Abstract
Open wireless sensor networks (WSNs) in Internet of things (IoT) has led to many zero-day security attacks. Since intrusion detection is a key security solution, this paper presents a lightweight machine learning-based intrusion detection technique with high performance for resource limited IoT wireless networks namely, IoT intrusion detection system (IoTIDS). IoTIDS is based on hybridization of genetic algorithm (GA) and grey wolf optimizer (GWO), termed as GA–GWO. The main aim of the hybrid algorithm for the IoTIDS is to reduce the dimensionality of the huge wireless network traffic through intelligent selecting the most informative traffic features. By hybridizing, we try to eliminate their weaknesses through GA and GWO strengths. The effectiveness of the GA–GWO on IoTIDS is evaluated using AWID (aegean wi-fi intrusion dataset) as a new real-world wireless intrusion dataset, after preprocessing it under different scenarios. The experimental results proved that the proposed GA–GWO individually not only improved the performance of the IoTIDS in terms of computational costs, but it also enabled the IoTIDS to detect ? with high accuracy and low false alarm rate. Furthermore, GA–GWO in comparison to the original GA and GWO and other recent existing methods like FS, weight, and parameter optimization of SVM based on the GA (FWP-SVM-GA) and binary GWO (BGWO) has proven to be more effective.
Similar content being viewed by others
References
Aggarwal P, Sharma SK (2015) Analysis of KDD dataset attributes-class wise for intrusion detection. Procedia Comput Sci 57:842–851
Ahmad I, Basheri M, Iqbal MJ, Rahim A (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6:33789–33795
Ahmad I, Hussain M, Alghamdi A, Alelaiwi A (2014) Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components. Neural Comput Appl 24(7–8):1671–1682
Al-Betar MA, Awadallah MA, Faris H, Aljarah I, Hammouri AI (2018) Natural selection methods for grey wolf optimizer. Expert Syst Appl 113:481–498
Al-Garadi MA, Mohamed A, Al-Ali A, Du X, Guizani M (2018) A survey of machine and deep learning methods for internet of things (IoT) security. arXiv preprint arXiv:1807.11023
Aldosari HM (2015) A proposed security layer for the Internet of things communication reference model. Proc Comput Sci 65:95–98
Aličković E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Appl 28(4):753–763
Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160
Alzubi QM, Anbar M, Alqattan ZN, Al-Betar MA, Abdullah R (2019) Intrusion detection system based on a modified binary grey wolf optimisation. Neural Comput Appl 2019:1–13
Aminanto ME, Tanuwidjaja HC, Yoo PD, Kim K (2017) Wi-Fi intrusion detection using weighted-feature selection for neural networks classifier. In: 2017 International workshop on big data and information security (IWBIS). IEEE, pp 99–104
Aminanto ME, Kim K (2016) Detecting impersonation attack in WiFi networks using deep learning approach. In: International workshop on information security applications. Springer, Cham, pp 136–147
Amouri A, Alaparthy VT, Morgera SD (2018) Cross layer-based intrusion detection based on network behavior for IoT. In: 2018 IEEE 19th wireless and microwave technology conference (WAMICON). IEEE, pp 1–4
Andročec D, Vrček N (2018) Machine learning for the internet of things security: a systematic. In: 13th International conference on software technologies. https://doi.org/10.5220/0006841205970604
Anusha K, Sathiyamoorthy E (2016) Comparative study for feature selection algorithms in intrusion detection system. Autom Control Comput Sci 50(1):1–9
Aziz ASA, Sanaa EL, Hassanien AE (2017) Comparison of classification techniques applied for network intrusion detection and classification. J Appl Logic 24(2017):109–118
Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford
Balasaraswathi VR, Sugumaran M, Hamid Y (2017) Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J Commun Inf Netw 2(4):107–119
Bamakan SMH, Wang H, Yingjie T, Shi Y (2016) An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102
Beheshti Z, Shamsuddin SMH (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5(1):1–35
Bekara C (2014) Security issues and challenges for the IoT-based smart grid. Proc Comput Sci 34:532–537
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308
Blum C, Merkle D (2008) Swarm intelligence. In: Blum C, Merkle D (eds) Swarm intelligence in optimization, pp 43–85
Bostani H, Sheikhan M (2017a) Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach. Comput Commun 98:52–71
Bostani H, Sheikhan M (2017b) Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft Comput 21(9):2307–2324
Bouraoui A, Jamoussi S, BenAyed Y (2018) A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines. Artif Intell Rev 50(2):261–281
Brown C, Cowperthwaite A, Hijazi A, Somayaji A (2009) Analysis of the 1999 darpa/lincoln laboratory ids evaluation data with netadhict. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–7
Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
Butun I, Morgera SD, Sankar R (2013) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutor 16(1):266–282
Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28
Chen Z, Lin T, Tang N, Xia X (2016) A parallel genetic algorithm based feature selection and parameter optimization for support vector machine. Sci Program 2016:1–10
Dastanpour A, Mahmood RAR (2013) Feature selection based on genetic algorithm and SupportVector machine for intrusion detection system. In: The second international conference on informatics engineering and information science (ICIEIS2013), pp 169–181
Davis L (1991). A review of: handbook of genetic algorithms, vol 3. Taylor & Francis, pp 446–448
Desale KS, Ade R (2015) Genetic algorithm based feature selection approach for effective intrusion detection system. In: 2015 international conference on computer communication and informatics (ICCCI). IEEE, pp 1–6
Dhanabal L, Shantharajah SP (2015) A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int J Adv Res Comput Commun Eng 4(6):446–452
Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for Internet of Things. Future Gener Comput Syst 82:761–768
Domb M, Bonchek-Dokow E, Leshem G (2017) Lightweight adaptive Random-Forest for IoT rule generation and execution. J Inf Secur Appl 34:218–224
Eesa AS, Orman Z, Brifcani AMA (2015) A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst Appl 42(5):2670–2679
Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evolut Comput 1(1):19–31
El-Khatib K (2009) Impact of feature reduction on the efficiency of wireless intrusion detection systems. IEEE Trans Parallel Distrib Syst 21(8):1143–1149
Elhag S, Fernández A, Bawakid A, Alshomrani S, Herrera F (2015) On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst Appl 42(1):193–202
Emary E, Yamany W, Hassanien AE, Snasel V (2015a) Multi-objective gray-wolf optimization for attribute reduction. Proc Comput Sci 65:623–632
Emary E, Zawbaa HM, Hassanien AE (2016a) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65
Emary E, Zawbaa HM, Hassanien AE (2016b) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381
Emary E, Zawbaa HM, Grosan C, Hassenian AE (2015b) Feature subset selection approach by gray-wolf optimization. In: Afro-European conference for industrial advancement. Springer, Cham, pp 1–13
Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435
Ferriyan A, Thamrin AH, Takeda K, Murai J (2017) Feature selection using genetic algorithm to improve classification in network intrusion detection system. In: 2017 International electronics symposium on knowledge creation and intelligent computing (IES-KCIC). IEEE, pp 46–49
Gallagher K, Sambridge M (1994) Genetic algorithms: a powerful tool for large-scale nonlinear optimization problems. Comput Geosci 20(7–8):1229–1236
Ganapathy S, Kulothungan K, Muthurajkumar S, Vijayalakshmi M, Yogesh P, Kannan A (2013) Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. EURASIP J Wirel Commun Netw 2013(1):271
Gendreau AA, Moorman M (2016) Survey of intrusion detection systems towards an end to end secure internet of things. In: 2016 IEEE 4th international conference on future internet of things and cloud (FiCloud). IEEE, pp 84–90
Hamed T, Dara R, Kremer SC (2018) Network intrusion detection system based on recursive feature addition and bigram technique. Comput Secur 73:137–155
Hancer E, Xue B, Zhang M, Karaboga D, Akay B (2018) Pareto front feature selection based on artificial bee colony optimization. Inf Sci 422:462–479
Holland JH (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Digitized Nov. 27, 2007, ISBN 0472084607, p 183
Holland JH (1992a) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge
Holland JH (1992b) Genetic algorithms. Sci Am 267(1):66–72
Holland JH, Koza JR (1992) Genetic programming. Sci Am 267:66–72
Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240
Jafier SH (2018) Utilizing feature selection techniques in intrusion detection system for internet of things. In: Proceedings of the 2nd international conference on future networks and distributed systems. ACM, p 68
Kang SH, Kim KJ (2016) A feature selection approach to find optimal feature subsets for the network intrusion detection system. Clust Comput 19(1):325–333
Karthick PA, Ghosh DM, Ramakrishnan S (2018) Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput Methods Prog Biomed 154:45–56
Khammassi C, Krichen S (2017) A GA-LR wrapper approach for feature selection in network intrusion detection. Comput Secur 70:255–277
Kishor A, Singh PK (2016) Empirical study of grey wolf optimizer. In: Proceedings of fifth international conference on soft computing for problem solving. Springer, Singapore, pp 1037–1049
Kolias C, Kambourakis G, Maragoudakis M (2011) Swarm intelligence in intrusion detection: a survey. Comput Secur 30(8):625–642
Kolias C, Kambourakis G, Stavrou A, Gritzalis S (2015) Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun Surv Tutor 18(1):184–208
Larose DT, Larose CD (2014) Discovering knowledge in data: an introduction to data mining. Wiley, Hoboken
Li J, Zhao Z, Li R, Zhang H (2018) AI-based two-stage intrusion detection for software defined IoT Networks. IEEE Internet Things J 6(2):2093–2102
Li Q, Chen H, Huang H, Zhao X, Cai Z, Tong C et al (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med 2017:1–15
Lin WC, Ke SW, Tsai CF (2015) CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl Based Syst 78:13–21
Liu L, Xu B, Zhang X, Wu X (2018) An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP J Wirel Commun Netw 2018(1):113
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453
Mazini M, Shirazi B, Mahdavi I (2019) Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. J King Saud Univ Comput Inf Sci 31(4):541–553
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2016) Gray wolf optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186
Mendez DM, Papapanagiotou I, Yang B (2017) Internet of things: survey on security and privacy. arXiv preprint arXiv:1707.01879
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:8
Nehinbe JO (2011) A critical evaluation of datasets for investigating IDSs and IPSs researches. In: 2011 IEEE 10th international conference on cybernetic intelligent systems (CIS). IEEE, pp 92–97
Olivier F, Carlos G, Florent N (2015) New security architecture for IoT network. Proc Comput Sci 52:1028–1033
Oreski P, Benediktsson JA (2014) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12(2):309–313
Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst Appl 41(4):2052–2064
Phan AV, Le Nguyen M, Bui LT (2017) Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Appl Intell 46(2):455–469
Qureshi AUH, Larijani H, Mtetwa N, Javed A, Ahmad J (2019) RNN-ABC: a new swarm optimization based technique for anomaly detection. Computers 8(3):59
Qureshi AUH, Larijani H, Ahmad J, Mtetwa N (2019a) A heuristic intrusion detection system for internet-of-things (IoT). In: Proceedings of the intelligent computing—proceedings of the computing conference, London, UK, pp 16–17
Raman MG, Somu N, Kirthivasan K, Liscano R, Sriram VS (2017) An efficient intrusion detection system based on hypergraph-genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl Based Syst 134:1–12
Ray PP (2018) A survey on Internet of Things architectures. J King Saud Univ Comput Inf Sci 30(3):291–319
Restuccia F, D’Oro S, Melodia T (2018) Securing the internet of things in the age of machine learning and software-defined networking. IEEE Internet Things J 5(6):4829–4842
Roopa Devi EM, Suganthe RC (2017) Feature selection in intrusion detection grey wolf optimizer. Asian J Res Soc Sci Humanit 7(3):671–682
Roopa Devi EM, Suganthe RC (2018) Enhanced transductive support vector machine classification with grey wolf optimizer cuckoo search optimization for intrusion detection system. Concurrency Comput Pract Exp 32:e4999
Sathish V, Khader P, Abdul S (2017) Improved detecting host based intrusions based on hybrid SVM using grey wolf optimizer. Int J Secur Appl 11(9):59–72
Sen S, Clark JA (2011) Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Comput Netw 55(15):3441–3457
Senthilnayaki B, Venkatalakshmi K, Kannan A (2013) An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier. In: 2013 fifth international conference on advanced computing (ICoAC). IEEE, pp 1–7
Senthilnayaki B, Venkatalakshmi K, Kannan A (2015) Intrusion detection using optimal genetic feature selection and SVM based classifier. In: 2015 3rd international conference on signal processing, communication and networking (ICSCN). IEEE, pp 1–4
Seth JK, Chandra S (2016) Intrusion detection based on key feature selection using binary GWO. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE, pp 3735–3740
Sfar AR, Natalizio E, Challal Y, Chtourou Z (2018) A roadmap for security challenges in the Internet of Things. Digit Commun Netw 4(2):118–137
Shams EA, Rizaner A (2018) A novel support vector machine based intrusion detection system for mobile ad hoc networks. Wirel Netw 24(5):1821–1829
Sheikhan M, Bostani H (2016) A hybrid intrusion detection architecture for internet of things. In: 2016 8th international symposium on telecommunications (IST). IEEE, pp 601–606
Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recogn 64:141–158
Sheikhpour R, Sarram MA, Sheikhpour R (2016) Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer. Appl Soft Comput 40:113–131
Shenfield A, Day D, Ayesh A (2018) Intelligent intrusion detection systems using artificial neural networks. ICT Express 4(2):95–99
Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31(3):357–374
Shunmugapriya P, Kanmani S (2017) A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm Evolut Comput 36:27–36
Siedlecki W, Sklansky J (1993) A note on genetic algorithms for large-scale feature selection. In: Handbook of pattern recognition and computer vision. World Scentific Publishing, pp 88–107
Sindhu SSS, Geetha S, Kannan A (2012) Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1):129–141
Singh N, Singh SB (2017) Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance. J Appl Math 2017:15
Srivastava D, Singh R, Singh V (2019) An Intelligent gray wolf optimizer: a nature inspired technique in intrusion detection system (IDS). J Adv Robot 6(1):18–24p
Tao P, Sun Z, Sun Z (2018) An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624–13631
Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–6
Tawhid MA, Dsouza KB (2018) Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Appl Comput Inform 1(2):181–200
Thanthrige USKPM, Samarabandu J, Wang X (2016) Machine learning techniques for intrusion detection on public dataset. In: 2016 IEEE Canadian conference on electrical and computer engineering (CCECE). IEEE, pp 1–4
Thaseen IS, Kumar CA (2017) Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J King Saud Univ Comput Inf Sci 29(4):462–472
Too J, Abdullah A, Mohd Saad N, Mohd Ali N, Tee W (2018) A new competitive binary grey wolf optimizer to solve the feature selection problem in emg signals classification. Computers 7(4):58
Tsai CF, Eberle W, Chu CY (2013) Genetic algorithms in feature and instance selection. Knowl Based Syst 39:240–247
Usha M, Kavitha P (2017) Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier. Wirel Netw 23(8):2431–2446
Valdez F (2015) Bio-inspired optimization methods. In: Springer handbook of computational intelligence. Springer, Berlin, pp 1533–1538
Verma A, Ranga V (2018) Statistical analysis of CIDDS-001 dataset for network intrusion detection systems using distance-based machine learning. Proc Comput Sci 125:709–716
Witten IH, Frank E, Hall MA, Pal CJ (2016) Data Mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington
Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: practical machine learning tools and techniques with Java implementations
Xiao L, Wan X, Lu X, Zhang Y, Wu D (2018) IoT security techniques based on machine learning. arXiv preprint arXiv:1801.06275
Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H et al (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365–35381
Xue B, Zhang M, Browne WN, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626
Xue Y, Jia W, Zhao X, Pang W (2018) An evolutionary computation based feature selection method for intrusion detection. Secur Commun Netw 2018
Yong Z, Dun-wei G, Wan-qiu Z (2016) Feature selection of unreliable data using an improved multi-objective PSO algorithm. Neurocomputing 171:1281–1290
Zarpelão BB, Miani RS, Kawakani CT, de Alvarenga SC (2017) A survey of intrusion detection in Internet of Things. J Netw Comput Appl 84:25–37
Zawbaa HM, Emary E, Grosan C (2016) Feature selection via chaotic antlion optimization. PLoS ONE 11(3):e0150652
Zawbaa HM, Emary E, Grosan C, Snasel V (2018) Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evolut Comput 42:29–42
Zeng D, Wang S, Shen Y, Shi C (2017) A GA-based feature selection and parameter optimization for support tucker machine. Proc Comput Sci 111:17–23
Zhang Y, Song XF, Gong DW (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418:561–574
Zorarpacı E, Özel SA (2016) A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst Appl 62:91–103
Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 45(3):35
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Davahli, A., Shamsi, M. & Abaei, G. Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. J Ambient Intell Human Comput 11, 5581–5609 (2020). https://doi.org/10.1007/s12652-020-01919-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-01919-x