OPTIMIZATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW
DOI:
https://doi.org/10.29121/granthaalayah.v8.i8.2020.1031Keywords:
Intrusion Detection, Anomaly Detection, Misuse Detection, Optimization AlgorithmsAbstract [English]
With the growth and development of the Internet, the devices and the hosts connected to the Internet have become the target for attackers and intruders. Consequently, the integrity of systems and data has become more sophisticated. Meanwhile, many institutions suffer from money-losing or other losses due to attacks on computer systems. Accordingly, the detection of intrusion and attacks has become a challenge and a vital necessity at the same time. Many different methods were used to build intrusion detection systems (IDSs), and all these methods seek to a plus the efficiency of intrusion detection systems. This paper is a survey which tries to covers some of the optimization algorithms used in the field of intrusion detection in past ten years such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Cuttlefish Algorithms (CFA), and Particle Swarm Optimization (PSO). It is hoped that this review will provide useful insights about the intrusion detection literature and is a good source for anyone interested in applying one of the used optimization algorithms in the field of intrusion detection.
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References
Ashoor, A.S., S.J.I.J.o.S. Gore, and E. Research, Importance of intrusion detection system (IDS). 2011. 2(1): p. 1-4.
Mohammadi, S., et al., Cyber intrusion detection by combined feature selection algorithm. 2019. 44: p. 80-88. DOI: https://doi.org/10.1016/j.jisa.2018.11.007
Khraisat, A., et al., Survey of intrusion detection systems: techniques, datasets and challenges. 2019. 2(1): p. 20. DOI: https://doi.org/10.1186/s42400-019-0038-7
Aljawarneh, S., M.B. Yassein, and M.J.C.C. Aljundi, An enhanced J48 classification algorithm for the anomaly intrusion detection systems. 2019. 22(5): p. 10549-10565. DOI: https://doi.org/10.1007/s10586-017-1109-8
Pradhan, M., C.K. Nayak, and S.K. Pradhan, Intrusion Detection System (IDS) and Their Types, in Securing the Internet of Things: Concepts, Methodologies, Tools, and Applications. 2020, IGI Global. p. 481-497. DOI: https://doi.org/10.4018/978-1-5225-9866-4.ch026
Parihar, L.S., A.J.I.J.f.S. Tiwari, and A.R.i. Technology, Survey on intrusion detection using data mining methods. 2016. 3(12): p. 342-7.
Tavallaee, M., et al. A detailed analysis of the KDD CUP 99 data set. in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. 2009. IEEE. DOI: https://doi.org/10.1109/CISDA.2009.5356528
Kaushik, S.S., P.J.I.J.o.C.S. Deshmukh, and I. Technologies, Detection of attacks in an intrusion detection system. 2011. 2(3): p. 982-986.
Dhanabal, L., S.J.I.J.o.A.R.i.C. Shantharajah, and C. Engineering, A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. 2015. 4(6): p. 446-452.
Alharbi, A., et al., Denial-of-Service, Probing, User to Root (U2R) & Remote to User (R2L) Attack Detection using Hidden Markov Models. 2018.
Hamamoto, A.H., et al., Network anomaly detection system using genetic algorithm and fuzzy logic. 2018. 92: p. 390-402. DOI: https://doi.org/10.1016/j.eswa.2017.09.013
Raman, M.G., et al., An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine. 2017. 134: p. 1-12. DOI: https://doi.org/10.1016/j.knosys.2017.07.005
Ali, G.A. and A. Jantan. A new approach based on honeybee to improve intrusion detection system using neural network and bees algorithm. in International Conference on Software Engineering and Computer Systems. 2011. Springer. DOI: https://doi.org/10.1007/978-3-642-22203-0_65
Aburomman, A.A. and M.B.I.J.A.S.C. Reaz, A novel SVM-kNN-PSO ensemble method for intrusion detection system. 2016. 38: p. 360-372. DOI: https://doi.org/10.1016/j.asoc.2015.10.011
Vardhini, K.K. and T. Sitamahalakshmi. Implementation of Intrusion Detection System Using Artificial Bee Colony with Correlation-Based Feature Selection. in Proceedings of the First International Conference on Computational Intelligence and Informatics. 2017. Springer.
Chung, Y.Y. and N.J.A.s.c. Wahid, A hybrid network intrusion detection system using simplified swarm optimization (SSO). 2012. 12(9): p. 3014-3022. DOI: https://doi.org/10.1016/j.asoc.2012.04.020
Zebari, D.A., et al., Image Steganography Based on Swarm Intelligence Algorithms: A Survey. 2020. 7(8): p. 9.
Sadeeq, H., et al. A Novel Hybrid Bird Mating Optimizer with Differential Evolution for Engineering Design Optimization Problems. in International Conference of Reliable Information and Communication Technology. 2017. Springer. DOI: https://doi.org/10.1007/978-3-319-59427-9_55
Eesa, A.S., et al., Cuttlefish algorithm-a novel bio-inspired optimization algorithm. 2013. 4(9): p. 1978-1986.
Eesa, A.S., Z. Orman, and A.M.A.J.E.S.w.A. Brifcani, A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. 2015. 42(5): p. 2670-2679. DOI: https://doi.org/10.1016/j.eswa.2014.11.009
Eesa, A.S., A.M. Abdulazeez, and Z.J.S.J.o.U.o.Z. Orman, A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree. 2017. 5(4): p. 313-318. DOI: https://doi.org/10.25271/2017.5.4.382
Shukran, M.A.M., et al., Artificial bee colony based data mining algorithms for classification tasks. 2011. 5(4): p. 217. DOI: https://doi.org/10.5539/mas.v5n4p217
Ahmed, J.A., A.M.A.J.I.J.o.M. Brifcani, Aerospace, Industrial, Mechatronic, and M. Engineering, A new internal architecture based on feature selection for holonic manufacturing system. 2015. 2(8): p. 1431.
Bae, C., et al., A novel anomaly-network intrusion detection system using ABC algorithms. 2012. 8(12): p. 8231-8248.
Varma, P.R.K., V.V. Kumari, and S.S.J.P.c.s. Kumar, Feature selection using relative fuzzy entropy and ant colony optimization applied to real-time intrusion detection system. 2016. 85: p. 503-510. DOI: https://doi.org/10.1016/j.procs.2016.05.203
Aghdam, M.H. and P.J.I.N.S. Kabiri, Feature Selection for Intrusion Detection System Using Ant Colony Optimization. 2016. 18(3): p. 420-432.
Li, Z., Y. Li, and L. Xu. Anomaly intrusion detection method based on k-means clustering algorithm with particle swarm optimization. in 2011 international conference of information technology, computer engineering and management sciences. 2011. IEEE. DOI: https://doi.org/10.1109/ICM.2011.184
Hoque, M.S., et al., An implementation of intrusion detection system using genetic algorithm. 2012.
Kumar, K.P.M.J.I.J.o.S., Engineering and C. Technology, Intrusion Detection system for malicious traffic by using PSO-GA algorithm. 2013. 3(6): p. 236.
Manekar, V. and K.J.I.J.o.A.C.R. Waghmare, Intrusion detection system using support vector machine (SVM) and particle swarm optimization (PSO). 2014. 4(3): p. 808.
Ahmad, I.J.I.J.o.D.S.N., Feature selection using particle swarm optimization in intrusion detection. 2015. 11(10): p. 806954. DOI: https://doi.org/10.1155/2015/806954
Mahmod, M.S., et al., Hybrid intrusion detection system using artificial bee colony algorithm and multi-layer perceptron. 2015. 13(2): p. 1.
Bamakan, S.M.H., et al., A new intrusion detection approach using PSO based multiple criteria linear programming. 2015. 55: p. 231-237. DOI: https://doi.org/10.1016/j.procs.2015.07.040
Ghanem, T.F., W.S. Elkilani, and H.M.J.J.o.a.r. Abdul-Kader, A hybrid approach for efficient anomaly detection using metaheuristic methods. 2015. 6(4): p. 609-619. DOI: https://doi.org/10.1016/j.jare.2014.02.009
Sharma, S., A. Gupta, and S. Agrawal. An intrusion detection system for detecting denial-of-service attack in cloud using artificial bee colony. in Proceedings of the International Congress on Information and Communication Technology. 2016. Springer.
Hajisalem, V. and S.J.C.N. Babaie, A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. 2018. 136: p. 37-50. DOI: https://doi.org/10.1016/j.comnet.2018.02.028
Ali, M.H., et al., A new intrusion detection system based on fast learning network and particle swarm optimization. 2018. 6: p. 20255-20261. DOI: https://doi.org/10.1109/ACCESS.2018.2820092
Yang, J., et al. Modified naive bayes algorithm for network intrusion detection based on artificial bee colony algorithm. in 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). 2018. IEEE. DOI: https://doi.org/10.1109/IDAACS-SWS.2018.8525758
Shokoohsaljooghi, A. and H.J.I.J.o.I.T. Mirvaziri, Performance improvement of intrusion detection system using neural networks and particle swarm optimization algorithms. 2019: p. 1-12. DOI: https://doi.org/10.1007/s41870-019-00315-9
Pradeep Mohan Kumar, K., et al., Intrusion detection system based on GA‐fuzzy classifier for detecting malicious attacks. 2019: p. e5242. DOI: https://doi.org/10.1002/cpe.5242
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