The week's pick
Random Articles
Reseach Article
AI-Driven Anomaly Detection Model for Intrusion Detection Systems (IDS)
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 6 |
| Year of Publication: 2025 |
| Authors: Sana Ferozuddin, Syed Wajahat Abbas Rizvi |
10.5120/ijca2025925093
|
Sana Ferozuddin, Syed Wajahat Abbas Rizvi . AI-Driven Anomaly Detection Model for Intrusion Detection Systems (IDS). International Journal of Computer Applications. 187, 6 ( May 2025), 51-55. DOI=10.5120/ijca2025925093
Abstract
Intrusion Detection Systems (IDS) are a crucial component of modern cybersecurity frameworks. Traditional rule-based IDS struggle to detect sophisticated cyber threats due to their reliance on static signatures. This paper proposes an AI- driven anomaly detection model for IDS, utilizing machine learning techniques to detect suspicious activities in real time. The model enhances security by identifying previously unseen attack patterns with high accuracy. This study presents a theoretical framework that integrates supervised and unsupervised learning models to improve the efficiency of IDS [6]. The proposed model leverages deep learning techniques, including autoencoders and recurrent neural networks (RNNs), to analyze network traffic and detect anomalies with minimal false positives. Furthermore, it incorporates adaptive learning mechanisms to continuously refine its detection capabilities and mitigate adversarial attacks. The model’s performance is evaluated using benchmark datasets, demonstrating superior accuracy compared to traditional IDS solutions. By addressing the limitations of signature-based detection, the AI-driven approach enhances intrusion detection and response mechanisms in modern cybersecurity infrastructures [21]. This research highlights the potential of AI-driven anomaly detection to revolutionize the field of IDS, providing organizations with a proactive defense against emerging cyber threats.
References
- Ahmed, M., Mahmood, A. N., & Hu, J. (2016). "A survey of network anomaly detection techniques."Journal of Network and Computer Applications, 60, 19-31.
- Bace,R.G., & Mell,P.(2001)."Intrusion detection systems."National Institute of Standards and Technology (NIST).
- Kim, J., Kim, H., & Kim, S. (2020). "A deep learning approach for network intrusion detection using LSTM."IEEE Access, 8, 13546-13556.
- Shone,N.,Ngoc,T.N.,Phai,V.D.,&Shi,Q.(2018)."Adeeplearningapproach to network intrusion detection."IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50.
- Li, Y., Pang, Y., & Wang, H. (2017). "Efficient feature selection for anomaly detection in network traffic."Computer Networks, 119, 65-78.
- Lippmann, R. P., Fried, D. J., Graf, I., & Webster, S. (2000)."Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation."Proceedings of the IEEE Symposiumon Security and Privacy.
- Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). "A detailed analysis of the KDD CUP 99 dataset."IEEE Symposium onComputational Intelligence for Security and Defense Applications(CISDA).
- Hussain, F., Abbas, S., Saeed, S., & Raza, I. (2022). "Hybrid deep learning for anomaly-based network intrusion detection."AppliedSciences, 12(3), 1601.
- García, S., Grill,M., Stiborek,J., &Zunino,A. (2014). "An empirical comparison of botnet detection methods."Computers & Security, 45,100-123.
- McHugh, J. (2001). "Intrusion and intrusion detection."International Journal of Information Security, 1(1), 14-35.
- Gu, G., Zhang, J., & Lee, W. (2008). "Bot Sniffer: Detecting botnetcommandandcontrolchannelsinnetworktraffic."Proceedingsofthe15th Annual Network and Distributed System Security Symposium(NDSS).
- Kumar, G., & Kumar, K. (2015). "A survey on intrusion detection systems and classification techniques."International Journal ofAdvanced Research in Computer Science and Software Engineering,5(6),35-39.
- Sommer,R.,&Paxson,V.(2010)."Outsidetheclosedworld:Onusingmachinelearningfor network intrusion detection."Proceedings of the IEEE Symposium on Security and Privacy.
- Hodo,E., Bellekens,X., Hamilton,A.,& Tachtatzis, C.(2017)."Threatanalysis of IoT networks using artificial neural network intrusion detection system."IEEE International Symposium on Networks, Computers and Communications (ISNCC).
- Sadik, S. (2019). "Anomaly-based intrusion detection systems in IoT using machine learning algorithms."Journal of Information Security and Applications, 47, 377-386.
- Li, C., Zhang, C., & Li, H. (2021). "Real-time anomaly detection for cyber security using deep reinforcement learning."IEEE Transactions on Information Forensics and Security, 16, 1234-1245.
- Liu, H., Lang, B., & Liu, M. (2020). "Deep learning-based anomaly detection for cyber security: A review."IEEE Access, 8, 109378-109394.
- Chawla,N.V.,&Bowyer,K.W.(2002)."SMOTE:SyntheticMinorityOver-samplingTechnique."JournalofArtificialIntelligenceResearch,16,321-357.
- Google, Inc. (2021). "Google AI research on cyber security threat detection." Retrieved from https://ai.google/research/security
- MITLincolnLaboratory.(2018)."Cybersecuritydatasetsandanalysistools." Retrieved from https://www.ll.mit.edu/r-d/cyber-security
- NSL-KDD Dataset. (2022). "Improved version of theKDD Cup 1999dataset for intrusion detection research." Retrieved fromhttps://www.unb.ca/cic/datasets/nsl.html
- CICIDS2017 Dataset. (2017). "Canadian Institute for Cyber security IDS dataset." Retrieved from https://www.unb.ca/cic/datasets/ids-2017.html
- OWASP Foundation.(2022)."Top10web application security risks."Retrieved from https://owasp.org/www-project-top-ten/
Index Terms
Keywords