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MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT

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Abstract

The number of Internet of Things (IoT) devices overgrows, and this technology dominates. The importance of IoT security and the growing need to devise intrusion detection systems (IDSs) to detect all types of attacks. The limited sources on the IoT. They have led researchers to explore and provide new and efficient solutions to create Botnet Detection in IoT systems. These systems use data features to detect network traffic status and thus detect malicious behavior. Also, data set features indicate the type of network traffic. Many features in the problem space and network behaviour unpredictability make IDSs the main challenge in establishing security in computer networks. Many unnecessary features have also made feature selection an essential aspect of attack detection systems. This paper developed a multi-objective MOAEOSCA algorithm hybridizing Artificial Ecosystem-based Optimization (AEO) algorithms and the Sine Cosine Algorithm (SCA) for botnet detection in IoT. By accurately identifying the weaknesses of the MOAEOSCA algorithm, it has been tried to cover the weaknesses to a large extent and to reach a robust algorithm. We promoted the proposed algorithm using Bitwise operations, Disruption operator, and Opposition-based learning (OBL) mechanisms. Ten standard datasets in the UCI repository were examined to evaluate the proposed algorithm’s performance in solving the feature selection problem to detect a botnet. Simulation findings indicated that the proposed algorithm had an acceptable accuracy in Botnet Detection in the IoT, outperforming other methods. According to the experiments carried out in this paper, the MOAEOSCA algorithm has shown that nine data sets out of ten data sets in the feature selection problem performed better than other optimization algorithms. But in all seven botnet data sets, performance has shown better than different optimization algorithms.

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All data sets used in this paper are downloaded from the UCI data repository.

References

  1. Abdollahzadeh B, Gharehchopogh FS (2021) A multi-objective optimization algorithm for feature selection problems. Eng Comput. 1–19.

  2. Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7:43473–43486

    Article  Google Scholar 

  3. Al Shorman A, Faris H, Aljarah I (2020) Unsupervised intelligent system based on one class support vector machine and Grey wolf optimization for IoT botnet detection. J Ambient Intell Humaniz Comput 11(7):2809–2825

    Article  Google Scholar 

  4. Aladeemy M, … Poranki S (2020) New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows. Appl Soft Comput 86:105866

    Article  Google Scholar 

  5. Al-Kasassbeh M et al. Detection of IoT-botnet attacks using fuzzy rule interpolation. J Intell Fuzzy Syst (Preprint). : 1–11

  6. AlKhatib AA, Sawalha T, AlZu’bi S (2020) Load balancing techniques in software-defined cloud computing: an overview. In 2020 seventh international conference on software defined systems (SDS). IEEE

  7. Al-Tashi Q et al (2019) Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7:39496–39508

    Article  Google Scholar 

  8. AlZu’bi S, … Gupta BB (2019) An efficient employment of internet of multimedia things in smart and future agriculture. Multimed Tools Appl 78(20):29581–29605

    Article  Google Scholar 

  9. AlZu’bi S, Aqel D, Mughaid A (2021) Recent intelligent approaches for managing and optimizing smart blood donation process. In 2021 international conference on information technology (ICIT). IEEE.

  10. AlZu'bi S, Jararweh Y (2020) Data fusion in autonomous vehicles research, literature tracing from imaginary idea to smart surrounding community. In 2020 fifth international conference on fog and Mobile edge computing (FMEC). IEEE.

  11. Armano G, Farmani MR (2016) Multiobjective clustering analysis using particle swarm optimization. Expert Syst Appl 55:184–193

    Article  Google Scholar 

  12. Asghari K, … Saneifard R (2021) A fixed structure learning automata-based optimization algorithm for structure learning of Bayesian networks. Expert Syst 38(7):e12734

    Article  Google Scholar 

  13. Asghari K, … Saneifard R (2021) Multi-swarm and chaotic whale-particle swarm optimization algorithm with a selection method based on roulette wheel. Expert Syst 38(8):e12779

    Article  Google Scholar 

  14. Azizi M, et al. (2022) Multi-objective atomic orbital search (MOAOS) for global and engineering design optimization. IEEE Access,

  15. Bagui S, Wang X, Bagui S (2021) Machine learning based intrusion detection for IoT botnet. Int J Mach Learn Comput 11(6):406

    Google Scholar 

  16. Bezerra VH et al. (2018) One-class classification to detect botnets in IoT devices∗. In Anais Principais do XVIII Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais. SBC

  17. Chen S-C, Chen Y-R, Tzeng W-G (2018) Effective botnet detection through neural networks on convolutional features. In 2018 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data Science and engineering (TrustCom/BigDataSE). IEEE

  18. Cheraghchi F, … Petriu E (2018) Modeling the speed-based vessel schedule recovery problem using evolutionary multiobjective optimization. Inf Sci 448:53–74

    Article  MathSciNet  Google Scholar 

  19. Chuang L-Y, … Yang CH (2011) A hybrid feature selection method for DNA microarray data. Comput Biol Med 41(4):228–237

    Article  Google Scholar 

  20. Ghafori S, Gharehchopogh FS (2022) A multiobjective Cuckoo Search Algorithm for community detection in social networks, in Multi-Objective Combinatorial Optimization Problems and Solution Methods. Elsevier. 177–193

  21. Ghaith IH, Rawashdeh A, Al Zubi S (2021) Transfer learning in data fusion at autonomous driving. In 2021 international conference on information technology (ICIT). IEEE

  22. Ghanem W, Jantan A (2016) Novel multi-objective artificial bee Colony optimization for wrapper based feature selection in intrusion detection. Int J Adv Soft Comput Appl 8(1)

  23. Gharehchopogh FS (2022) Advances in tree seed algorithm: A comprehensive survey. Arch Comput Methods Eng. 1–24

  24. Gharehchopogh FS (2022) An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems. J Bionic Eng. 1–26.

  25. Ghosh AK (2006) On optimum choice of k in nearest neighbor classification. Comput Stat Data Anal 50(11):3113–3123

    Article  MathSciNet  MATH  Google Scholar 

  26. Goldanloo MJ, Gharehchopogh FS (2022) A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems. J Supercomput 78(3):3998–4031

    Article  Google Scholar 

  27. Habib, M., I. Aljarah, and H. Faris (2020) A modified multi-objective particle swarm optimizer-based Lévy flight: an approach toward intrusion detection in internet of things. Arabian J Sci Eng.

  28. Habib M, et al. (2020) Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things, in Evolutionary Machine Learning Techniques. Springer. p. 203–229

  29. Hamdani TM et al (2007) Multi-objective feature selection with NSGA II. In international conference on adaptive and natural computing algorithms.. Springer.

  30. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier.

  31. Hancer E, … Akay B (2018) Pareto front feature selection based on artificial bee colony optimization. Inf Sci 422:462–479

    Article  Google Scholar 

  32. Hassan BA, Rashid TA (2021) A multidisciplinary ensemble algorithm for clustering heterogeneous datasets. Neural Comput & Applic 33(17):10987–11010

    Article  Google Scholar 

  33. Hattawi W et al (2021) Recent quality models in BigData applications. In 2021 international conference on information technology (ICIT). IEEE

  34. Hosseini S, Nezhad AE, Seilani H (2022) Botnet detection using negative selection algorithm, convolution neural network and classification methods. Evol Syst 13(1):101–115

    Article  Google Scholar 

  35. Jagadeesan, S. and B. Amutha, An Efficient Botnet Detection with the Enhanced Support Vector Neural Network. Measurement, 2021: p. 109140

  36. Kesavamoorthy R, Soundar KR (2019) Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system. Clust Comput 22(4):9469–9476

    Article  Google Scholar 

  37. Khammassi C, Krichen S (2020) A NSGA2-LR wrapper approach for feature selection in network intrusion detection. Comput Netw 172:107183

    Article  Google Scholar 

  38. Khan MA, Salah K (2018) IoT security: review, blockchain solutions, and open challenges. Futur Gener Comput Syst 82:395–411

    Article  Google Scholar 

  39. Khodadadi N, … Sareh P (2021) Multi-objective crystal structure algorithm (MOCryStAl): introduction and performance evaluation. IEEE Access 9:117795–117812

    Article  Google Scholar 

  40. Khodadadi N, Talatahari S, Dadras Eslamlou A (2022) MOTEO: a novel multi-objective thermal exchange optimization algorithm for engineering problems. Soft Comput. 1–26

  41. Knowles J, Corne D (2002) On metrics for comparing nondominated sets. In proceedings of the 2002 congress on evolutionary computation. CEC'02 (cat. No. 02TH8600). IEEE.

  42. Kuhn M, Johnson K (2013) Applied predictive modeling. 26. Springer

  43. Li J, … Zhang H (2018) Ai-based two-stage intrusion detection for software defined iot networks. IEEE Internet Things J 6(2):2093–2102

    Article  MathSciNet  Google Scholar 

  44. Li S, … Tang C (2018) An improved information security risk assessments method for cyber-physical-social computing and networking. IEEE Access 6:10311–10319

    Article  Google Scholar 

  45. Lin Q, … Chen J (2016) Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf Sci 339:332–352

    Article  Google Scholar 

  46. Ma X, … Zhu Z (2017) On Tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Trans Evol Comput 22(2):226–244

    Article  Google Scholar 

  47. McDermott CD, Majdani F, Petrovski AV (2018) Botnet detection in the internet of things using deep learning approaches. In 2018 international joint conference on neural networks (IJCNN). IEEE.

  48. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  49. Mohammadi M, … Hosseinzadeh M (2021) A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. J Netw Comput Appl 178:102983

    Article  Google Scholar 

  50. Mohemmed AW, Zhang M (2008) Evaluation of particle swarm optimization based centroid classifier with different distance metrics. In 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence). IEEE

  51. Mohmmadzadeh H, Gharehchopogh FS (2021) An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems. J Supercomput 77(8):9102–9144

    Article  Google Scholar 

  52. Nadimi-Shahraki MH, … Abd Elaziz M (2021) Migration-based moth-flame optimization algorithm. Processes 9(12):2276

    Article  MathSciNet  Google Scholar 

  53. Nadimi-Shahraki MH, … Abualigah L (2021) An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems. Entropy 23(12):1637

    Article  MathSciNet  Google Scholar 

  54. Nadimi-Shahraki MH, … Bahreininejad A (2022) GGWO: gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems. J Comput Sci 61:101636

    Article  Google Scholar 

  55. Naseri TS, Gharehchopogh FS (2022) A feature selection based on the farmland fertility algorithm for improved intrusion detection systems. J Netw Syst Manag 30(3):1–27

    Article  Google Scholar 

  56. Neggaz N, … Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103

    Article  Google Scholar 

  57. Nguyen H-T, Ngo Q-D, Le V-H (2018) IoT botnet detection approach based on PSI graph and DGCNN classifier. In 2018 IEEE international conference on information communication and signal processing (ICICSP). IEEE

  58. Oliva D, Abd Elaziz M (2020) An improved brainstorm optimization using chaotic opposite-based learning with disruption operator for global optimization and feature selection. Soft Comput 24:1–22

    Article  Google Scholar 

  59. Padmavathi B, Muthukumar B (2022) An efficient botnet detection approach based on feature learning and classification. J Control Decision:1–14

  60. Pan A, … Wu Q (2018) A diversity enhanced multiobjective particle swarm optimization. Inf Sci 436:441–465

    Article  MathSciNet  MATH  Google Scholar 

  61. Qadir QM, … Zhang Z (2018) Low power wide area networks: a survey of enabling technologies, applications and interoperability needs. IEEE Access 6:77454–77473

    Article  Google Scholar 

  62. Rahman CM, Rashid TA (2021) A new evolutionary algorithm: learner performance based behavior algorithm. Egypt Inf J 22(2):213–223

    Google Scholar 

  63. Rana S et al (2018) An effective lightweight cryptographic algorithm to secure resource-constrained devices. Spectrum 9(11)

  64. Rezaee H et al (2011) Tracking and occlusion handling in multi-sensor networks by particle filter. In 2011 IEEE GCC conference and exhibition (GCC). IEEE

  65. Roopak M, Tian GY, Chambers J (2020) Multi-objective-based feature selection for DDoS attack detection in IoT networks. IET Networks 9(3):120–127

    Article  Google Scholar 

  66. Samadi Bonab M, … Alemi P (2020) A wrapper-based feature selection for improving performance of intrusion detection systems. Int J Commun Syst 33(12):e4434

    Article  Google Scholar 

  67. Sanchez-Pi N, Martí L, Molina JM (2018) Applying voreal for iot intrusion detection. In international conference on hybrid artificial intelligence systems. Springer.

  68. Selvarani P, Suresh A, Malarvizhi N (2019) Secure and optimal authentication framework for cloud management using HGAPSO algorithm. Clust Comput 22(2):4007–4016

    Article  Google Scholar 

  69. Shamsaldin AS, … Mohammadi M (2019) Donkey and smuggler optimization algorithm: a collaborative working approach to path finding. J Comput Design Eng 6(4):562–583

    Article  Google Scholar 

  70. Sreenivasamurthy S, Obraczka K (2018) Clustering for load balancing and energy efficiency in IoT applications. In 2018 IEEE 26th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS). IEEE

  71. Suman C, Tripathy S, Saha S (2019) Building an effective intrusion detection system using unsupervised feature selection in multi-objective optimization framework. arXiv preprint arXiv:1905.06562

  72. Téllez N, et al. (2018) A tabu search method for load balancing in fog computing. Int. J Artif Intell. 16(2)

  73. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06). IEEE

  74. Wang X-H et al (2020) Multi-objective feature selection based on artificial bee colony: an acceleration approach with variable sample size. Appl Soft Comput 88:106041

    Article  MathSciNet  Google Scholar 

  75. Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656–1671

    Article  Google Scholar 

  76. Xue Y, … Pang W (2018) An evolutionary computation based feature selection method for intrusion detection. Sec Commun Networks 2018:1–10

    Article  Google Scholar 

  77. Zavala GR, … Coello Coello CA (2014) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49(4):537–558

    Article  MathSciNet  Google Scholar 

  78. Zhao W, Wang L, Zhang Z (2019) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput & Applic 32:1–43

    Google Scholar 

  79. Zhu Y, … Ming Z (2017) An improved NSGA-III algorithm for feature selection used in intrusion detection. Knowl-Based Syst 116:74–85

    Article  Google Scholar 

  80. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Fatemeh Hosseini, Farhad Soleimanian Gharehchopogh & Mohammad Masdari

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  1. Fatemeh Hosseini
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  2. Farhad Soleimanian Gharehchopogh
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  3. Mohammad Masdari
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Hosseini, F., Gharehchopogh, F.S. & Masdari, M. MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT. Multimed Tools Appl 82, 13369–13399 (2023). https://doi.org/10.1007/s11042-022-13836-6

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