Adversarial Machine Learning Approaches for Strengthening Cybersecurity in Intrusion Detection Systems
Rahul Gandhi · Journal for Research in Applied Sciences and Biotechnology · 2023
In this study, adversarial machine learning to enhance IDS’s capability to counterattack sophisticated cyberattacks employed in the investigation. This paper describes challenges in practice of adversarial techniques, performance measurement and ethical issues. In the research proposal, the authors describe the comprehensive and multi-level method of detecting artifacts, building complex models, and gathering data. Researchers stressed important conclusions regarding aggressiveness of privacy-preserving methods, the need for developing new performance metrics, and the tension between robust model and detection performance. The research assists in developing IDS that are both efficient and formally correct in various contexts of a network.