Published Online:July 2026
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS010726
DOI:10.71329/IUPJCS/2026.20.3.7-19
Author Name:Shivam Jaiswal, Shyam Patel, Vinod Kumar S, Sahil Patel, Sagar K G and Sunena Rose
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:7-19
The rapid growth of digital technology and Internet services has increased cybersecurity risks. Both individuals and organizations face numerous cyber threats, including phishing emails, malware attacks, and network intrusions. Traditional cybersecurity tools such as firewalls and signature-based intrusion detection systems often fail to detect advanced attacks like zero-day exploits and advanced persistent threats. This review examines more than forty-seven papers related to artificial intelligence (AI)-based cybersecurity threat detection, analyzing different AI methods, evaluating their effectiveness, and highlighting the contributions of existing research. It identifies the challenges, including disconnected security solutions, limited automated response mechanisms, poor explainability, and data privacy concerns. To overcome these issues, the review proposes a unified AI-based cybersecurity framework for intelligent threat detection, automatic response systems, explainable AI (XAI) models, and privacy-preserving technologies.
The rapid growth of digital communication technologies has increased the use of interconnected network systems by both individuals and organizations.