Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things

  • Maria Habib
  • Ibrahim Aljarah
  • Hossam Faris
  • Seyedali MirjaliliEmail author
Part of the Algorithms for Intelligent Systems book series (AIS)


Nowadays, the world witnesses an immense growth in Internet of things devices. Such devices are found in smart homes, wearable devices, retail, health care, industry, and transportation. As we are entering Internet of things (IoT) digital era, IoT devices not only hack our world, but also start to hack our personal life. The widespread IoT has created a rich platform for potential IoT cyberattacks. Data mining and machine learning techniques have significant roles in the field of IoT botnet detection. The aim of this chapter is to develop detection model based on multi-objective particle swarm optimization (MOPSO) for identifying the malicious behaviors in IoT network traffic. The performance of MOPSO is verified against multi-objective non-dominating sorting genetic algorithm (NSGA-II), common traditional machine learning algorithms, and some conventional filter-based feature selection methods. As per the obtained results, MOPSO is competitive and outperforms NSGA-II, traditional machine learning methods, and filter-based methods in most of the studied datasets.


Internet of things Classification Multi-objective particle swarm optimization Non-dominating sorting genetic algorithm Multi-objective feature selection Botnets 


  1. 1.
    Ahmed S, Mafarja M, Faris H, Aljarah I (2018) Feature selection using salp swarm algorithm with chaos. In: Proceedings of the 2nd international conference on intelligent systems, metaheuristics & swarm intelligence. ACM, pp 65–69Google Scholar
  2. 2.
    Al-Dabagh MZN, Alhabib MHM, AL-Mukhtar FH (2018) Face recognition system based on kernel discriminant analysis k-nearest neighbor and support vector machine. Int J Res Eng 5(3):335–338CrossRefGoogle Scholar
  3. 3.
    Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput 1–18Google Scholar
  4. 4.
    Aljarah I, Ludwig SA (2013) Mapreduce intrusion detection system based on a particle swarm optimization clustering algorithm. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 955–962Google Scholar
  5. 5.
    Aljarah I, Ludwig SA (2013) Towards a scalable intrusion detection system based on parallel pso clustering using mapreduce. In: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. ACM, pp 169–170Google Scholar
  6. 6.
    Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979CrossRefGoogle Scholar
  7. 7.
    Angrishi K (2017) Turning internet of things (iot) into internet of vulnerabilities (iov): Iot botnets. arXiv preprint arXiv:1702.03681
  8. 8.
    Antonakakis M, April T, Bailey M, Bernhard M, Bursztein E, Cochran J, Durumeric Z, Halderman JA, Invernizzi L, Kallitsis M et al (2017) Understanding the mirai botnet. In: USENIX security symposium, pp 1092–1110Google Scholar
  9. 9.
    Atallah DM, Badawy M, El-Sayed A, Ghoneim MA (2019) Predicting kidney transplantation outcome based on hybrid feature selection and knn classifier. Multimed Tools Appl 1–25Google Scholar
  10. 10.
    bin Mohd Zain MZ, Kanesan J, Chuah JH, Dhanapal S, Kendall G (2018) A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Appl Soft ComputGoogle Scholar
  11. 11.
    Bramer M (2007) Principles of data mining, vol 180. SpringerGoogle Scholar
  12. 12.
    Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Appl Soft Comput 40(1):16–28Google Scholar
  13. 13.
    Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRefGoogle Scholar
  14. 14.
    Conti M, Dehghantanha A, Franke K, Watson S (2018). Challenges and opportunities. Internet Things Secur ForensicsGoogle Scholar
  15. 15.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms. MIT pressGoogle Scholar
  16. 16.
    Dua D, Efi KT (2017) UCI machine learning repositoryGoogle Scholar
  17. 17.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro machine and human science, 1995. MHS’95., Proceedings of the sixth international symposium on. IEEE, pp 39–43Google Scholar
  18. 18.
    Elrawy MF, Awad AI, Hamed HFA (2018) Intrusion detection systems for iot-based smart environments: a survey. J Cloud Comput 7(1):21CrossRefGoogle Scholar
  19. 19.
    Faris Al-Zoubi AM, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48:67–83CrossRefGoogle Scholar
  20. 20.
    Faris H, Aljarah I, Al-Shboul B (2016) A hybrid approach based on particle swarm optimization and random forests for e-mail spam filtering. In: International conference on computational collective intelligence. Springer, pp 498–508Google Scholar
  21. 21.
    Faris H, Aljarah I et al (2015) Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In:2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, pp 1–5Google Scholar
  22. 22.
    Faris H, Hassonah MA, Al-Zoubi AM, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369CrossRefGoogle Scholar
  23. 23.
    Faris H, Mafarja MM, Heidari AA, Aljarah I, Al-Zoubi AM, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67CrossRefGoogle Scholar
  24. 24.
    Freund Y, Schapire RE (1999) Large margin classification using the perceptron algorithm. Mach Learn 37(3):277–296CrossRefGoogle Scholar
  25. 25.
    Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput & Secur 28(1–2):18–28CrossRefGoogle Scholar
  26. 26.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18CrossRefGoogle Scholar
  27. 27.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. ElsevierGoogle Scholar
  28. 28.
    Hemdan EE-D, Manjaiah DH (2018) Cybercrimes investigation and intrusion detection in internet of things based on data science methods. In: Cognitive computing for big data systems over IoT. Springer, pp 39–62Google Scholar
  29. 29.
    Jing Q, Vasilakos AV, Wan J, Lu J, Qiu D (2014) Security of the internet of things: perspectives and challenges. Wirel Netw 20(8):2481–2501CrossRefGoogle Scholar
  30. 30.
    Kesavamoorthy R, Soundar KR (2018) Swarm intelligence based autonomous ddos attack detection and defense using multi agent system. Clust Comput 1–8Google Scholar
  31. 31.
    Kolias C, Kambourakis G, Stavrou A, Voas J (2017) Ddos in the iot: mirai and other botnets. Computer 50(7):80–84CrossRefGoogle Scholar
  32. 32.
    Kowshalya MA, Valarmathi ML (2016) Detection of sybil’s across communities over social internet of things. J Appl Eng Sci 14(1):75–83CrossRefGoogle Scholar
  33. 33.
    Kuhn M, Johnson K (2013) Applied predictive modeling, vol 26. SpringerGoogle Scholar
  34. 34.
    Li J, Zhao Z, Li R, Zhang H, Zhang T (2018) Ai-based two-stage intrusion detection for software defined iot networks. IEEE Internet Things JGoogle Scholar
  35. 35.
    Liu L, Xu B, Wu Zhang XX (2018) An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP J Wirel Commun Netw 1:113CrossRefGoogle Scholar
  36. 36.
    Mafarja M, Aljarah I, Faris H, Hammouri AI, Al-Zoubi AM, Mirjalili S (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286CrossRefGoogle Scholar
  37. 37.
    Mafarja M, Aljarah I, Heidari AA, Faris H, Fournier-Viger P, Li X, Mirjalili S (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl-Based Syst 161:185–204CrossRefGoogle Scholar
  38. 38.
    Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Al-Zoubi AM, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25–45CrossRefGoogle Scholar
  39. 39.
    Mafarja M, Heidari AA, Faris H, Mirjalili S, Aljarah I (2020) Dragonfly algorithm: theory, literature review, and application in feature selection. In: Nature-inspired optimizers. Springer, pp 47–67Google Scholar
  40. 40.
    Mafarja MM, Mirjalili S (2018) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Comput 1–17Google Scholar
  41. 41.
    Marzano A, Alexander D, Fonseca O, Fazzion E, Hoepers C, Steding-Jessen K, Chaves MHPC, Cunha Í, Guedes D, Meira W (2018) The evolution of bashlite and mirai iot botnets. In: 2018 IEEE symposium on computers and communications (ISCC). IEEE, pp 00813–00818Google Scholar
  42. 42.
    Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) Nbc-maids: naïve bayesian classification technique in multi-agent system-enriched ids for securing iot against ddos attacks. J Supercomput 1–15Google Scholar
  43. 43.
    Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y (2018) N-baiot network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput 17(3):12–22CrossRefGoogle Scholar
  44. 44.
    Mir A, Nasiri JA (2018) Knn-based least squares twin support vector machine for pattern classification. Appl Intell 48(12):4551–4564CrossRefGoogle Scholar
  45. 45.
    Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14CrossRefGoogle Scholar
  46. 46.
    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, pp 2929–2932Google Scholar
  47. 47.
    Moustafa N, Turnbull B, Choo K-KR (2018) An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. EEE Internet Things JGoogle Scholar
  48. 48.
    Pamukov ME, Poulkov VK, Shterev VA (2018) Negative selection and neural network based algorithm for intrusion detection in iot. In: 2018 41st international conference on telecommunications and signal processing (TSP). IEEE, pp 1–5Google Scholar
  49. 49.
    Rana S, Hossain S, Shoun HI, Abul Kashem M (2018) An effective lightweight cryptographic algorithm to secure resource-constrained devices. Int J Adv Comput Sci Appl 9(11):267–275Google Scholar
  50. 50.
    Rathore S, Park JH (2018) Semi-supervised learning based distributed attack detection framework for iot. Appl Soft Comput 72:79–89CrossRefGoogle Scholar
  51. 51.
    Sanchez-Pi N, Martí L, Molina JM (2018) Applying voreal for iot intrusion detection. In: International Conference on Hybrid Artificial Intelligence Systems. Springer, pp 363–374Google Scholar
  52. 52.
    Selvarani P, Suresh A, Malarvizhi N (2018) Secure and optimal authentication framework for cloud management using hgapso algorithm. Clust Comput 1–10Google Scholar
  53. 53.
    Shaikh F, Bou-Harb E, Crichigno J, Ghani N (2018) A machine learning model for classifying unsolicited iot devices by observing network telescopes. In: 2018 14th international wireless communications & mobile computing conference (IWCMC). IEEE, pp 938–943Google Scholar
  54. 54.
    Vijayalakshmi J, Robin CRR (2018) An exponent based error detection mechanism against dxdos attack for improving the security in cloud. Clust Comput 1–10Google Scholar
  55. 55.
    Whitter-Jones J (2018) Security review on the internet of things. In: 2018 Third international conference on fog and mobile edge computing (FMEC). IEEE, pp 163–168Google Scholar
  56. 56.
    Xiao L, Wan X, Lu X, Zhang Y, Wu D (2018) Iot security techniques based on machine learning: how do iot devices use ai to enhance security? IEEE Signal Process Mag 35(5):41–49CrossRefGoogle Scholar
  57. 57.
    Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656–1671CrossRefGoogle Scholar
  58. 58.
    Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74Google Scholar
  59. 59.
    Zhang H, Sun G (2002) Feature selection using tabu search method. Pattern Recognit 35(3):701–711CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Maria Habib
    • 1
  • Ibrahim Aljarah
    • 1
  • Hossam Faris
    • 1
  • Seyedali Mirjalili
    • 2
    • 3
    Email author
  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Torrens University AustraliaBrisbaneAustralia
  3. 3.Griffith UniversityBrisbaneAustralia

Personalised recommendations