AI and Machine Learning in Cybersecurity: Enhancing Threat Detection and Response Systems

Abstract

Traditional cybersecurity systems have major challenges because of the fast 
changeability of cyber threats. The recent changes in Artificial Intelligence (AI) and 
Machine Learning (ML) provide cutting-edge ways to increase detection and response 
mechanisms of threats. This paper examines how AI and ML can be used to enhance 
cybersecurity with respect to enhanced real-time threat detection and anomaly 
detection as well as incident automation. We shall also assess several ML algorithms, 
including supervised and unsupervised learning models, and their efficiency in 
detecting the upcoming cyber threats. We discuss the investigation in several sets of 
data to compare the effectiveness of the models such as the decision trees, neural 
networks, and deep learning algorithms in the practice. Some early evidence suggests 
that the application of AI systems is more effective than traditional practices because 
it seems to have greater accuracy in identifying threats that are completely novel. This 
evidence highlights the possibility of AI and ML to reinvent cybersecurity through the 
provision of quicker and adaptive security procedures. The study finishes with the 
discussion of the future implications of the use of those technologies in present 
cybersecurity frameworks by pointing to the necessity of constant training and 
adapting of the models to the changing threats.