Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision trees and ensemble methods such as random forests, are promising implementations that can be performed over large datasets in addition to increasing the accuracy of classification for known and unknown threats. ML can be identified by its ability to enable IDS to learn from historical data and adapt accordingly to new patterns of attacks without heavy domain-specific knowledge, and recently, deep learning methodologies have been incorporated into end-to-end processing of raw data for further improving detection rates. Further, the paper discusses how machine learning has become an essential phase in the evolution of IDS and how its capacity to furnish real-time intrusion detection defeats a particular set of hurdles related to false positives and model interpretability.

Keywords: Intrusion Detection Systems, Malware, Machine Learning, Decision Trees, Cybersecurity

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1. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
2. Othman, S. M., Alsohybe, N. T., Ba-Alwi, F. M., & Zahary, A. T. (2018). Survey on intrusion detection system types. International Journal of Cyber-Security and Digital Forensics, 7(4), 444-463.
3. Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. applied sciences, 9(20), 4396.
4. Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., & Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171, 1251-1260.
5. Yang, L., Moubayed, A., Hamieh, I., & Shami, A. (2019, December). Tree-based intelligent intrusion detection system in internet of vehicles. In 2019 IEEE global communications conference (GLOBECOM) (pp. 1-6). IEEE.
6. Ferrag, M. A., Maglaras, L., Ahmim, A., Derdour, M., & Janicke, H. (2020). Rdtids: Rules and decision tree-based intrusion detection system for internet-of-things networks. Future internet, 12(3), 44.
7. Efe, A., & Abacı, İ. N. (2022). Comparison of the host based intrusion detection systems and network based intrusion detection systems. Celal Bayar University Journal of Science, 18(1), 23-32.
8. Rahul-Vigneswaran, K., Poornachandran, P., & Soman, K. P. (2020). A compendium on network and host based intrusion detection systems. In ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (pp. 23-30). Springer Singapore.
9. Karanfilovska, M., Kochovska, T., Todorov, Z., Cholakoska, A., Jakimovski, G., & Efnusheva, D. (2022). Analysis and modelling of a ML-based NIDS for IoT networks. Procedia Computer Science, 204, 187-195.
10. Satilmiş, H., Akleylek, S., & Tok, Z. Y. (2024). A Systematic Literature Review on Host-Based Intrusion Detection Systems. Ieee Access, 12, 27237-27266.
11. Otoum, Y., & Nayak, A. (2021). As-ids: Anomaly and signature based ids for the internet of things. Journal of Network and Systems Management, 29(3), 23.
12. Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7), 1177.
13. Wang, M., Zheng, K., Yang, Y., & Wang, X. (2020). An explainable machine learning framework for intrusion detection systems. IEEE Access, 8, 73127-73141.
14. Guezzaz, A., Benkirane, S., Azrour, M., & Khurram, S. (2021). A reliable network intrusion detection approach using decision tree with enhanced data quality. Security and Communication Networks, 2021(1), 1230593.
15. Almomani, O., Almaiah, M. A., Alsaaidah, A., Smadi, S., Mohammad, A. H., & Althunibat, A. (2021, July). Machine learning classifiers for network intrusion detection system: comparative study. In 2021 International Conference on Information Technology (ICIT) (pp. 440-445). IEEE.
16. Mahbooba, B., Timilsina, M., Sahal, R., & Serrano, M. (2021). Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model. Complexity, 2021(1), 6634811.
17. Ravipati, R. D., & Abualkibash, M. (2019). Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets-a review paper. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 11.
18. Zhang, C., Jia, D., Wang, L., Wang, W., Liu, F., & Yang, A. (2022). Comparative research on network intrusion detection methods based on machine learning. Computers & Security, 121, 102861.
19. Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188, 107840.
20. Biswas, S. K. (2018). Intrusion detection using machine learning: A comparison study. International Journal of pure and applied mathematics, 118(19), 101-114
21. Rai, M., & Mandoria, H. L. (2019, September). Network Intrusion Detection: A comparative study using state-of-the-art machine learning methods. In 2019 international conference on issues and challenges in intelligent computing techniques (ICICT) (Vol. 1, pp. 1-5). IEEE.
22. Khan, F. A., & Gumaei, A. (2019). A comparative study of machine learning classifiers for network intrusion detection. In Artificial Intelligence and Security: 5th International Conference, ICAIS 2019, New York, NY, USA, July 26-28, 2019, Proceedings, Part II 5 (pp. 75-86). Springer International Publishing.
23. Vitorino, J., Andrade, R., Praça, I., Sousa, O., & Maia, E. (2021, December). A comparative analysis of machine learning techniques for IoT intrusion detection. In International Symposium on Foundations and Practice of Security (pp. 191-207). Cham: Springer International Publishing.
24. Disha, R. A., & Waheed, S. (2021, September). A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset. In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT) (pp. 1-5). IEEE.
25. Aksu, D., Üstebay, S., Aydin, M. A., & Atmaca, T. (2018). Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. In Computer and Information Sciences: 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings 32 (pp. 141-149). Springer International Publishing.

Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision trees and ensemble methods such as random forests, are promising implementations that can be performed over large datasets in addition to increasing the accuracy of classification for known and unknown threats. ML can be identified by its ability to enable IDS to learn from historical data and adapt accordingly to new patterns of attacks without heavy domain-specific knowledge, and recently, deep learning methodologies have been incorporated into end-to-end processing of raw data for further improving detection rates. Further, the paper discusses how machine learning has become an essential phase in the evolution of IDS and how its capacity to furnish real-time intrusion detection defeats a particular set of hurdles related to false positives and model interpretability.

Keywords: Intrusion Detection Systems, Malware, Machine Learning, Decision Trees, Cybersecurity

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
2. Othman, S. M., Alsohybe, N. T., Ba-Alwi, F. M., & Zahary, A. T. (2018). Survey on intrusion detection system types. International Journal of Cyber-Security and Digital Forensics, 7(4), 444-463.
3. Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. applied sciences, 9(20), 4396.
4. Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., & Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171, 1251-1260.
5. Yang, L., Moubayed, A., Hamieh, I., & Shami, A. (2019, December). Tree-based intelligent intrusion detection system in internet of vehicles. In 2019 IEEE global communications conference (GLOBECOM) (pp. 1-6). IEEE.
6. Ferrag, M. A., Maglaras, L., Ahmim, A., Derdour, M., & Janicke, H. (2020). Rdtids: Rules and decision tree-based intrusion detection system for internet-of-things networks. Future internet, 12(3), 44.
7. Efe, A., & Abacı, İ. N. (2022). Comparison of the host based intrusion detection systems and network based intrusion detection systems. Celal Bayar University Journal of Science, 18(1), 23-32.
8. Rahul-Vigneswaran, K., Poornachandran, P., & Soman, K. P. (2020). A compendium on network and host based intrusion detection systems. In ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (pp. 23-30). Springer Singapore.
9. Karanfilovska, M., Kochovska, T., Todorov, Z., Cholakoska, A., Jakimovski, G., & Efnusheva, D. (2022). Analysis and modelling of a ML-based NIDS for IoT networks. Procedia Computer Science, 204, 187-195.
10. Satilmiş, H., Akleylek, S., & Tok, Z. Y. (2024). A Systematic Literature Review on Host-Based Intrusion Detection Systems. Ieee Access, 12, 27237-27266.
11. Otoum, Y., & Nayak, A. (2021). As-ids: Anomaly and signature based ids for the internet of things. Journal of Network and Systems Management, 29(3), 23.
12. Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7), 1177.
13. Wang, M., Zheng, K., Yang, Y., & Wang, X. (2020). An explainable machine learning framework for intrusion detection systems. IEEE Access, 8, 73127-73141.
14. Guezzaz, A., Benkirane, S., Azrour, M., & Khurram, S. (2021). A reliable network intrusion detection approach using decision tree with enhanced data quality. Security and Communication Networks, 2021(1), 1230593.
15. Almomani, O., Almaiah, M. A., Alsaaidah, A., Smadi, S., Mohammad, A. H., & Althunibat, A. (2021, July). Machine learning classifiers for network intrusion detection system: comparative study. In 2021 International Conference on Information Technology (ICIT) (pp. 440-445). IEEE.
16. Mahbooba, B., Timilsina, M., Sahal, R., & Serrano, M. (2021). Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model. Complexity, 2021(1), 6634811.
17. Ravipati, R. D., & Abualkibash, M. (2019). Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets-a review paper. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 11.
18. Zhang, C., Jia, D., Wang, L., Wang, W., Liu, F., & Yang, A. (2022). Comparative research on network intrusion detection methods based on machine learning. Computers & Security, 121, 102861.
19. Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188, 107840.
20. Biswas, S. K. (2018). Intrusion detection using machine learning: A comparison study. International Journal of pure and applied mathematics, 118(19), 101-114
21. Rai, M., & Mandoria, H. L. (2019, September). Network Intrusion Detection: A comparative study using state-of-the-art machine learning methods. In 2019 international conference on issues and challenges in intelligent computing techniques (ICICT) (Vol. 1, pp. 1-5). IEEE.
22. Khan, F. A., & Gumaei, A. (2019). A comparative study of machine learning classifiers for network intrusion detection. In Artificial Intelligence and Security: 5th International Conference, ICAIS 2019, New York, NY, USA, July 26-28, 2019, Proceedings, Part II 5 (pp. 75-86). Springer International Publishing.
23. Vitorino, J., Andrade, R., Praça, I., Sousa, O., & Maia, E. (2021, December). A comparative analysis of machine learning techniques for IoT intrusion detection. In International Symposium on Foundations and Practice of Security (pp. 191-207). Cham: Springer International Publishing.
24. Disha, R. A., & Waheed, S. (2021, September). A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset. In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT) (pp. 1-5). IEEE.
25. Aksu, D., Üstebay, S., Aydin, M. A., & Atmaca, T. (2018). Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. In Computer and Information Sciences: 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings 32 (pp. 141-149). Springer International Publishing.