Analyzing AI Approaches for Enhancing Intrusion Detection Systems

Cybersecurity is important for the growing digital world, to protect data, systems, network and digital component from the cyberattacks. Many technologies have been implemented for securing the data, but the mechanisms of preventing the attacks before it happens through the Intrusion Detection System (IDS) is booming using the modern technologies. The objective is to shed light on rapidly growing AI technologies for IDS. By studying their respective strengths, limitations and offering insights into their applicability and effectiveness in securing modern digital environments against evolving cyberattacks. The method used here is the comparative analysis of the two AI technologies, Machine Learning and Deep Learning for IDS. It starts with analysis of few parameters like working mechanism of both technologies, various algorithms used for IDS, different datasets, accuracy, precision used for IDS, their advantages and disadvantages. The comparative analysis identifies the best technology well suited for IDS. turned out that both technologies have their advantage, disadvantage and its limitations of implementing with few datasets. The comparative analysis shows results that either of the two technologies have their limitations and could not be well suited as a standalone technology, for the future attacks. The finding of the analysis has proposed an idea of an novel hybrid model which integrates the Machine learning as well Deep learning Algorithms to build an new hybrid model which has the capacity to identify all types of attack, whether it is an signature or anomaly based intrusion and is capable of finding, handling the new type of attacks.