Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms
Tóm tắt
As Internet access widens, IDS (Intrusion Detection System) is becoming a very important component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles massive amounts of data traffc that contain redundant and irrelevant features, which impact the performance of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques, and support vector machine models are employed in some such methods. Good feature selection leads to better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.
Tài liệu tham khảo
Heasman, John, S. Movle. Intrusion detection system [M]. U.S. Patent Application No. 10/511, 775, 2003.
D. Anderson, T. Frivold, A. Valdes. Next-generation intrusion detection expert system(nides): a summary [C]//SRI International Computer Science Laboratory Menio Park, CA, 1995.
R. Martin. Snort: lightweight intrusion detection for networks [J]. LISA, 1999, 99 (1): 229–238.
H. Debar, M. Dacier, A. Wespi. Towards taxonomy of intrusion detection systems [J]. Computer networks 1999, 31(9), 805–822.
H. Debar, M. Dacier, A. Wespi. A revised taxonomy for intrusion-detection system [J]. Annals of telecommuni cations, 2000, 55: 361–378.
P. Garca-Teodoroa, J. Daz-Verdejoa, G. Maci-Fernndez, et al. Anomaly-based network intrusion detection: techniques, systems and challenges[J]. Computers and security, 2009, 28(1): 18–28.
T. F. Lunt. A survey of intrusion detection techniques [J]. Computers and security, 1993, 12(4): 405–418.
Y. Hamid, M. Sugumaran, V. R. Balasaraswathi. IDS using machine learning-current state of art and future directions [J]. British journal of applied science and technology, 2016, 15(3).
S. Chebrolu, A. Abraham, J. P. Thomas. Feature deduction and ensemble design of intrusion detection systems [J]. Computers and security, 2005, 24(4): 295–307.
D. Manoranjan, H. Liu. Feature selection for classification [J]. Intelligent data analysis, 1997, 1(3): 131–156.
G. H. John, R. Kohavi, K. Peger. Irrelevant features and the subset selection problem [C]//Proceedings of ML-94, 1994: 121–129.
J. Frank. Artificial intelligence and intrusion detection: Current and future directions [C]//Proceedings of the 17th National Computer Security Conference, 1994, 10: 1–12.
W. Lee, S. J. Stolfo. A framework for constructing features and models for intrusion detection systems [C]//ACM Transactions on Information and System Security, 2000, 3(4): 227–261.
K. Kira, L. A. Rendell. The feature selection problem: traditional methods and a new algorithm [M]. AAAI-92.2, MIT Press, 1992: 129–134.
B. Kumar, T. Swarnkar. Filter versus wrapper feature subset selection in large dimensionality micro array: a review [J]. International journal of computer science and information technologies, 2011, 2(3): 1048–1053.
V. Bolón-Canedo, N. Sánchez-Maroño, A. Alonso-Betanzos. A review of feature selection methods on synthetic data [J]. Knowledge and information system, 2013, 34(3): 483–519.
S Maldonado, R Weber, F Famili, et al. Feature selection for high-dimensional class-imbalanced data sets using support vector machines [J]. Information sciences, 2014, 286: 228–246.
S. Binitha, S. S. Sathya. A survey of bio inspired optimization algorithms [J]. International journal of soft computing and engineering, 2012, 2(2): 137–151.
D. Karaboga, B. Akay. A comparative study of artificial bee colony algorithm [J]. Applied mathematics and computation, 2009, 214(1): 108–132.
T. Back. Evolutionary algorithms in theory and practice [M]. Oxford: Oxford University Press, 1996.
J. R. Koza. Genetic programming: on the programming of computers by means of natural selection (Vol. 1) [M]. Cambridge: MIT press, 1992.
J. H. Holland. Genetic algorithms and the optimal allocation of trials [J]. SIAM journal on computing, 1973, 2(2): 88–105.
H. G. Beyer, H. P. Sshwefel. Evolution strategies [J]. Natural computing, 2002: 3–52.
E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm intelligence [M]. Oxford: Oxford University Press, 1999.
M. Dorigo, C. Blum. Ant colony optimization theory: a survey [J]. Theoretical computer science, 2005, 344(2): 243–278.
M. Dorigo, V. Maniezzo, A. Colorn. Ant system: optimization by a colony of cooperating agents [C]//IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1): 29–41.
R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimization [J]. Swarm intelligence, 2007, 1(1): 33–57.
D. Karaboga, B. Basturk. A powerful and effcient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm [J]. Journal of global optimization, 2007, 39(3): 459–471.
X. L. Li, Z. J. Shao, J. X. Qian. An optimizing method based on autonomous animats: fish-swarm algorithm [J]. System engineering theory and practice, 2002, 22(11): 32–38.
X. S. Yang. Fire fly algorithm for multimodal optimization [D]. Cambridge: International Symposium on Stochastic Algorithms, 2009: 169–178.
S. Goings, H. Goldsby, B. H. C. Cheng, et al. An ecology-based evolutionary algorithm to evolve solutions to complex problems [J]. Artificial life, 2012, 13: 171–177.
G. Stein, B. Chen, A. S. Wu, et al. Decision tree classifier for network intrusion detection with GA-based feature selection [C]//Proceedings of the 43rd Annual Southeast Regional Conference. 2005, 2: 136–141.
C. H. Tsang. Network-based anomaly intrusion detection using ant colony clustering model and genetic-fuzzy rule mining approach [D]. Hong Kong: City University of Hong Kong, 2006.
I. Ahmad, A. B. Abdulla, A. S. Alghamdi, et al. Intrusion detection using feature subset selection based on MLP [J]. Scientific research and essays, 2011, 24(7-8): 1671–1682.
I. Ahmad, A. B. Abdulah, A. S. Alghamdi, et al. Feature subset selection for network intrusion detection mechanism [C]//Proceedings of CSIT, 2011, 5.
S. S. S. Sindhu, S. Geetha, A. Kannan, et al. Decision tree based light weight intrusion detection using a wrapper approach [J]. Expert systems with applications, 2012, 39(1): 129–141.
I. Ahmad, M. Hussain, A. Alghamdi, et al. Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components [J]. Neural computing and applications, 2014, 6(34): 6804–6810.
F. J. Kuang, W. H. Xu, S. Zhang. A novel hybrid KPCA and SVM with GA model for intrusion detection [J]. Ap plied soft computing, 2014, 18, 178–184.
B. M. Aslahi-Shahri, R. Rahmani, M. Chizari, et al. A hybrid method consisting of GA and SVM for intrusion detection system [J]. Neural computing and applications, 2015, 1–8.
J. Wang, X. Hong, R. R. Ren, et al. A realtime intrusion detection system based on PSO-SVM [C]//Proceedings of the International Workshop on Information Security and Application, 2009: 319–321.
T. Jiang, H. Gu. Anomaly detection combining oneclass SVMs and particle swarm optimization algorithms [J]. Nonlinear dynamics, 2010, 61(1-2): 303–310.
A. Osama, Z. A. Othman. Bees algorithm for feature selection in network anomaly detection [J]. Journal of applied sciences research, 2012, 8(3): 1748–1756.
C. Chung, Y. Ying, N. Wahid. A hybrid network intrusion detection system using simplified swarm optimization (SSO) [J]. Applied soft computing, 2012, 12(9): 3014–3022.
K. I. Rufai, R. C. Muniyandi, Z. A. Othman. Improving bee algorithm based feature selection in intrusion detection system using membrane computing [J]. Journal of networks, 2014, 9(3): 523–529.
X. Z. Wang. ACO and SVM selection feature weighting of network [J]. International Journal of Security and Its applications, 2015, 9(4): 129–270.
M. S. Rani, S. B. Xavier. A hybrid intrusion detection system based on C5.0 decision tree and one-Class SVM [J]. International journal of current engineering and technology, 2015, 5(3): 2001–2007.
A. S. Eesa, Z. Orman, A. M. A. Brifcani. A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems [J]. Expert systems with applications, 2015, 42(5): 2670–2679.
T. Mehmod, H. B. M. Rais. Ant colony optimization and feature selection for intrusion detection [J]. Advances in machine learning and signal processing, 2016, 305–312.
M. H. Aghdam, P. Kabiri. Feature selection for intrusion detection system using ant colony optimisation [J]. International journal of network security, 2016, 18(3): 420–432.
C. Y. Cheng, L. Y. Bao, C. H. Bao. Network intrusion detection with bat algorithm for synchronization of feature selection and support vector machines [C]//International Symposium on Neural Networks. Springer International Publishing Switzerland, 2016: 401–408.
W. A. H. M. Ghanem, A. Jantan. Novel multiobjective artificial bee colony optimization for wrapper based feature selection in intrusion detection [J]. International journal of advance soft computing applications, 2016, 8(1).
X. Xu, X. N. Wang. An adaptive network intrusion detection method based on pca and support vector machines [C]//ADMA, 2005: 696–703.
T. S. Chou, K. K. Yen, J. Luo. Network intrusion detection design using feature selection of soft computing paradigms [J]. International journal of computational intelligence, 2008, 4(3): 196–208.
Z. Y. Tan, A. Jamdagni, X. He. et al. Network intrusion detection based on LDA for payload feature selection [C]//2010 IEEE Globecom Workshops, 2010: 1545–1549.
Y. H. Li, J. B. Xia, S. L. Zhang, et al. An effcient intrusion detection system based on support vector machines and gradually feature removal method [J]. Expert systems with applications, 2012, 39(1): 424–430.
A. Shrivastava, M. Baghel, H. Gupta. A novel hybrid feature selection and intrusion detection based on pcnn and support vector machine [J]. International journal of computer technology and applications, 2013, 4 (6), 922.
P. Louvieris, N. Clewley, X. Liu. Effects-based feature identification for network intrusion detection [J]. Neurocomputing, 2013, 121, 265–273.
S. Balakrishnan, K. Venkatalakshmi, A. Kannan. A intrusion detection system using feature selection and classification technique [J]. International journal of computer science and application (IJCSA), 2016, 3(4).
M. Ambusaidi, X. J. He, P. Nanda, et al. Building an intrusion detection system using a filter-based feature selection algorithm [C]//IEEE transactions on computers, 2014, 65(10): 2986–2998.
A. R. Vasudevan, S. Selvakumar. Intraclass and interclass correlation coeffcient-based feature selection in NIDS dataset [J]. Security and communication networks, 2015, 8(18): 3441–3458.
C. Y. Yin, L. Y. Ma, L. Feng, et al. A feature selection algorithm towards effcient intrusion [J]. International journal of multimedia and ubiquitous engineering, 2015, 10(11): 253–264.
H. S. Chae, B. O. Jo, S. H. Choi, et al. Feature selection for intrusion detection using NSL-KDD [J]. Recent advances in computer science, 2015, 960–978.
V. Rampure, A. Tiwari. A rough set based feature selection on KDD CUP 99 data set [J]. International journal of database theory and application, 2015, 8(1): 149–156.
H. Bostani, M. Sheikhan. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems [J]. Soft computing, 2015, 1–18.
S. Ramakrishnan, S. Devaraju. Attack's feature selection-based network intrusion detection using fuzzy control language [J]. International journal of fuzzy systems, 2016, 1–13.
A. I. Madbouly, T. M. Barakat. Enhanced relevant feature selection model for intrusion detection systems [J]. International journal intelligent engineering informatics, 2016, 4(1): 21–45.
S. Ganapathy, P. Vijayakumar, et al. An intelligent CRF based feature selection for effective intrusion detection [J]. The international arab journal of information technology, 2016, 16(2).
S. Rastegari, P. Hingston, C. P. Lam. Evolving statistical rulesets for network intrusion detection [J]. Applied soft computing, 2015, 33: 348–359.
M. H. Bhuyan, D. K. Bhattacharyya, J. K. Kalita. Network anomaly detection: methods, systems and tools [J]. IEEE communications surveys and tutorials, 2014, 16(1): 303–336.
C. H. Tsang, S. Kwong. Multiagent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction [C]//IEEE International Conference on Industrial Technology, 2005: 51–56.