AI-Powered Intrusion Detection Systems: The Future of Cybersecurity in a Connected World

Abstract

As cyberattacks have become more advanced and more connected devices enter the market, legacy 
intrusion detection systems (IDS) will have difficulties in adapting to the new dynamic in cybersecurity 
risks. The use of advanced methods created by attackers makes it essential to find adaptive, intelligent 
solutions. However, given the use of machine learning (ML) and deep learning (DL) algorithms, AI
powered Intrusion Detection Systems (IDS) are about to become the next stage in the realm of 
cybersecurity. The systems also offer network traffic and system behavior analysis that detects known 
and unknown threats with real-time and accurate results. The present research set out to discuss how AI 
and ML are to be applied in IDS and what role they play in improving the performance, scalability, and 
precise nature of identification. Some of the AI and ML algorithms that the study evaluates are decision 
trees, support vectors machine, and neural networks, sending their efficiency against the classical 
approach. The findings are that the AI-enabled IDS considerably extend more results than traditional 
systems in terms of identifying attacks due to a higher detection rate and false positive decreases. There 
are, however, a number of challenges especially regarding its interpretability, in large networks, and 
how to make sure the effect of the required policy is minimal to the system performance. These facts 
also reveal the necessity of future research to overcome these limitations and make AI ingrain more 
into the cybersecurity structure.