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ABSTRACT
In an era where cyber threats continually evolve, the need for robust Intrusion Detection Systems (IDS) has become paramount. This research integrates Machine Learning (ML) into IDS to improve the detection and mitigation of security breaches. It emphasizes the application of ML algorithms for analyzing network activities, enabling autonomous threat identification and response.
The proposed IDS leverages the adaptability of machine learning, allowing it to learn and evolve with emerging attack patterns. This adaptability addresses the limitations of traditional rule-based approaches, providing a more dynamic and responsive defense mechanism.
The research delves into the practical implications of deploying an ML-powered IDS, considering real-world scenarios, challenges, and opportunities. Through extensive experimentation and evaluation, the study assesses the system’s accuracy, efficiency, and scalability. The results showcase the effectiveness of the ML-based IDS in detecting and preventing intrusions, highlighting its potential to bolster cybersecurity in an ever-changing threat landscape.
Accordingly, this research contributes to the ongoing efforts to fortify digital infrastructures against cyber threats. Integrating Machine Learning into the Intrusion Detection System enhances detection capabilities and positions the system as an adaptive against evolving security challenges.
