improving intrusion detection systems by feature reducing based on genetics algorithm and data mining techniques

Subject Areas : Electronics Engineering

Mehdi Keshavarzi ¹ , hossein Momenzadeh ²

1 - دانشجو
2 - دانشگاه آزاد بوشهر

Received: 2020-08-31 Accepted : 2020-08-31 Published : 2019-07-23

Keywords:

Abstract :

The network-based computer systems play critical role in our modern society; so there is highly chance these systems might be target of intrusion and attacks. In order to implement full-scale security in a computer network, firewalls and other intrusion prevention mechanisms aren’t always enough and needs other systems called intrusion detection systems. An Intrusion detection system can be set of tools, algorithms and evidence that help to identify, locate and report illegal or not approved activities by the network. Intrusion detection systems can be established by software or hardware systems and each have their own advantages and disadvantages. Because of various characteristics of intrusion detection data, in this research we select effective characteristics using improved genetic algorithm. Then by means of standard data mining techniques, we present a model for data classification.For performance evaluation of this suggested method, we used NSL-KDD database that has more realistic records than other intrusion detection data.                                                                          

References:

[1]    Liao, Hung-Jen, Chun-Hung Richard Lin, Ying-Chih Lin, and Kuang-Yuan Tung. (2013), Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications 36, no. 1 : 16-24.

[2]    Pan, Shengyi, Thomas Morris, and Uttam Adhikari.(2015). Developing a hybrid intrusion detection system using data mining for power systems. IEEE Transactions on Smart Grid 6.6 : 3104-3113.

[3]    Zuech, Richard, Taghi M. Khoshgoftaar, and Randall Wald.(2015). Intrusion detection and big heterogeneous data: a survey. Journal of Big Data 2.1 : 1.

[4]    Denning, Dorothy E., and Peter G. Neumann. (1985), Requirements and model for IDES—a real-time intrusion detection expert system. Document A005, SRI International 333.

[5]   خدایار, محمد؛ علیرضا عصاره و منصور امینی لاری، ۱۳۹۳، بکارگیری الگوریتم های ترکیبی یادگیری ماشین در بهبود سیستمهای تشخیص نفوذ، همایش ملی مهندسی رایانه و مدیریت فناوری اطلاعات، تهران، شرکت علم و صنعت طلوع فرزین.

[6]    Goyal, Anup, and Chetan Kumar, (2008). GA-NIDS: a genetic algorithm based network intrusion detection system. Northwestern university.

[7]    Muda, Z., W. Yassin, M. N. Sulaiman, and N. I. Udzir, (2011). Intrusion detection based on K-Means clustering and Naïve Bayes classification. In Information Technology in Asia (CITA 11), 2011 7th International Conference on, pp. 1-6.

[8]    Sriparna Saha, Ashok Singh Sairam, Asif Ekbal,(2012). Genetic Algorithm Combined with Support Vector Machine for Building an Intrusion Detection System, International Conference on Advances in Computing, Communications and Informatics (ICACCI-2012)

[9]    Zhu, Shuxin, and Bin Hu, (2013). Hybrid feature selection based on improved GA for the intrusion detection system. Indonesian Journal of Electrical Engineering and Computer Science 11, no. 4 : 1725-1730.

[10] Benaicha, Salah Eddine, Lalia Saoudi, Salah Eddine Bouhouita Guermeche, and Ouarda Lounis, (2014). Intrusion detection system using genetic algorithm. In Science and Information Conference (SAI), 2014, pp. 564-568.

[11] Chae, Hee-su, Byung-oh Jo, Sang-Hyun Choi, and Twaekyung Park, (2015). Feature Selection for Intrusion Detection using NSL-KDD. Recent Advances in Computer Science, ISBN : 978-960.

[12] Aghdam, Mehdi Hosseinzadeh, and Peyman Kabiri, (2016). Feature selection for intrusion detection system using ant colony optimization. International Journal of Network Security 18.3 : 420-432.

[13] Park, Chan Hee, and Seoung Bum Kim. (2015), Sequential random k-nearest neighbor feature selection for high-dimensional data." Expert Systems with Applications 42.5 : 2336-2342.

[14]  

[15] Hidayati, R., Kanamori, K., Feng, L., & Ohwada, H. (2016). Combining Feature Selection with Decision Tree Criteria and Neural Network for Corporate Value Classification. In Pacific Rim Knowledge Acquisition Workshop (pp. 31-42). Springer International Publishing.

[16] Nsl-kdd dataset for network based  intrusion detection systems. Available on: http://nsl.cs.unb.ca/KDD/NSL-KDD.html, March 2009.

[17] Tavallaee M, Stakhanova N and Ghorbani AA., (2010). Towards credible evaluation of anomaly based intrusion detection methods , IEEE Transaction on System, Man and Cybernetics, Part-c, Applications and Reviews; 40(5):516-524.

[18] Warsi, Sana, Yogesh Rai, and Santosh Kushwaha, (2015). Selective Iteration based Particle Swarm Optimization (SIPSO) for Intrusion Detection System. International Journal of Computer Applications 124.17.

[19] Mubarak, Shaik Liyakhat, (2016). Intrusion Detection System using SVM, SOM & NN.