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Investigations on Bio-Inspired Algorithm for Network Intrusion Detection – A Review


Affiliations
1 School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India
 

A network is a collection of interconnected devices that can share information and resources, exchange files, and enable electronic communications. IDS is an important part of Network Security to secure a network. An Intrusion Detection System (IDS) is a fundamental building block in network security. A wide variety of techniques have been proposed and implemented to improve the performance and accuracy of intrusion detection models. It is used by many MNC companies such as Wipro, TCS, and L&T and they are having their IDS in the organization system. CNN (Convolutional Neural Network, Machine Learning, Data mining, Deep learning models such as SVM (Support Vector Machine) are performing well above the benchmark to prevent the systems from all kinds of attacks. Recently, bio-inspired optimization algorithms are metaheuristics that mimic the nature of solving optimization problems. Bio-inspired algorithms are gaining the moment that brings a revolution in computer science. This paper investigates the feature selection techniques of bio-inspired algorithms-driven Intrusion Detection Systems. This paper categorises these SI approaches based on their applicability in improving various aspects of an intrusion detection process. Furthermore, the paper discusses the capabilities and characteristics of various datasets used in experimentation. The main goal is to assist researchers in evaluating the capabilities and limitations of SI algorithms in identifying security threats and challenges in designing and implementing an IDS for the detection of cyber-attacks across multiple domains. The survey identifies existing issues and provides recommendations for how to effectively address them.

Keywords

IDS, Deep Learning, Optimization, Classification, Feature Selection, Bio-Inspired Algorithm.
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  • Y. K. Saheed and M. O. Arowolo, “Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms,” IEEE Access, vol. 9, pp. 161546–161554, 2021, doi: 10.1109/ACCESS.2021.3128837.

  • M. T. Ali A. Ghorbani, Wei Lu, “Network Intrusion Detection and Prevention Advances in Information Security,” Inf. Syst., p. 223, 2010.

  • B. A. Tama and S. Lim, “Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation,” Comput. Sci. Rev., vol. 39, p. 100357, 2021, doi: 10.1016/j.cosrev.2020.100357.

  • W. Peng, X. Kong, G. Peng, X. Li, and Z. Wang, “Network intrusion detection based on deep learning,” Proc. - 2019 Int. Conf. Commun. Inf. Syst. Comput. Eng. CISCE 2019, pp. 431–435, 2019, doi: 10.1109/CISCE.2019.00102.

  • M. S. Husain, “Nature Inspired Approach for Intrusion Detection Systems,” Des. Anal. Secur. Protoc. Commun., pp. 171–182, 2020, doi: 10.1002/9781119555759.ch8.

  • S. Roy, S. Biswas, and S. Sinha Chaudhuri, “Nature-Inspired Swarm Intelligence and Its Applications,” Int. J. Mod. Educ. Comput. Sci., vol. 6, no. 12, pp. 55–65, 2014, doi: 10.5815/ijmecs.2014.12.08.

  • E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition,” 2007 IEEE Congr. Evol. Comput. CEC 2007, pp. 4661–4667, 2007, doi: 10.1109/CEC.2007.4425083.

  • Z. Li and X. Huang, “Glowworm Swarm Optimization and Its Application to Blind Signal Separation,” Math. Probl. Eng., vol. 2016, 2016, doi: 10.1155/2016/5481602.

  • B. Selvakumar and K. Muneeswaran, “Firefly algorithm based feature selection for network intrusion detection,” Comput. Secur., vol. 81, pp. 148–155, 2019, doi: 10.1016/j.cose.2018.11.005.

  • O. Almomani, “SS symmetry Detection System Based on PSO , GWO , FFA and,” A Featur. Sel. Model Netw. Intrusion Detect. Syst. Based PSO, GWO, FFA GA Algorithms, vol. 33, no. 32, pp. 1–22, 2020.

  • Y. Luo, “Research on Network Security Intrusion Detection System Based on Machine Learning,” Int. J. Netw. Secur., vol. 23, no. 3, pp. 490–495, 2021, doi: 10.6633/IJNS.202105.

  • M. S. Abbasi, H. Al-Sahaf, M. Mansoori, and I. Welch, “Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection,” Appl. Soft Comput., vol. 121, p. 108744, 2022, doi: 10.1016/j.asoc.2022.108744.

  • A. Ayough, M. Zandieh, and H. Farsijani, “GA and ICA approaches to job rotation scheduling problem: Considering employee’s boredom,” Int. J. Adv. Manuf. Technol., vol. 60, no. 5–8, pp. 651–666, 2012, doi: 10.1007/s00170-011-3641-7.

  • P. Mukilan and W. Semunigus, “Human object detection: An enhanced black widow optimization algorithm with deep convolution neural network,” Neural Comput. Appl., vol. 33, no. 22, pp. 15831–15842, 2021, doi: 10.1007/s00521-021-06203-3.

  • R. Malik, Y. Singh, Z. A. Sheikh, P. Anand, P. K. Singh, and T. C. Workneh, “An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems,” J. Adv. Transp., vol. 2022, 2022, doi: 10.1155/2022/7892130.

  • A. S. Mahboob, H. S. Shahhoseini, M. R. Ostadi Moghaddam, and S. Yousefi, “A coronavirus herd immunity optimizer for intrusion detection system,” 2021 29th Iran. Conf. Electr. Eng. ICEE 2021, pp. 579–585, 2021, doi: 10.1109/ICEE52715.2021.9544165.

  • A. Thakkar and R. Lohiya, “Role of swarm and evolutionary algorithms for intrusion detection system: A survey,” Swarm Evol. Comput., vol. 53, no. December 2019, p. 100631, 2020, doi: 10.1016/j.swevo.2019.100631.

  • A. C. Enache and V. Sgârciu, “Enhanced intrusion detection system based on bat algorithm-support Vector Machine,” SECRYPT 2014 - Proc. 11th Int. Conf. Secur. Cryptogr. Part ICETE 2014 - 11th Int. Jt. Conf. E-bus. Telecommun., pp. 184–189, 2014, doi: 10.5220/0005015501840189.

  • X. S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” Int. J. Math. Model. Numer. Optim., vol. 1, no. 4, pp. 330–343, 2010, doi: 10.1504/IJMMNO.2010.035430.

  • A. S. Joshi, O. Kulkarni, G. M. Kakandikar, and V. M. Nandedkar, “Cuckoo Search Optimization- A Review,” Mater. Today Proc., vol. 4, no. 8, pp. 7262–7269, 2017, doi: 10.1016/j.matpr.2017.07.055.

  • W. Zhiheng and L. Jianhua, “Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm,” IEEE Access, vol. 9, pp. 88564–88582, 2021, doi: 10.1109/ACCESS.2021.3090512.

  • M. H. Nasir, S. A. Khan, M. M. Khan, and M. Fatima, “Swarm Intelligence inspired Intrusion Detection Systems — A systematic literature review,” Comput. Networks, vol. 205, no. January, p. 108708, 2022, doi: 10.1016/j.comnet.2021.108708.

  • A. Hosseinalipour and R. Ghanbarzadeh, “A novel approach for spam detection using horse herd optimization algorithm,” Neural Comput. Appl., vol. 0123456789, 2022, doi: 10.1007/s00521-022-07148-x.

  • S. Khosravi and A. Chalechale, “Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words,” Math. Probl. Eng., vol. 2022, no. Dl, 2022, doi: 10.1155/2022/4894922.

  • M. Amudha, R. Manickam, and R. Gayathri, “A Study on Climate Change with Mayfly Algorithm Optimization,” Recent trends Manag. Commer., vol. 2, no. 3, pp. 2–8, 2021, doi: 10.46632/rmc/2/3/5.

  • Y. J. Zheng, “Water wave optimization: A new nature-inspired metaheuristic,” Comput. Oper. Res., vol. 55, pp. 1–11, 2015, doi: 10.1016/j.cor.2014.10.008.

  • P. Kanchan, “Rainfall Analysis and Forecasting Using Deep Learning Technique,” J. Informatics Electr. Electron. Eng., vol. 2, no. 2, pp. 1–11, 2021, doi: 10.54060/jieee/002.02.015.

  • M. Alshinwan et al., “Dragonfly algorithm: a comprehensive survey of its results, variants, and applications,” Multimed. Tools Appl., no. February, 2021, doi: 10.1007/s11042-020-10255-3.

  • J. Pierezan et al., “Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization,” Technologies, vol. 9, no. 2, p. 35, 2021, doi: 10.3390/technologies9020035.

  • S. Idris, O. Oyefolahan Ishaq, and N. Ndunagu Juliana, “Intrusion Detection System Based on Support Vector Machine Optimised with Cat Swarm Optimization Algorithm,” 2019 2nd Int. Conf. IEEE Niger. Comput. Chapter, Niger. 2019, 2019, doi: 10.1109/NigeriaComputConf45974.2019.8949676.

  • C. Kiran Kumar and M. Govindarajan, “An efficient rapid intrusion detection method for detecting intrusions in networks,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 5991–5997, 2020.

  • Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001, doi: 10.1177/003754970107600201.

  • M. Sazzadul Hoque, “An Implementation of Intrusion Detection System Using Genetic Algorithm,” Int. J. Netw. Secur. Its Appl., vol. 4, no. 2, pp. 109–120, 2012, doi: 10.5121/ijnsa.2012.4208.

  • M. Romero Montoya et al., “Solution Search for the Capacitated P-Median Problem using Tabu Search,” Int. J. Comb. Optim. Probl. Informatics, vol. 10, no. 2, pp. 17–25, 2019, [Online]. Available: www.editada.org

  • K. Dnrxu and O. Dú, “Tabu- * HQHWLF $ OJRULWK P,” IEEE Int. Conf. Comput. Sci. Eng., pp. 215–220, 2017.

  • A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, 2019, doi: 10.1186/s42400-019-0038-7.

  • M. A. Siddiqi and W. Pak, “Optimizing filter-based feature selection method flow for intrusion detection system,” Electron., vol. 9, no. 12, pp. 1–18, 2020, doi: 10.3390/electronics9122114.

  • H. Alazzam, A. Sharieh, and K. E. Sabri, “A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer,” Expert Syst. Appl., vol. 148, 2020, doi: 10.1016/j.eswa.2020.113249.

  • T. S. Naseri and F. S. Gharehchopogh, “A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems,” J. Netw. Syst. Manag., vol. 30, no. 3, 2022, doi: 10.1007/s10922-022-09653-9.

  • C. Zhang, F. Ruan, L. Yin, X. Chen, L. Zhai, and F. Liu, “A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset,” Proc. Int. Conf. Anti-Counterfeiting, Secur. Identification, ASID, vol. 2019-Octob, pp. 41–45, 2019, doi: 10.1109/ICASID.2019.8925239.

  • E. Alhajjar, P. Maxwell, and N. Bastian, “Adversarial machine learning in Network Intrusion Detection Systems,” Expert Syst. Appl., vol. 186, no. May, p. 115782, 2021, doi: 10.1016/j.eswa.2021.115782.

  • A. E. Ibor, O. B. Okunoye, F. A. Oladeji, and K. A. Abdulsalam, “Novel Hybrid Model for Intrusion Prediction on Cyber Physical Systems’ Communication Networks based on Bio-inspired Deep Neural Network Structure,” J. Inf. Secur. Appl., vol. 65, no. January, p. 103107, 2022, doi: 10.1016/j.jisa.2021.103107.

  • M. D. Moizuddin and M. V. Jose, “A bio-inspired hybrid deep learning model for network intrusion detection,” Knowledge-Based Syst., vol. 238, p. 107894, 2022, doi: 10.1016/j.knosys.2021.107894.

  • M. Choraś and M. Pawlicki, “Intrusion detection approach based on optimised artificial neural network,” Neurocomputing, vol. 452, pp. 705–715, 2021, doi: 10.1016/j.neucom.2020.07.138.

  • M. Sarhan, S. Layeghy, N. Moustafa, M. Gallagher, and M. Portmann, “Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks,” 2021, [Online]. Available: http://arxiv.org/abs/2108.12722

  • E. ul H. Qazi, M. Imran, N. Haider, M. Shoaib, and I. Razzak, “An intelligent and efficient network intrusion detection system using deep learning,” Comput. Electr. Eng., vol. 99, no. February 2021, p. 107764, 2022, doi: 10.1016/j.compeleceng.2022.107764.

  • Y. N. Kunang, S. Nurmaini, D. Stiawan, and B. Y. Suprapto, “Attack classification of an intrusion detection system using deep learning and hyperparameter optimization,” J. Inf. Secur. Appl., vol. 58, no. March, p. 102804, 2021, doi: 10.1016/j.jisa.2021.102804.

  • T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Comput. Electr. Eng., vol. 99, no. February, p. 107810, 2022, doi: 10.1016/j.compeleceng.2022.107810.

  • M. Hammad, N. Hewahi, and W. Elmedany, “MMM-RF: A novel high accuracy multinomial mixture model for network intrusion detection systems,” Comput. Secur., vol. 120, 2022, doi: 10.1016/j.cose.2022.102777.

  • N. Gupta, V. Jindal, and P. Bedi, “CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems,” Comput. Secur., vol. 112, p. 102499, 2022, doi: 10.1016/j.cose.2021.102499.

  • R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.

  • A. Ferriyan, A. H. Thamrin, K. Takeda, and J. Murai, “Generating network intrusion detection dataset based on real and encrypted synthetic attack traffic,” Appl. Sci., vol. 11, no. 17, 2021, doi: 10.3390/app11177868.

  • J. Zhang, H. Ishibuchi, and L. He, “A classification-assisted environmental selection strategy for multiobjective optimization,” Swarm Evol. Comput., vol. 71, no. September 2021, p. 101074, 2022, doi: 10.1016/j.swevo.2022.101074.

  • W. Tang, X. M. Yang, X. Xie, L. M. Peng, C. H. Youn, and Y. Cao, “Avidity-model based clonal selection algorithm for network intrusion detection,” IEEE Int. Work. Qual. Serv. IWQoS, 2010, doi: 10.1109/IWQoS.2010.5542731.

  • H. Bangui and B. Buhnova, “Lightweight intrusion detection for edge computing networks using deep forest and bio-inspired algorithms,” Comput. Electr. Eng., vol. 100, no. March, p. 107901, 2022, doi: 10.1016/j.compeleceng.2022.107901.

  • F. Hosseinpour, K. A. Bakar, A. H. Hardoroudi, and A. F. Dareshur, “Design of a new distributed model for intrusion detection system based on artificial immune system,” Proc. - 6th Intl. Conf. Adv. Inf. Manag. Serv. IMS2010, with ICMIA2010 - 2nd Int. Conf. Data Min. Intell. Inf. Technol. Appl., pp. 378–383, 2010.


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  • Investigations on Bio-Inspired Algorithm for Network Intrusion Detection – A Review

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Authors

Jeyavim Sherin R C
School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India
Parkavi K
School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India

Abstract


A network is a collection of interconnected devices that can share information and resources, exchange files, and enable electronic communications. IDS is an important part of Network Security to secure a network. An Intrusion Detection System (IDS) is a fundamental building block in network security. A wide variety of techniques have been proposed and implemented to improve the performance and accuracy of intrusion detection models. It is used by many MNC companies such as Wipro, TCS, and L&T and they are having their IDS in the organization system. CNN (Convolutional Neural Network, Machine Learning, Data mining, Deep learning models such as SVM (Support Vector Machine) are performing well above the benchmark to prevent the systems from all kinds of attacks. Recently, bio-inspired optimization algorithms are metaheuristics that mimic the nature of solving optimization problems. Bio-inspired algorithms are gaining the moment that brings a revolution in computer science. This paper investigates the feature selection techniques of bio-inspired algorithms-driven Intrusion Detection Systems. This paper categorises these SI approaches based on their applicability in improving various aspects of an intrusion detection process. Furthermore, the paper discusses the capabilities and characteristics of various datasets used in experimentation. The main goal is to assist researchers in evaluating the capabilities and limitations of SI algorithms in identifying security threats and challenges in designing and implementing an IDS for the detection of cyber-attacks across multiple domains. The survey identifies existing issues and provides recommendations for how to effectively address them.

Keywords


IDS, Deep Learning, Optimization, Classification, Feature Selection, Bio-Inspired Algorithm.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F214503