An Effective and Lightweight Intrusion Detection for IoT based on Fog and Cloud using KNN Classification
Subject Areas : Intelligent Multimedia Processing and Communication Systems (IMPCS)
ali kaffash
1
(Ph.D Student, Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran)
Seyed Reza Kamel Tabbakh Farizani
2
(Associate Professor, Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran)
Maryam Kheirabadi
3
(Assistant Professor, Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran)
Keywords: Intrusion Detection System, Fog, Cloud, KNN, MLP,
Abstract :
Introduction: In today's ever-evolving landscape of technology, the Internet of Things (IoT) has emerged as a transformative force, interconnecting countless smart devices that permeate our daily lives. From smart homes and cities to industrial automation and healthcare, IoT has brought about unprecedented convenience and efficiency. However, this rapid proliferation of IoT devices has also given rise to significant security challenges. The IoT ecosystem encompasses a diverse array of devices, ranging from wearable fitness trackers to critical infrastructure components, all of which are susceptible to cyber threats. Unauthorized access, data breaches, and malicious attacks on IoT networks pose severe risks to data privacy, infrastructure stability, and public safety. As a result, the need for robust security measures, such as IoT Intrusion Detection Systems, has become increasingly evident. The importance of these systems cannot be overstated, as they serve as the first line of defense against a myriad of IoT-related threats. By identifying and responding to potential security breaches, IoT Intrusion Detection Systems help maintain the integrity of data, ensure the functionality of IoT devices, and preserve the trustworthiness of the entire IoT network. The limitation of resources in electronic devices of the Internet of Things has caused less attention to the security. Today, Intrusion detection systems (IDSs) are one of the most important solutions to identify all types of attacks and threats and adopt appropriate solutions to deal with them. In addition, due to the openness of the environment, the placement of devices based on the Internet of Things makes this environment more vulnerable. For this reason, providing an effective and efficient intrusion detection system can be a suitable solution for this environment. Method: In this article, we have presented a two-layer intrusion detection system based on KNN classification to separate malicious traffic from normal mode and multilayer perceptron artificial network to detect the type of attack. The data set used is KDD-CUP 99 data set.
Results: The experiment results show 99.743% accuracy for the data set as well as the improvement of Accuracy, Recall, Precision, F-measure, TPR and FPR parameters. In addition, the delay time of the proposed method is improved 40% compared to the MLP-MLP method and has a 139% lower delay than the fog-free state. Discussion: The present study aims to propose two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-nearest neighbor (KNN) algorithms and multi-layer perceptron (MLP) neural network applied in the fog and cloud to separate the attacks from normal traffic, respectively. we evaluated our proposed method using KDD-CUP 99 dataset. The results prove the improvement in accuracy, compared to the previous methods.
Method: In this article, we have presented a two-layer intrusion detection system based on KNN classification to separate malicious traffic from normal mode and multilayer perceptron artificial network to detect the type of attack. The data set used is KDD-CUP 99 data set. Results: The experiment results show 99.743% accuracy for the data set as well as the improvement of Accuracy, Recall, Precision, F-measure, TPR and FPR parameters. In addition, the delay time of the proposed method is improved 40% compared to the MLP-MLP method and has a 139% lower delay than the fog-free state.
Discussion: The present study aims to propose two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-nearest neighbor (KNN) algorithms and multi-layer perceptron (MLP) neural network applied in the fog and cloud to separate the attacks from normal traffic, respectively. we evaluated our proposed method using KDD-CUP 99 dataset. The results prove the improvement in accuracy, compared to the previous methods.
[1] M. T. S. MohammadJavad Zand, "Improvement of IOT Security in ZigBee Network Using AES256 Algorithm," Intelligent Multimedia Processing and Communication Systems(IMPCS), no. 2, p. 53, 2020.
[2] W. H. Hassan, "Current research on Internet of Things (IoT) security: A survey.," Computer Networks, vol. 148, pp. 283-294, 2019.
[3] A. G. Rozbeh Hosseinnezhad, "Intrusion Detection System in The Cloud Computing Using Heterogeneity Detection Technique," Intelligent Multimedia Processing and Communication Systems (IMPCS), no. 1, p. 39, 2021.
[4] R. B. M. ,. L. C. M. W. ,. G. A. G. Cristiano Antonio de Souza Carlos Becker Westphall, "Intrusion detection and prevention in fog based IoT environments: A Systematic Literature Review," Computer Networks, 2022.
[5] G. K. A. S. J. V. C. Kolias, "DDoS in the IoT: Mirai and Other Botnets," Computer, vol. 50, no. 7, pp. 80-84, 2017.
[6] S. Y. H. Tanaka, "On modeling and simulation of the behavior of," in 2017 IEEE International Symposium on, 2017.
[7] V. K. P. M. A.C. Panchal, "Security issues in IIoT: a comprehen- sive survey of attacks on IIoT and its countermeasures," in 2018 IEEE Global Con- ference on Wireless Computing and Networking (GCWCN), 2018.
[8] C. B. W. ,. R. B. M. B. M. S. ,. G. d. S. V. Cristiano Antonio de Souza, "Hybrid approach to intrusion detection in fog-based IoT environments," Computer Networks, vol. 180, 2020.
[9] R. M. C. K. S. d. A. B.B. Zarpelão, "A survey of intrusion detection in Internet of Things," Journal of Network and Computer Applications, vol. 84, pp. 25-37, 2017.
[10] L. G. P. M. Q. A. X. L. M. T. D. K. H. S. B. A. K. Victor Chang, "A Survey on Intrusion Detection Systems for Fog and Cloud Computing," future internet, vol. 14, no. 89, 2022.
[11] F. V. A. P. P. J. H. T. &. T. H. Hosseinpour, "An intrusion detection system for fog computing and IoT based logistic systems using a smart data approach," International Journal of Digital Content Technology and its Applications,, p. 10, 2016.
[12] "http:// kdd. ics. uci. edu/ datab ases/ kddcu p99/ kddcu p99. html," [Online].
[13] "https:// www. unb. ca/ cic/ datas ets/ ids. html," [Online].
[14] A. A. K. N. G. Yasmine Labiod, "Fog Computing Based Intrusion Detection Architecture to Protect IoT Networks," Wireless Personal Communications, vol. 125, pp. 231-259, 2022.
[15] K. P. S. P. P. R. Vinayakumar, "Evaluating effectiveness of shallow and deep networks to intrusion detection system," in International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.
[16] X. Z. X. L. X. L. F. &. Y. L. An, "Sample selected extreme learning machine based intrusion detection in fog computing and MEC," Wireless Communications and Mobile Computing, 2018.
[17] "Distributed attack detection scheme using deep learning approach for internet of things," Future Generation Computer Systems, vol. 82, pp. 761-768, 2018.
[18] K. ,. S. S. S.Prabavathy, "Design of Cognitive Fog Computing for Intrusion Detection in Internet of Things," Journal of Communications and Networks, pp. 291-298, 2018.
[19] L. Z. S. W. C. H. T. L. Victor C. M. Leung, "Intrusion detection system based on decision tree over big data in fog environment," Big IoT Data Analytics in Fog Computing, 2018.
[20] F.-B. Mocnik, "An improved algorithm for dynamic nearest-neighbour models," Journal of Spatial Science, vol. 67, no. 3, pp. 411-438, 2022.
[21] J. L. ,. B. Souradip Roy, "A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks," Internet of Things, vol. 19, 2022.
