Artificial Immune Mechanisms for Proactive Threat Detection in Computing Systems

Author(s)	ROHINI MUDLIYAR, Dr. VIRENDRA KUMAR SWARNKAR
Country	India
Abstract	The implementation of the AIS model integrates machine learning techniques, negative selection algorithms, and immune-based pattern recognition in order to enhance the identification of potential threats. In the event that there is a potential breach of security, the system is able to monitor the situation in real time, recognise any suspicious behaviour, and take immediate protective measures. The effectiveness of the security system that is based on AIS is evaluated based on a number of performance parameters, such as the detection accuracy, the false-positive rates, the flexibility, and the response time. Based on the data, it can be concluded that the AIS-based solution provides better detection rates and more flexibility when compared to more traditional security methods. According to the findings of the study, AIS has the capacity to enhance the resilience of the system, reduce the number of false alarms, and help mitigate cyber-attacks in real time. It is possible that in the future, research may investigate methods to enhance AIS-based security models. One example of this would be the incorporation of deep learning and the deployment of these models on a large scale in complex network environments.
Keywords	AIS, NSA, ANN, LSTM/GRU, CLONALG, DCA
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-19
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.57984

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