Publication Date: 2024/02/16
Abstract: Cyberattacks are evolving, and conventional signature-based detection mechanisms will not succeed at detecting such attacks. Sophisticated detection systems that utilize modern data analytics, such as machine learning and artificial intelligence, can identify hidden patterns or behavioral relationships in the large array of cyber-related residuals. This study suggests cyber threat detection research into a comprehensive artificial intelligence framework. The features should have behavior modeling, intelligent correlation, and dynamic detection models. All these difficulties are the challenges to human research efforts as related to new endeavors with multi- source data sets. They also include three different, most optimized algorithms with chances of being free from such production variants that are biased multi-mode sources. With the constant informing of realistic threats, machine learning models have to produce sturdy representations that can transfer knowledge to identify innovative attacks. Transparency and auditability of a model encourage faith in automated decisions. Continual training against adversarial samples and concept drift makes them resilient. End-to-end, multi-layered cyber defense benefits from a variety of sources, including integrated analytics leveraging the full spectrum visibility through orchestration across the network, user, and malware data. The alternative learning paradigms of self-supervision and reinforcement learning provide hope to topics such as high-valued threat intelligence. Finally, human-machine integration, which takes advantage of strengths based on complementary aptitudes, shall chart the next course. Analyst cognition-enhancing algorithms decrease operational workloads. The scope of this study is to promote cyber protection with A.I. evolving beyond traditional limitations.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.10670055
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB674.pdf
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