Intrusion Detection Systems In Modern Networks Technologies Challenges And Future Research Directions
DOI:
https://doi.org/10.37547/ijasr-06-05-03Keywords:
Network security, Intrusion Detection Systems (IDS), anomaly detectionAbstract
This paper examines the main types, detection approaches, and architectural solutions of Intrusion Detection Systems (IDS). Based on early research, it analyzes host-based, network-based, and hybrid deployment models, as well as signature-based, anomaly-based, and hybrid detection methods. The paper further presents modern industry-grade platforms widely used today—such as Suricata, Zeek, Wazuh, and AI/ML-based security solutions—in place of earlier academic prototypes developed in the 2010s. In addition, it discusses current challenges, including real-time processing requirements, false positive rates, encrypted traffic analysis, scalability issues, and the integration of artificial intelligence, as well as outlines future research directions in the field.
References
1. Hoque N., Bhuyan M.H., Baishya R.C., Bhattacharyya D.K., Kalita J.K. Network attacks: Taxonomy, tools and systems. Journal of Network and Computer Applications, 2014, Vol. 40, pp. 307–324.
2. Al-Sada, B., Sadighian, A., & Oligeri, G. (2024). MITRE ATT&CK: State of the art and way forward. ACM Computing Surveys, 57(1), 1-37.
3. Syafril, W. I., Arifwidodo, B., & Pranindito, D. (2024, November). Analysis Of Intrusion Prevention System (IPS) On Software Defined Network (SDN) In Preventing Distributed Denial of Service (DDoS) Attacks. In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) (pp. 759-765). IEEE.
4. Vierino, F. T., Wahanani, H. E., & Junaidi, A. (2026). Evaluating Web Application Security Using OWASP Top 10 and NIST SP 800-115. bit-Tech, 8(3), 3754-3764.
5. Sharma, N., & Arora, B. (2025). Machine Learning and Deep Learning Models for Anomaly Intrusion Detection in Networks: A Systematic Review. SN Computer Science, 6(7), 832.
6. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 20.
7. Somasundaram, S., & Abraham, S. (2021). Machine learning based intrusion detection systems: A review. Computers & Security, 103, 102187.
8. The Zeek Project. (2024). Zeek Network Security Monitor Documentation. https://docs.zeek.org/
9. Open Information Security Foundation (OISF). (2024). Suricata User Guide. https://docs.suricata.io/
10. Wazuh, Inc. (2024). Wazuh: Open Source XDR. Open Source SIEM. https://wazuh.com/
11. National Institute of Standards and Technology. (2012). Guide to Intrusion Detection and Prevention Systems (IDPS) (SP 800-94).
12. MITRE. (2024). ATT&CK Framework. https://attack.mitre.org/
13. Wang, Y., et al. (2022). Adversarial attacks on network intrusion detection systems: A survey. IEEE Transactions on Information Forensics and Security, 17, 3451–3466.
14. 14. European Union Agency for Cybersecurity (ENISA). (2023). ENISA Threat Landscape 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nuriddin Safoev, Suhrobjon Bozorov, Sirojiddin Salimov, Shakarov Muhiddin Abdug‘Affor O‘G‘Li
This work is licensed under a Creative Commons Attribution 4.0 International License.
