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ACNN-BOT: An Ant Colony Inspired Feature Selection Approach for ANN Based Botnet Detection

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Abstract

With the growth and development of Internet and wireless communication, Internet-of-Things (IoT) has become a prominent technology for smart devices. In general, IoT systems are treated as badly secured because of always on nature of connectivity and high complexity of distributed computing. Because of this IoT systems have attracted different types of malicious attacks. Botnet is also one of the major types of malicious attacks of IoT systems. Early detection of IoT-Botnet can help to secure the network. In recent years, Machine Learning and Deep Learning have become popular tools for Botnet detection. The performance of these models mainly depends on the available features in the dataset. Selection of the most pertinent set of features may play an important role in order to improve the performance of Machine Learning and Deep Learning based detection models. In this paper, we proposed a novel feature selection approach inspired by Ant Colony optimization algorithm followed by Artificial Neural Network based IoT-Botnet detection. The proposed model has performed significantly well with an accuracy of 99.68% as well as in terms of precision and recall metrics. The proposed model also performed well in terms of feature selection with the improvement in the accuracy of 5% compared to complete feature set.

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Data Availability

Dataset used in this research work is freely available on the interenet and proper citation has been given for the dataset.

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References

  1. Mahoney, M. S. (1988). The history of computing in the history of technology. Annals of the History of Computing, 10(2), 113–125. https://doi.org/10.1109/MAHC.1988.10011

    Article  MATH  Google Scholar 

  2. Kim Ann Zimmermann, https://www.livescience.com/20718-computer-history.html.

  3. Bahukhandi, S., & Rana, S. (2016). S, Introduction & History of Computer Viruses. Int. J. Sci. Eng. Res.,7(12), 44–47. https://www.ijser.org/onlineResearchPaperViewer.aspx?Introduction-History-of-Computer-Viruses.pdf. [Online]. Available:

  4. Avira, https://www.avira.com/en/blog/malware-threat-report-q2-2020-statistics-and-trends.

  5. UK Web Host Review, https://www.ukwebhostreview.com/cybersecurity-statistics/.

  6. Joshi, C., & Bharti, V., & Ranjan, R. (2020). Botnet detection using machine learning algorithms. In Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. https://doi.org/10.1007/978-981-15-7533-4.

  7. Security Intelligence, https://securityintelligence.com/posts/internet-of-threats-iot-botnets-network-attacks/.

  8. Kabir, M. M., Shahjahan, M., & Murase, K. (2012). A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications, 39(3), 3747–3763. https://doi.org/10.1016/j.eswa.2011.09.073

    Article  Google Scholar 

  9. Jimenez, F., Martinez, C., Marzano, E., Palma, J. T., Sanchez, G., & Sciavicco, G. (2019). Multiobjective evolutionary feature selection for fuzzy classification. IEEE Transactions on Fuzzy Systems, 27(5), 1085–1099. https://doi.org/10.1109/TFUZZ.2019.2892363

    Article  Google Scholar 

  10. Halim, Z., et al. (2021). An effective genetic algorithm-based feature selection method for intrusion detection systems. Computers and Security, 110, 102448. https://doi.org/10.1016/j.cose.2021.102448

    Article  Google Scholar 

  11. Wang, P., Lin, H. T., Wang, T. C., et al. (2011). A new approach for solving the IP traceback problem for Web services. International Journal on Advances in Information Sciences and Service Sciences, 3(2), 46–59.

    Article  MathSciNet  Google Scholar 

  12. Wang, P., & Wang, Z. X. (2010). Taiwan Internet Conference (p. 2010). National University of Tainan.

    Google Scholar 

  13. Wang, P., Wang, R. X. et al. (2018) The reconstruction and analysis of the infection way of botnet. The 2010 Taiwan Internet Conference, National University of Tainan. TANET.

  14. Wang, P., Wang, G. F. et al. (2010) The attack path analysis model of Botnet. In The 20th information security conference (CISC2010), the national traffic.

  15. Yang, Z. M., Li, Q., & Liu, J. R. (2015). The research of the threat intelligence sharing and utilization Oriented to the attack tracking. The Information Security Research, 1(1), 31–36.

    Google Scholar 

  16. Wang, X. L., Yang, Q. H., & Jin, X. (2016). Periodic communication detection algorithm of botnet based on quantum computing. Chinese Journal of Quantum Electronics, 33(2), 182–187.

    Google Scholar 

  17. Singh, A., Ranjan, R. K., & Tiwari, A. (2021). Credit card fraud detection under extreme imbalanced data: A comparative study of data-level algorithms. Journal of Experimental Theoretical Artificial Intelligence. https://doi.org/10.1080/0952813X.2021.1907795

    Article  Google Scholar 

  18. Mishra, A. M., Harnal, S., Mohiuddin, K., Gautam, V., Nasr, O. A., Goyal, N., Alwetaishi, M., & Singh, A. (2022). A deep learning-based novel approach for weed growth estimation. Intelligent Automation and Soft Computing, 31(2), 1157–1173.

    Article  Google Scholar 

  19. Stevanovic, M., Pedersen J.M. (2013) Machine learning for identifying botnet network traffic Machine learning for identifying botnet network traffic (Technical report), vol. 17.

  20. Benyahia, S., Meftah, B., & Lézoray, O. (2022). Multi-features extraction based on deep learning for skin lesion classification. Tissue and Cell, 74, 101701. https://doi.org/10.1016/j.tice.2021.101701

    Article  Google Scholar 

  21. Rehman, M. U., Akhtar, S., Zakwan, M., & Mahmood, M. H. (2022). Novel architecture with selected feature vector for effective classification of mitotic and non-mitotic cells in breast cancer histology images. Biomedical Signal Processing and Control, 71, 103212. https://doi.org/10.1016/j.bspc.2021.103212

    Article  Google Scholar 

  22. Rehman, M. U., Ryu, J., Nizami, I. F., & Chong, K. T. (2023). RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames. Computers in Biology and Medicine, 152, 106426. https://doi.org/10.1016/j.compbiomed.2022.106426

    Article  Google Scholar 

  23. Tyagi, A., & Mehra, R. (2020). An optimized CNN based intelligent prognostics model for disease prediction and classification from Dermoscopy images. Multimedia and Tools Applications, 79(35), 26817–26835.

    Article  Google Scholar 

  24. Kumari, P., Jain, P. K., & Pamula, R. (2018). An efficient use of ensemble methods to predict students academic performance. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT). (pp. 1-6). IEEE.

  25. Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Generation Computer Systems, 100, 779–796. https://doi.org/10.1016/j.future.2019.05.041

    Article  Google Scholar 

  26. Joshi, C., Bharti, V., & Ranjan, R. K. (2020). Analysis of feature selection methods for p2p botnet detection. In Advances in Computing and Data Sciences: 4th International Conference, ICACDS 2020, Valletta, Malta, April 24–25, 2020, Revised Selected Papers 4 (pp. 272–282). Springer Singapore.

  27. Shabnam, Aditya, K.S., Chirag, J. (2019). Peer-To-Peer Botnet: Analysis of Botnet detection techniques using machine learning algorithms. International Journal of Electrical, Electronics and Data Communication (IJEEDC), 7(8), pp. 19-25

  28. Zhao, D., Traore, I., Sayed, B., Lu, W., Saad, S., Ghorbani, A., & Garant, D. (2013). Botnet detection based on traffic behavior analysis and flow intervals. Computers and Security, 39(PARTA), 2–16. https://doi.org/10.1016/j.cose.2013.04.007

    Article  Google Scholar 

  29. 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 2018. ICSP, 118-122. https://doi.org/10.1109/ICICSP.2018.8549713

  30. Strayer, W. T., Walsh, R., Livadas, C., Lapsley D. (2006) Detecting botnets with tight command and control. In Proceedings of - Confernce on Local Computing Networks. LCN, pp. 195-202, , https://doi.org/10.1109/LCN.2006.322100

  31. Xing, Y., Shu, H., Zhao, H., Li, D., & Guo, L. (2021). Survey on Botnet detection techniques: Classification, methods, and evaluation. Mathematical Problems in Engineering, 2021, 6640499. https://doi.org/10.1155/2021/6640499

    Article  Google Scholar 

  32. Joshi, C., Ranjan, R. K., & Bharti, V. (2021). ANN based Multi-Class classification of P2P Botnet. International Journal of Computing and Digital System, 1319–1325.

  33. Conti, M., Dehghantanha, A., Franke, K., & Watson, S. (2018). Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.07.060

    Article  Google Scholar 

  34. Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2017.04.002

    Article  Google Scholar 

  35. Bertino, E. (2016). Data security and privacy in the IoT. In Advances in Database Technology - EDBT. (Vol. 2016-March, pp. 1-3). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2016.02

  36. Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other Botnets. Computer, 50(7), 80–84. https://doi.org/10.1109/MC.2017.201

    Article  Google Scholar 

  37. Grizzard, J. B., et al. (2020). Cyberattacks detection in IoT-based smart city applications using machine learning techniques. International Journal of Environmental Research and Public Health, 17(24), 1–21. https://doi.org/10.3390/ijerph17249347

    Article  Google Scholar 

  38. Tsimenidis, S., Lagkas, T., & Rantos, K. (2022). Deep learning in IoT intrusion detection. Journal of Network and Systems Management. https://doi.org/10.1007/s10922-021-09621-9

    Article  Google Scholar 

  39. Popoola, S. I., Adebisi, B., Hammoudeh, M., Gui, G., & Gacanin, H. (2021). Hybrid deep learning for Botnet attack detection in the Internet-of-Things networks. IEEE Internet Things Journal, 8(6), 4944–4956. https://doi.org/10.1109/JIOT.2020.3034156

    Article  Google Scholar 

  40. Joshi C., Bharti V., Ranjan R.K. (2020) Analysis of feature selection methods for P2P Botnet detection. In: Singh M., Gupta P., Tyagi V., Flusser J., Ören T., Valentino G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_25.

  41. Khushaba, R. N., Kodagoda, S., Lal, S., & Dissanayake, G. (2010). Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Transactions on Biomedical Engineering, 58(1), 121–131.

    Article  Google Scholar 

  42. García, S., Grill, M., Stiborek, J., & Zunino, A. (2014). An empirical comparison of botnet detection methods. Computers and Security, 45, 100–123. https://doi.org/10.1016/j.cose.2014.05.011

    Article  Google Scholar 

  43. Joshi, C., Ranjan, R. K., & Bharti, V. (2021). A Fuzzy Logic based feature engineering approach for Botnet detection using ANN. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.06.018

    Article  Google Scholar 

  44. Kumar, B., Ranjan, R. K., & Husain, A. (2021). A multi-objective enhanced fruit fly optimization (MO-EFOA) framework for Despeckling SAR images using DTCWT based local adaptive thresholding. International Journal of Remote Sensing, 42(14), 5493–5514. https://doi.org/10.1080/01431161.2021.1921875

    Article  Google Scholar 

  45. Moodi, M., Ghazvini, M., Moodi, H., & Ghavami, B. (2020). A smart adaptive particle swarm optimization-support vector machine: android botnet detection application. Journal of Supercomputing, 76(12), 9854–9881. https://doi.org/10.1007/s11227-020-03233-x

    Article  Google Scholar 

  46. Habib, M., Aljarah, I., Faris, H., & Mirjalili, S. (2020). Multi-objective particle swarm optimization for botnet detection in Internet of Things. Evolutionary Machine Learning Techniques: Algorithms and Applications. https://doi.org/10.1007/978-981-32-9990-0_10

    Article  Google Scholar 

  47. Wang, P., Lin, H.T., Wang, T.S. (2011) A revised ant colony optimization scheme for discovering attack paths of botnet. In Proceedings of International Conference on Parallel Distribution System - ICPADS, pp. 918-923, https://doi.org/10.1109/ICPADS.2011.11

  48. Saraç, E., & Özel, S. A. (2014). An ant colony optimization based feature selection for web page classification. The Scientific World Journal, 2014(5), 35–40. https://doi.org/10.1155/2014/649260

    Article  Google Scholar 

  49. Almomani, O. (2020). A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry (Basel), 12(6), 1–20. https://doi.org/10.3390/sym12061046

    Article  Google Scholar 

  50. 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 Computing, 17(3), 12–22. https://doi.org/10.1109/MPRV.2018.03367731

    Article  Google Scholar 

  51. Alaiz-Moreton, H., Aveleira-Mata, J., Ondicol-Garcia, J., Muñoz-Castañeda, A. L., García, I., & Benavides, C. (2019). Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity, 2019, 6516253.

    Article  Google Scholar 

  52. Doshi, R., Apthorpe, N., and Feamster, N. (2018). Machine learning DDoS detection for consumer internet of things devices. In Proceedings of - 2018 IEEE Security and Privacy Workshops (SPW) 2018 no. Ml, pp. 29-35, 2018, https://doi.org/10.1109/SPW.2018.00013.

  53. Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017). Malware traffic classification using Convolutional neural network for representation learning. International Conference on Information Networking. https://doi.org/10.1109/ICOIN.2017.7899588

    Article  Google Scholar 

  54. Asadi, M., Jabraeil Jamali, M. A., Parsa, S., & Majidnezhad, V. (2020). Detecting botnet by using particle swarm optimization algorithm based on voting system. Future Generation Computer Systems, 107, 95–111.

    Article  Google Scholar 

  55. Huseynov, K., Kim, K., & Yoo, P. D. (2014). In Semi-supervised Botnet Detection Using Ant Colony Clustering, 31th Symp (p. 7) Cryptography and Information Security (SCIS).

  56. Sebastian G., Agustin P., & Maria J.E. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4743746

  57. Dorigo, M., Maniezzo, V., Colorni, A. (1991). Positive feedback as a search strategy , Technical report, No. 91-016, Department of Electronics, Milan Polytechnic Institute, June .

  58. Wang, P., Lin, H. T., Wang, T. C., & Kuo, P. T. (2011). A new approach for solving the IP traceback problem for Web services. International Journal on Advances in Information Sciences and Service Sciences, 3(2), 46–59.

    Article  Google Scholar 

  59. Martin, J., & Crowley, J.L. (1995). Experimental comparison of correlation techniques. International Conference on Intelligent Autonomous Systems.

  60. Guerra-Manzanares, A., Medina-Galindo, J., Bahsi, H., and Nõmm, S. (2020). MedBIoT: Generation of an IoT botnet dataset in a medium-sized IoT network. In ICISSP 2020 - Proceedings of 6th International Conference on Information System of Security and Privacy. no. March, pp. 207-218, , https://doi.org/10.5220/0009187802070218

  61. Vanitha, S., & Balasubramanie, P. (2023). Improved ant colony optimization and machine learning based ensemble intrusion detection model. Intelligent Automation and Soft Computing, 36(1), 849–864. https://doi.org/10.32604/iasc.2023.032324

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, DIT University, Dehradun, Uttarakhand, India

    Chirag Joshi

  2. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Ranjeet K. Ranjan

  3. Department of Computer Science and Engineering, Maharishi Markandeshwar University (Deemed to be University), Mullana, Haryana, India

    Vishal Bharti

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Joshi, C., Ranjan, R.K. & Bharti, V. ACNN-BOT: An Ant Colony Inspired Feature Selection Approach for ANN Based Botnet Detection. Wireless Pers Commun 132, 1999–2021 (2023). https://doi.org/10.1007/s11277-023-10695-8

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