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A wrapper method based on a modified two-step league championship algorithm for detecting botnets in IoT environments

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Abstract

Today, the Internet of Things (IoT) is extending due to a wide range of applications and services. The variety of devices connected to the internet, the discussion of security on these networks is a challenging issue. Security includes diverse aspects such as botnets. Botnets are a set of devices such as smartphones, computers, and others polluted by a program. This program, which is a bot herder, performs many deleterious operations and leads to various anomalies in the network. Identifying botnets due to their unique complexity is one of the main challenges in IoT security. In this paper, we propose a model for identifying botnets in the internet of things. The proposed method is based on selecting features using the modified League Championship Algorithm (LCA) and constructing the model using artificial neural networks. Feature selection speeds up the learning process and increases the resolution of botnets. The proposed method is simulated using MATLAB. The results reveal that the proposed method can make a better detection method than other schemes, and our modified version selects an optimal subset of features. As a result, it is an efficient model.

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References

  1. Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J 4(5):1125–1142

    Article  Google Scholar 

  2. Li S, Da Xu L, Zhao S (2018) 5G internet of things: a survey. J Ind Inf Integr 10:1–9

    Google Scholar 

  3. Alaa M, Zaidan AA, Zaidan BB, Talal M, Kiah MLM (2017) A review of smart home applications based on Internet of Things. J Netw Comput Appl 97:48–65

    Article  Google Scholar 

  4. Vilela PH, Rodrigues JJ, Solic P, Saleem K, Furtado V (2019) Performance evaluation of a Fog-assisted IoT solution for e-Health applications. Futur Gener Comput Syst 97:379–386

    Article  Google Scholar 

  5. Al-Turjman F, Alturjman S (2020) 5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications. Multimed Tools Appl 79(13):8627–8648

    Article  Google Scholar 

  6. Kamalesh MS, Chokkalingam B, Arumugam J, Sengottaiyan G, Subramani S, Shah MA (2021) An intelligent real time pothole detection and warning system for automobile applications based on IoT technology. J Appl Sci Eng 24(1):77–81

    Google Scholar 

  7. Balakrishna S, Thirumaran M (2018) Semantic interoperable traffic management framework for IoT smart city applications. EAI Endorsed Trans Internet Things 4(13):1

    Article  Google Scholar 

  8. Ashouri M, Davidsson P, Spalazzese R (2018) Cloud, edge, or both? Towards decision support for designing IoT applications. In: 5th International Conference on Internet of Things: Systems, Management and Security. IEEE, pp 155–162

  9. Wu D, Shi H, Wang H, Wang R, Fang H (2018) A feature-based learning system for Internet of Things applications. IEEE Internet Things J 6(2):1928–1937

    Article  Google Scholar 

  10. Sethi P, Sarangi SR (2017) Internet of things: architectures, protocols, and applications. J Electr Comput Eng

  11. Ray PP (2018) A survey on Internet of Things architectures. J King Saud University-Comput Inf Sci 30(3):291–319

    Google Scholar 

  12. Conti M, Dehghantanha A, Franke K, Watson S (2018) Internet of Things security and forensics: challenges and opportunities

  13. 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 

  14. Zhang X, Upton O, Beebe NL, Choo KKR (2020) Iot botnet forensics: a comprehensive digital forensic case study on mirai botnet servers. Forens Sci Int: Digit Investig 32:300926

    Google Scholar 

  15. Kolias C, Kambourakis G, Stavrou A, Voas J (2017) DDoS in the IoT: Mirai and other botnets. Computer 50(7):80–84

    Article  Google Scholar 

  16. Prokofiev AO, Smirnova YS, Surov V A (2018, January) A method to detect Internet of Things botnets. In: IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus). IEEE, pp 105–108

  17. Xia H, Li L, Cheng X, Cheng X, Qiu T (2020) Modeling and analysis botnet propagation in social Internet of Things. IEEE Internet Things J

  18. Ji Y, Yao L, Liu S, Yao H, Ye Q, Wang R (2018) The study on the botnet and its prevention policies in the internet of things. In: IEEE 22nd International conference on computer supported cooperative work in design ((CSCWD)). IEEE, pp 837–842

  19. McDermott CD, Isaacs JP, Petrovski AV (2019) Evaluating awareness and perception of botnet activity within consumer internet-of-things (IoT) networks. Informatics 6(1):8

    Article  Google Scholar 

  20. Angrishi K (2017) Turning internet of things (iot) into internet of vulnerabilities (iov): Iot botnets. arXiv preprint arXiv:1702.03681

  21. Stevanovic M, Pedersen JM (2014) An efficient flow-based botnet detection using supervised machine learning. In: International conference on computing, networking and communications (ICNC). IEEE, pp 797–801

  22. Saied A, Overill RE, Radzik T (2016) Detection of known and unknown DDoS attacks using Artificial Neural Networks. Neurocomputing 172:385–393

    Article  Google Scholar 

  23. Wang CY, Ou CL, Zhang YE, Cho FM, Chen PH, Chang JB, Shieh CK (2018) BotCluster: a session-based P2P botnet clustering system on NetFlow. Comput Netw 145:175–189

    Article  Google Scholar 

  24. Khanchi S, Vahdat A, Heywood MI, Zincir-Heywood AN (2018) On botnet detection with genetic programming under streaming data label budgets and class imbalance. Swarm Evol Comput 39:123–140

    Article  Google Scholar 

  25. Cid-Fuentes JÁ, Szabo C, Falkner K (2018) An adaptive framework for the detection of novel botnets. Comput Secur 79:148–161

    Article  Google Scholar 

  26. Yahyazadeh M, Abadi M (2015) BotGrab: a negative reputation system for botnet detection. Comput Electr Eng 41:68–85

    Article  Google Scholar 

  27. Kirubavathi G, Anitha R (2016) Botnet detection via mining of traffic flow characteristics. Comput Electr Eng 50:91–101

    Article  Google Scholar 

  28. Chen CM, Lin HC (2015) Detecting botnet by anomalous traffic. J Inf Secur Appl 21:42–51

    Google Scholar 

  29. Ersson J, Moradian E (2013) Botnet detection with event-driven analysis. Procedia Comput Sci 22:662–671

    Article  Google Scholar 

  30. Yen TF, Oprea A, Onarlioglu K, Leetham T, Robertson W, Juels A, Kirda E (2013) Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks. In: Proceedings of the 29th annual computer security applications conference, pp 199–208

  31. Narang P, Hota C, Sencar HT (2016) Noise-resistant mechanisms for the detection of stealthy peer-to-peer botnets. Comput Commun 96:29–42

    Article  Google Scholar 

  32. Khattak S, Ahmed Z, Syed AA, Khayam SA (2015) BotFlex: a community-driven tool for botnet detection. J Netw Comput Appl 58:144–154

    Article  Google Scholar 

  33. HaddadPajouh H, Dehghantanha A, Khayami R, Choo KKR (2018) A deep recurrent neural network based approach for internet of things malware threat hunting. Futur Gener Comput Syst 85:88–96

    Article  Google Scholar 

  34. Azmoodeh A, Dehghantanha A, Choo KKR (2018) Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans Sustain Comput 4(1):88–95

    Article  Google Scholar 

  35. Alhanahnah M, Lin Q, Yan Q, Zhang N, Chen Z (2018) Efficient signature generation for classifying cross-architecture IoT malware. In: IEEE conference on communications and network security (CNS). IEEE, pp 1–9

  36. Alauthaman M, Aslam N, Zhang L, Alasem R, Hossain MA (2018) A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks. Neural Comput Appl 29(11):991–1004

    Article  Google Scholar 

  37. Alasmary H, Khormali A, Anwar A, Park J, Choi J, Abusnaina A, Mohaisen A (2019) Analyzing and detecting emerging internet of things malware: a graph-based approach. IEEE Internet Things J 6(5):8977–8988

    Article  Google Scholar 

  38. Dovom EM, Azmoodeh A, Dehghantanha A, Newton DE, Parizi RM, Karimipour H (2019) Fuzzy pattern tree for edge malware detection and categorization in IoT. J Syst Architect 97:1–7

    Article  Google Scholar 

  39. Darabian H, Dehghantanha A, Hashemi S, Homayoun S, Choo KKR (2020) An opcode-based technique for polymorphic Internet of Things malware detection. Concurr Comput: Pract Exp 32(6):e5173

    Article  Google Scholar 

  40. Takase H, Kobayashi R, Kato M, Ohmura R (2020) A prototype implementation and evaluation of the malware detection mechanism for IoT devices using the processor information. Int J Inf Secur 19(1):71–81

    Article  Google Scholar 

  41. Nguyen HT, Ngo QD, Le VH (2020) A novel graph-based approach for IoT botnet detection. Int J Inf Secur 19(5):567–577

    Article  Google Scholar 

  42. Asadi M, Jamali MAJ, Parsa S, Majidnezhad V (2020) Detecting botnet by using particle swarm optimization algorithm based on voting system. Futur Gener Comput Syst 107:95–111

    Article  Google Scholar 

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Correspondence to Hamid Barati.

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Shojarazavi, T., Barati, H. & Barati, A. A wrapper method based on a modified two-step league championship algorithm for detecting botnets in IoT environments. Computing 104, 1753–1774 (2022). https://doi.org/10.1007/s00607-022-01070-9

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  • DOI: https://doi.org/10.1007/s00607-022-01070-9

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