A Comprehensively Investigation of AI-Powered Intrusion Detection Systems: Improving Precision and Decreased False Positives in Contemporary Cybersecurity Systems
Rajesh Jagadeesan Ravikumar, Charulatha Umashankar · 2024
The sophistication and frequency of cyberattacks have prompted the development of more advanced intrusion detection systems (IDS) across the landscape of cybersecurity, which is constantly evolving. This study presents an in-depth assessment into artificial intelligence (AI)-powered intrusion detection systems, with a particular emphasis on the capacity of these systems to improve detection precision and reduce the number of false positives in modern cybersecurity contexts. Traditional intrusion detection systems frequently have difficulty keeping up with the ever-changing nature of threats, which results in a considerable amount of false alarms that place a burden on security teams and may even lead to possible vulnerabilities. In this paper, a number of different machine learning and deep learning algorithms that are used in AI-powered intrusion detection systems are analyzed and compared with regard to how well they recognize both known and new threats. By analyzing the performance of these systems across a variety of datasets and network contexts, the research sheds light on the delicate balance that must be maintained between the precision of detection and the reduction of false positives. In addition, the research investigates the incorporation of artificial intelligence (AI) into the existing cybersecurity infrastructure, analyzing the difficulties and possibilities that are brought about by this method. Through the use of experimental analysis and case studies, the purpose of this research is to determine the most effective artificial intelligence strategies that provide the optimum balance between high detection rates and low false-positive rates. The results of this research make a contribution to the continued development of intrusion detection systems that are more dependable and effective, which ultimately results in an improvement in the overall security posture of contemporary digital environments.