Advances and Challenges of Machine Learning in Intrusion Detection Systems

In our connected world today, keeping computer networks safe from unwanted visitors and harmful actions is very important. Intrusion Detection Systems, or IDS, act like security cameras for networks, watching for strange activities and warning about possible dangers. Old-style IDS use fixed rules to find known problems, but they often miss new kinds of attacks. This is where machine learning helps. Machine learning is a smart way for computers to learn from information and get better at tasks on their own. In this review, we look at how machine learning makes IDS stronger and more flexible. We explain simple ideas, like the main types of machine learning used, such as ones that learn with examples and others that find unusual patterns without help. We also talk about big issues, like dealing with tons of data or handling sneaky tricks from attackers. From recent papers, we share good steps forward, like using deeper learning for spot-on results, and tough spots, such as too many wrong alerts or needing strong computers. At the end, we offer ideas for fixes, like mixing different learning ways or making better data sets. This paper gives an easy overview for anyone interested and points to ways to make online safety better in places like hospitals or smart homes. Everything comes from studies in English.

Keywords:

Machine Learning , Intrusion Detection , Cybersecurity , Advances , Challenges