A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-10 Number-8
Year of Publication : 2014
Authors : Omar S. Soliman , Aliaa Rassem
  10.14445/22315381/IJETT-V10P271

MLA 

Omar S. Soliman , Aliaa Rassem. "A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm", International Journal of Engineering Trends and Technology (IJETT), V10(8),370-379 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Network intrusion detection systems (NIDSs) have a role of identifying malicious activities by monitoring the behavior of networks. Due to the currently high volume of networks trafic in addition to the increased number of attacks and their dynamic properties, NIDSs have the challenge of improving their classification performance. Bio-Inspired Optimization Algorithms (BIOs) are used to automatically extract the the discrimination rules of normal or abnormal behavior to improve the classification accuracy and the detection ability of NIDS. A quantum vaccined immune clonal algorithm with the estimation of distribution algorithm (QVICA-with EDA) is proposed in this paper to build a new NIDS. The proposed algorithm is used as classification algorithm of the new NIDS where it is trained and tested using the KDD data set. Also, the new NIDS is compared with another detection system based on particle swarm optimization (PSO). Results shows the ability of the proposed algorithm of achieving high intrusions classification accuracy where the highest obtained accuracy is 94.8 %.

References

[1] http://kdd.ics.uci.edu/databases/kddcup99.
[2] Osama Alomari and Z Othman. Bees algorithm for feature selection in network anomaly detection. Journal of Applied Sciences Research, 8(3):1748–1756, 2012.
[3] Ver´onica Bol´on-Canedo, Noelia S´anchez-Maro˜no, and Amparo Alonso-Betanzos. Feature selection and classification in multiple class datasets: An application to kdd cup 99 dataset. Expert Systems with Applications, 38(5):5947–5957, 2011.
[4] FENG Jian-li GONG Chang-qing. Study of an intrusion detection based on quantum neural networks technology [j]. Journal of Shenyang Institute of Aeronautical Engineering, 1:016, 2010.
[5] Yu Sheng Chen, Yu Sheng Qin, Yu Gui Xiang, Jing Xi Zhong, and Xu Long Jiao. Intrusion detection system based on immune algorithm and support vector machine in wireless sensor network. In Information and Automation, pages 372– 376. Springer, 2011.
[6] Yuk Ying Chung and Noorhaniza Wahid. A hybrid network intrusion detection system using simplified swarm optimization (sso). Applied Soft Computing, 2012.
[7] Tian Fang, Dongmei Fu, and Yunfeng Zhao. A hybrid artificial immune algorithm for feature selection of ovarian cancer data. In Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. International Workshop on, volume 1, pages 681–685. IEEE, 2008.
[8] Qinghua Zhang; Yuzhen Fu. Research of adaptive immune network intrusion detection model. International Journal of Systems, Control and Communications, 3(3):280–286, 2011.
[9] Maoguo Gong, Jian Zhang, Jingjing Ma, and Licheng Jiao. An efficient negative selection algorithm with further training for anomaly detection. Knowledge-Based Systems, 30:185–191, 2012.
[10] Xiaojuan He, Jianchao Zeng, Songdong Xue, and Lifang Wang. An new estimation of distribution algorithm based edge histogram model for flexible job-shop problem. In Computer Science for Environmental Engineering and EcoInformatics, pages 315–320. Springer, 2011.
[11] Zhang Hongmei, Gao Haihua, and Wang Xingyu. Quantum particle swarm optimization based network intrusion feature selection and detection. 2007.
[12] Mohammad Sazzadul Hoque, Md Mukit, Md Bikas, Abu Naser, et al. An implementation of intrusion detection system using genetic algorithm. arXiv preprint arXiv:1204.1336, 2012.
[13] Mohammad Sazzadul Hoque, Md Mukit, Md Bikas, Abu Naser, et al. An implementation of intrusion detection system using genetic algorithm. arXiv preprint arXiv:1204.1336, 2012.
[14] Pedro Larra˜naga, Ramon Etxeberria, Jos´e A Lozano, and Jos´e M Pe˜na. Combinatorial optimization by learning and simulation of bayesian networks. In Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, pages 343–352. Morgan Kaufmann Publishers Inc., 2000.
[15] Sang Min Lee, Dong Seong Kim, and Jong Sou Park. A survey and taxonomy of lightweight intrusion detection systems. Journal of Internet Services and Information Security, 2(1/2):119– 131, 2012.
[16] Feng Liu, Juan Liu, Jing Feng, and Huaibei Zhou. Estimation distribution of algorithm for fuzzy clustering gene expression data. In Advances in Natural Computation, pages 328–335. Springer, 2006.
[17] Li-li Liu and Yuan Liu. Mqpso based on wavelet neural network for network anomaly detection. In Wireless Communications, Networking and Mobile Computing, 2009. WiCom’09. 5th International Conference on, pages 1–5. IEEE, 2009.
[18] Yuan Liu. Qpso-optimized rbf neural network for network anomaly detection. Journal of Information & Computational Science, 8(9):1479–1485, 2011.
[19] Adetunmbi A Olusola, Adeola S Oladele, and Daramola O Abosede. Analysis of kdd99 intrusion detection dataset for selection of relevance features. In Proceedings of the World Congress on Engineering and Computer Science, volume 1, pages 20–22, 2010.
[20] Chung-Ming Ou and CR Ou. Immunity-inspired host-based intrusion detection systems. In Genetic and Evolutionary Computing (ICGEC), 2011 Fifth International Conference on, pages 283–286. IEEE, 2011.
[21] Leila Ranjbar and Siavash Khorsandi. A collaborative intrusion detection system against ddos attack in peer to peer network. In Software Engineering and Computer Systems, pages 353–367. Springer, 2011.
[22] Khushboo Satpute, Shikha Agrawal, Jitendra Agrawal, and Sanjeev Sharma. A survey on anomaly detection in network intrusion detection system using particle swarm optimization based machine learning techniques. In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), pages 441–452. Springer, 2013.
[23] Omar S Soliman and Aliaa Rassem. A bio inspired clonal algorithm with estimationof distribution algorithm for global optimization. In Informatics and Systems (INFOS), 2012 8th International Conference on, pages BIO–166. IEEE, 2012.
[24] KG Srinivasa. Application of genetic algorithms for detecting anomaly in network intrusion detection systems. In Advances in Computer Science and Information Technology. Networks and Communications, pages 582–591. Springer, 2012.
[25] Jianyong Sun, Qingfu Zhang, and Edward PK Tsang. De/eda: A new evolutionary algorithm for global optimization. Information Sciences, 169(3):249–262, 2005.
[26] S.Yang and L. Jiao. A quantum-vaccine-inspired immune clonal algorithm and memory-enhanced learning. Electrical Engineering, IEEE Transactions on, 2011.
[27] MM Wa’el, Hamdy N Agiza, and Elsayed Radwan. Intrusion detection using rough sets based parallel genetic algorithm hybrid model. In Proceedings of the World Congress on Engineering and Computer Science, volume 2, pages 20–22, 2009.
[28] Hui WANG, Xiaojun BI, Lijun YU, and Lijun ZHANG. An adjustable threshold immune negative selection algorithm based on vaccine theory [j]. Journal of Harbin Engineering University, 1:015, 2011.
[29] Rui Wu, Chang Su, Kewen Xia, and Yi Wu. An approach to wls-svm based on qpso algorithm in anomaly detection. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, pages 4468–4472. IEEE, 2008.
[30] Shelly Xiaonan Wu and Wolfgang Banzhaf. The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing, 10(1):1–35, 2010.
[31] Chun Yang, Haidong Yang, and Feiqi Deng. Quantum-inspired immune evolutionary algorithm based parameter optimization for mixtures of kernels and its application to supervised anomaly idss. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, pages 4568–4573. IEEE, 2008.
[32] Quan-min Zha, Rong-gui Wang, and Wei He. Intrusion detection algorithm based on quantum genetic clustering. Application Research of Computers, 1:073, 2010.
[33] Haiyi Zhang, Yang Yi, and Jiansheng Wu. Network intrusion detection system based on incremental support vector machine. In Contemporary Challenges and Solutions in Applied Artificial Intelligence, pages 91–96. Springer, 2013.
[34] Zong-Fei Zhang. Quantum evolutionary algorithm for optimizing network intrusion signature database. Jisuanji Yingyong/ Journal of Computer Applications, 30(8):2142–2145, 2010.

Keywords
Estimation of Distribution Algorithm (EDA), Network Intrusions Detection System (NIDS), Quantum Vaccined Immune Clonal Algorithm (QVICA).