. Mezzina, “A glimpse on big data analytics in the framework of marketing strategies,” Soft Computing, vol. 22, no. 1, pp. 325–342, 2018. doi: 10.1007/s00500-017-2536-4.
[41] I. Andrea, C. Chrysostomou, and G. Hadjichristofi, “Internet of Things: Security vulnerabilities and challenges,” in 2015 IEEE symposium on computers and communication (ISCC), IEEE, 2015, pp. 180–187. doi: 10.1109/ISCC.2015.7405513.
[42] M. Anirudh, S. A. Thileeban, and D. J. Nallathambi, “Use of honeypots for mitigating DoS attacks targeted on IoT networks,” in 2017 International conference on computer, communication and signal processing (ICCCSP), IEEE, 2017, pp. 1–4. doi: 10.1109/ICCCSP.2017.7944057.
[43] G. Han, L. Xiao, and H. V Poor, “Two-dimensional anti-jamming communication based on deep reinforcement learning,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2087–2091. doi: 10.1109/ICASSP.2017.7952524.
[44] A. Merlo, M. Migliardi, and P. Fontanelli, “Measuring and estimating power consumption in Android to support energy-based intrusion detection,” Journal of Computer Security, vol. 23, pp. 611–637, 2015, doi: 10.3233/JCS-150530.
[45] M. Migliardi and A. Merlo, “Improving energy efficiency in distributed intrusion detection systems,” Journal of High Speed Networks, vol. 19, pp. 251–264, 2013, doi: 10.3233/JHS-130476.
[46] L. Xiao, Y. Li, G. Han, G. Liu, and W. Zhuang, “PHY-layer spoofing detection with reinforcement learning in wireless networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 10037–10047, 2016. doi: 10.1109/TVT.2016.2524258.
[47] R. Pecori and L. Veltri, “A key agreement protocol for P2P VoIP applications,” in SoftCOM 2009 - 17th International Conference on Software, Telecommunications & Computer Networks, 2009, pp. 276–280.
[48] L. Xiao, Y. Li, X. Huang, and X. Du, “Cloud-Based Malware Detection Game for Mobile Devices with Offloading,” IEEE Transactions on Mobile Computing, vol. 16, no. 10, pp. 2742–2750, 2017, doi: 10.1109/TMC.2017.2687918.
[49] P. Srivastava, H. Peng, J. Li, H. Okhravi, H. Shrobe, and M. Payer, “Firmfuzz: Automated iot firmware introspection and analysis,” in Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things, 2019, pp. 15–21. doi: 10.1145/3338507.3358616.
[50] N. Dejon, D. Caputo, L. Verderame, A. Armando, and A. Merlo, “Automated security analysis of IoT software updates,” in IFIP International Conference on Information Security Theory and Practice, Springer, 2019, pp. 223–239. doi: 10.1007/978-3-030-41702-4_14.
[51] Z. Yan, P. Zhang, and A. V Vasilakos, “A survey on trust management for Internet of Things,” Journal of network and computer applications, vol. 42, pp. 120–134, 2014. doi: 10.1016/j.jnca.2014.01.014.
[52] M. W. Gardner and S. R. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmospheric Environment, vol. 32, no. 14–15, pp. 2627–2636, 1998. doi: 10.1016/S1352-2310(97)00447-0.
[53] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (1979), vol. 349, no. 6245, pp. 255–260, 2015. doi: 10.1126/science.aaa8415.
[54] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer, 2009. doi: 10.1007/978-0-387-21606-5.
[55] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science (1979), vol. 313, no. 5786, pp. 504–507, 2006. doi: 10.1126/science.1127647.
[56] F. Hussain, A. Anpalagan, A. S. Khwaja, and M. Naeem, “Resource allocation and congestion control in clustered M2M communication using Q‐learning,” Transactions on Emerging Telecommunications Technologies, vol. 28, no. 4, p. e3039, 2017. doi:10.1002/ett.3039.
[57] X.-W. Chen and X. Lin, “Big data deep learning: challenges and perspectives,” IEEE access, vol. 2, pp. 514–525, 2014. doi: 10.1109/ACCESS.2014.2325029.
[58] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539.
[59] H. F. Nweke, Y. W. Teh, M. A. Al-Garadi, and U. R. Alo, “Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges,” Expert Systems with Applications, vol. 105, pp. 233–261, 2018. doi: 10.1016/j.eswa.2018.03.056.
[60] R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International conference on machine learning, Pmlr, 2013, pp. 1310–1318.
[61] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018. doi: 10.1109/COMST.2018.2844341.
[62] G. E. Hinton, “A Practical Guide to Training Restricted Boltzmann Machines,” in Neural Networks: Tricks of the Trade: Second Edition, G. Montavon, G. B. Orr, and K.-R. Müller, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 599–619. doi: 10.1007/978-3-642-35289-8_32.
[63] Q. Zhang, L. T. Yang, Z. Chen, and P. Li, “A survey on deep learning for big data,” Information Fusion, vol. 42, pp. 146–157, 2018, doi: https://doi.org/10.1016/j.inffus.2017.10.006.
[64] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, SH. Ozair, A. Courville, Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems, vol. 27, 2014. doi: 10.48550/arXiv.1406.2661.
[65] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in Neural Information Processing Systems, vol. 29, 2016. doi: 10.48550/arXiv.1606.03498.
[66] L. I. Kuncheva, Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014. doi:10.1002/0471660264.
[67] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S.Legg and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015. doi: 10.1038/nature14236.
[68] Y. Liu, H. Wang, M. Peng, J. Guan, J. Xu, and Y. Wang, “DeePGA: A privacy-preserving data aggregation game in crowdsensing via deep reinforcement learning,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4113–4127, 2019. doi: 10.1109/JIOT.2019.2957400. [69] X. He, R. Jin, and H. Dai, “Deep PDS-learning for privacy-aware offloading in MEC-enabled IoT,” IEEE Internet Things Journal, vol. 6, no. 3, pp. 4547–4555, 2018. doi: 10.1109/JIOT.2018.2878718. [70] R. Meng, Q. Cui, Z. Zhou, Z. Fu, and X. Sun, “A steganography algorithm based on CycleGAN for covert communication in the Internet of Things,” IEEE Access, vol. 7, pp. 90574–90584, 2019. doi: 10.1109/ACCESS.2019.2920956. [71] Y. Yan, Q. Pei, and H. Li, “Privacy-preserving compressive model for enhanced deep-learning-based service provision system in edge computing,” IEEE Access, vol. 7, pp. 92921–92937, 2019. doi: 10.1109/ACCESS.2019.2927163. [72] S. A. Osia, A. S. Shamsabadi, A. Taheri, H. R. Rabiee, and H. Haddadi, “Private and scalable personal data analytics using hybrid edge-to-cloud deep learning,” Computer (Long Beach Calif), vol. 51, no. 5, pp. 42–49, 2018. doi: 10.1109/MC.2018.2381113.
[73] S. Jiang, D. Ye, J. Huang, Y. Shang, and Z. Zheng, “SmartSteganogaphy: Light-weight generative audio steganography model for smart embedding application,” Journal of Network and Computer Applications, vol. 165, p. 102689, 2020. doi: 10.1016/j.jnca.2020.102689.
[74] A. Dawoud, S. Shahristani, and C. Raun, “Deep learning and software-defined networks: Towards secure IoT architecture,” Internet of Things, vol. 3, pp. 82–89, 2018. doi: 10.1016/j.iot.2018.09.003.
[75] X. Yan, Y. Xu, X. Xing, B. Cui, Z. Guo, and T. Guo, “Trustworthy network anomaly detection based on an adaptive learning rate and momentum in IIoT,” IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6182–6192, 2020. doi: 10.1109/TII.2020.2975227.
[76] H. Yao, P. Gao, P. Zhang, J. Wang, C. Jiang, and L. Lu, “Hybrid intrusion detection system for edge-based IIoT relying on machine-learning-aided detection,” IEEE Network, vol. 33, no. 5, pp. 75–81, 2019. doi: 10.1109/MNET.001.1800479.
[77] S. Dou, K. Yang, and H. V. Poor, “Pc2a: predicting collective contextual anomalies via LSTM with deep generative model,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9645–9655, 2019. doi: 10.1109/JIOT.2019.2930202.
[78] H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, and Z. Han, “Capsule network assisted IoT traffic classification mechanism for smart cities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7515–7525, 2019. doi: 10.1109/JIOT.2019.2901348.
[79] A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning,” IEEE transactions on sustainable computing, vol. 4, no. 1, pp. 88–95, 2018. doi: 10.1109/TSUSC.2018.2809665.
[80] H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems, vol. 85, pp. 88–96, 2018. doi: 10.1016/j.future.2018.03.007.
[81] Z. Ren, H. Wu, Q. Ning, I. Hussain, and B. Chen, “End-to-end malware detection for android IoT devices using deep learning,” Ad Hoc Networks, vol. 101, p. 102098, 2020. doi: 10.1016/j.adhoc.2020.102098.
[82] S. Huda, S. Miah, J. Yearwood, S. Alyahya, H. Al-Dossari, and R. Doss, “A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network,” Journal of Parallel and Distributed Computing, vol. 120, pp. 23–31, 2018. doi: 10.1016/j.jpdc.2018.04.005.
[83] E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “MalDozer: Automatic framework for android malware detection using deep learning,” Digital Investigation, vol. 24, pp. S48–S59, 2018, doi: 10.1016/j.diin.2018.01.007.
[84] R. Taheri, R. Javidan, and Z. Pooranian, “Adversarial android malware detection for mobile multimedia applications in IoT environments,” Multimedia Tools and Applications, vol. 80, pp. 16713–16729, 2021. doi: 10.1007/s11042-020-08804-x.
[85] R. Taheri, R. Javidan, M. Shojafar, Z. Pooranian, A. Miri, and M. Conti, “On defending against label flipping attacks on malware detection systems,” Neural Computing and Applications, vol. 32, pp. 14781–14800, 2020. doi: 10.1007/s00521-020-04831-9.
[86] P. Mohamed Shakeel, S. Baskar, V. R. Sarma Dhulipala, S. Mishra, and M. M. Jaber, “Retracted article: maintaining security and privacy in health care system using learning based deep-Q-networks,” Journal of Medical Systems, vol. 42, no. 10, p. 186, 2018. doi: 10.1007/s10916-018-1045-z.
[87] H. Naeem, “Detection of malicious activities in internet of things environment based on binary visualization and machine intelligence,” Wireless Personal Communications, vol. 108, no. 4, pp. 2609–2629, 2019. doi: 10.1007/s11277-019-06540-6.
[88] S. Liu, M. Dibaei, Y. Tai, C. Chen, J. Zhang, and Y. Xiang, “Cyber vulnerability intelligence for internet of things binary,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2154–2163, 2019. doi: 10.1109/TII.2019.2942800.
[89] H. Wu, H. Shu, F. Kang, and X. Xiong, “BiN: A two-level learning-based bug search for cross-architecture binary,” IEEE Access, vol. 7, pp. 169548–169564, 2019. doi: 10.1109/ACCESS.2019.2953173.
[90] W. Niu, X. Zhang, X. Du, L. Zhao, R. Cao, and M. Guizani, “A deep learning based static taint analysis approach for IoT software vulnerability location,” Measurement, vol. 152, p. 107139, 2020. doi.org/10.1016/j.measurement.2019.107139. doi: 10.1016/j.measurement.2019.107139.
[91] W. Liang, W. Huang, J. Long, K. Zhang, K.-C. Li, and D. Zhang, “Deep reinforcement learning for resource protection and real-time detection in IoT environment,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6392–6401, 2020. doi: 10.1109/JIOT.2020.2974281.
[92] F. Ullah, H. Naeem, S. Jabbar, Sh. Khalid, M. A. Latif, F. Al-turjman, L. Mostarda , “Cyber security threats detection in internet of things using deep learning approach,” IEEE access, vol. 7, pp. 124379–124389, 2019. doi: 10.1109/ACCESS.2019.2937347.
[93] L. Peng, J. Zhang, M. Liu, and A. Hu, “Deep learning based RF fingerprint identification using differential constellation trace figure,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1091–1095, 2019. doi: 10.1109/TVT.2019.2950670.
[94] K. Sankhe, M. Belgiovine, F. Zhou, L. Angioloni, F. Restuccia, S. D’Oro, T. Melodia, S. Ioannidis, K. Chowdhury, “No radio left behind: Radio fingerprinting through deep learning of physical-layer hardware impairments,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 165–178, 2019. doi: 10.1109/TCCN.2019.2949308.
[95] R.-F. Liao et al., “Security enhancement for mobile edge computing through physical layer authentication,” IEEE Access, vol. 7, pp. 116390–116401, 2019. doi: 10.1109/ACCESS.2019.2934122.
[96] A. Ferdowsi and W. Saad, “Deep learning for signal authentication and security in massive internet-of-things systems,” IEEE Transactions on Communications, vol. 67, no. 2, pp. 1371–1387, 2018. doi: 10.1109/TCOMM.2018.2878025.
[97] B. Chatterjee, D. Das, S. Maity, and S. Sen, “RF-PUF: Enhancing IoT security through authentication of wireless nodes using in-situ machine learning,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 388–398, 2018. doi: 10.1109/JIOT.2018.2849324.
[98] K. Youssef, L. Bouchard, K. Haigh, J. Silovsky, B. Thapa, and C. Vander Valk, “Machine learning approach to RF transmitter identification,” IEEE Journal of Radio Frequency Identification, vol. 2, no. 4, pp. 197–205, 2018. doi: 10.1109/JRFID.2018.2880457.
[99] J. Chauhan, S. Seneviratne, Y. Hu, A. Misra, A. Seneviratne, and Y. Lee, “Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks,” Computer (Long Beach Calif), vol. 51, no. 5, pp. 60–67, 2018. doi: 10.1109/MC.2018.2381119.
[100] S. H. Oh, G.-W. Kim, and K.-S. Lim, “Compact deep learned feature-based face recognition for Visual Internet of Things,” The Journal of Supercomputing, vol. 74, no. 12, pp. 6729–6741, 2018. doi: 10.1007/s11227-017-2198-0.
[101] Y. Zhang et al., “Cyber physical security analytics for transactive energy systems,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 931–941, 2019. doi: 10.1109/TSG.2019.2928168.
[102] F. Li, Y. Shi, A. Shinde, J. Ye, and W. Song, “Enhanced cyber-physical security in internet of things through energy auditing,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5224–5231, 2019. doi: 10.1109/JIOT.2019.2899492.
[103] W. Jung, H. Zhao, M. Sun, and G. Zhou, “IoT botnet detection via power consumption modeling,” Smart Health, vol. 15, p. 100103, 2020. doi: 10.1016/j.smhl.2019.100103.
[104] F. Li, A. Shinde, Y. Shi, J. Ye, X.-Y. Li, and W. Song, “System statistics learning-based IoT security: Feasibility and suitability,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6396–6403, 2019. doi: 10.1109/JIOT.2019.2897063.
[105] M. E. Aminanto, R. Choi, H. C. Tanuwidjaja, P. D. Yoo, and K. Kim, “Deep abstraction and weighted feature selection for Wi-Fi impersonation detection,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 3, pp. 621–636, 2017. doi: 10.1109/TIFS.2017.2762828.
[106] X. Guo, H. Lin, Z. Li, and M. Peng, “Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6242–6251, 2019. doi: 10.1109/JIOT.2019.2960033.
[107] R.-H. Hwang, M.-C. Peng, C.-W. Huang, P.-C. Lin, and V.-L. Nguyen, “An unsupervised deep learning model for early network traffic anomaly detection,” IEEE Access, vol. 8, pp. 30387–30399, 2020. doi: 10.1109/ACCESS.2020.2973023.
[108] M. AL-Hawawreh, N. Moustafa, and E. Sitnikova, “Identification of malicious activities in industrial internet of things based on deep learning models,” Journal of information security and applications, vol. 41, pp. 1–11, 2018. doi: 10.1016/j.jisa.2018.05.002.
[109] P. K. Sharma, S. Singh, and J. H. Park, “OpCloudSec: Open cloud software defined wireless network security for the Internet of Things,” Computer Communications, vol. 122, pp. 1–8, 2018. doi: 10.1016/j.comcom.2018.03.008.
[110] R. M. A. Ujjan, Z. Pervez, K. Dahal, A. K. Bashir, R. Mumtaz, and J. González, “Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN,” Future Generation Computer Systems, vol. 111, pp. 763–779, 2020. doi: 10.1016/j.future.2019.10.015.
[111] Q. Tian, J. Li, and H. Liu, “A method for guaranteeing wireless communication based on a combination of deep and shallow learning,” IEEE Access, vol. 7, pp. 38688–38695, 2019. doi: 10.1109/ACCESS.2019.2905754.
[112] Y. Zhang, P. Li, and X. Wang, “Intrusion detection for IoT based on improved genetic algorithm and deep belief network,” IEEE Access, vol. 7, pp. 31711–31722, 2019. doi: 10.1109/ACCESS.2019.2903723.
[113] A. Telikani and A. H. Gandomi, “Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things,” Internet of Things, vol. 14, p. 100122, 2021. doi: 10.1016/j.iot.2019.100122.
[114] N. Balakrishnan, A. Rajendran, D. Pelusi, and V. Ponnusamy, “Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things,” Internet of things, vol. 14, p. 100112, 2021. doi: 10.1016/j.iot.2019.100112.
[115] A. S. Almogren, “Intrusion detection in Edge-of-Things computing,” Journal of Parallel and Distributed Computing, vol. 137, pp. 259–265, 2020. doi: 10.1016/j.jpdc.2019.12.008.
[116] Y. Li et al., “Robust detection for network intrusion of industrial IoT based on multi-CNN fusion,” Measurement, vol. 154, p. 107450, 2020. doi: 10.1016/j.measurement.2019.107450.
[117] D. Li, L. Deng, M. Lee, and H. Wang, “IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning,” International Journal of Information Management, vol. 49, pp. 533–545, 2019. doi: 10.1016/j.ijinfomgt.2019.04.006.
[118] J. Li and B. Sun, “A network attack detection method using SDA and deep neural network based on internet of things,” International Journal of Wireless Information Networks, vol. 27, no. 2, pp. 209–214, 2020. doi: 10.1007/s10776-019-00462-7.
[119] S. Manimurugan, S. Al-Mutairi, M. M. Aborokbah, N. Chilamkurti, S. Ganesan, and R. Patan, “Effective attack detection in internet of medical things smart environment using a deep belief neural network,” IEEE Access, vol. 8, pp. 77396–77404, 2020. doi: 10.1109/ACCESS.2020.2986013.
[120] X. Wang and X. Zhang, “Wireless network attack defense algorithm using deep neural network in internet of things environment,” International Journal of Wireless Information Networks, vol. 26, pp. 143–151, 2019. doi: 10.1007/s10776-019-00430-1.
[121] A. Abeshu and N. Chilamkurti, “Deep learning: The frontier for distributed attack detection in fog-to-things computing,” IEEE Communications Magazine, vol. 56, no. 2, pp. 169–175, 2018. doi: 10.1109/MCOM.2018.1700332.
[122] A. Diro and N. Chilamkurti, “Leveraging LSTM networks for attack detection in fog-to-things communications,” IEEE Communications Magazine, vol. 56, no. 9, pp. 124–130, 2018. doi: 10.1109/MCOM.2018.1701270
[123] A. A. Diro and N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Future Generation Computer Systems, vol. 82, pp. 761–768, 2018. doi:10.1016/j.future.2017.08.043.
[124] R. Kozik, M. Choraś, M. Ficco, and F. Palmieri, “A scalable distributed machine learning approach for attack detection in edge computing environments,” Journal of Parallel and Distributed Computing, vol. 119, pp. 18–26, 2018. doi: 10.1016/j.jpdc.2018.03.006.
[125] V. M. Krundyshev, “Identifying cyberthreats in modern industrial systems by means of deep-learning networks,” Automatic Control and Computer Sciences, vol. 53, no. 8, pp. 1006–1011, 2019. doi: 10.3103/S014641161908011X.
[126] C. U. Om Kumar and P. R. K. Sathia Bhama, “Detecting and confronting flash attacks from IoT botnets,” The Journal of Supercomputing, vol. 75, pp. 8312–8338, 2019. doi: 10.1007/s11227-019-03005-2.
[127] G. D. L. T. Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting Internet of Things attacks using distributed deep learning,” Journal of Network and Computer Applications, vol. 163, p. 102662, 2020. doi: 10.1016/j.jnca.2020.102662.
[128] M. S. Pour et al., “On data-driven curation, learning, and analysis for inferring evolving internet-of-Things (IoT) botnets in the wild,” Computers & Security, vol. 91, p. 101707, 2020. doi: 10.1016/j.cose.2019.101707
[129] N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,” Future Generation Computer Systems, vol. 100, pp. 779–796, 2019. doi: 10.48550/arXiv.1811.00701.
[130] A. Samy, H. Yu, and H. Zhang, “Fog-based attack detection framework for internet of things using deep learning,” Ieee Access, vol. 8, pp. 74571–74585, 2020. doi: 10.1109/ACCESS.2020.2988854.
[131] Z. Tian, C. Luo, J. Qiu, X. Du, and M. Guizani, “A distributed deep learning system for web attack detection on edge devices,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1963–1971, 2019. doi: 10.1109/TII.2019.2938778.
[132] M. A. Ferrag and L. Maglaras, “DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids,” IEEE Transactions on Engineering Management, vol. 67, no. 4, pp. 1285–1297, 2019. doi: 10.1109/TEM.2019.2922936.
[133] S. Wang and Z. Qiao, “Robust pervasive detection for adversarial samples of artificial intelligence in IoT environments,” IEEE Access, vol. 7, pp. 88693–88704, 2019. doi: 10.1109/ACCESS.2019.2919695.
[134] S. Kim, W. Jo, and T. Shon, “APAD: Autoencoder-based payload anomaly detection for industrial IoE,” Applied Soft Computing, vol. 88, p. 106017, 2020. doi: 10.1016/j.asoc.2019.106017.
[135] M. Li, Y. Sun, H. Lu, S. Maharjan, and Z. Tian, “Deep reinforcement learning for partially observable data poisoning attack in crowdsensing systems,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6266–6278, 2019. doi: 10.1109/JIOT.2019.2962914.
[136] Y. Xiao, Y. Jia, X. Cheng, J. Yu, Z. Liang, and Z. Tian, “I can see your brain: Investigating home-use electroencephalography system security,” IEEE Internet Things J, vol. 6, no. 4, pp. 6681–6691, 2019. doi: 10.1109/JIOT.2019.2910115.
[137] J. Bergstra, D. Yamins, and D. Cox, “Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,” in International conference on machine learning, PMLR, 2013, pp. 115–123.