The Challenge of Adversarial Attacks on AI-Driven Cybersecurity Systems
M. N. V Kiranbabu1,*, A. Jeraldine Viji2, Amit Kumar Chandanan3, Vijay Birchha4, Tushar Kumar Pandey5, Sumit Kumar Sar6
1Associate Professor, Department of CSE, Koneru Lakshmaiah Education Foundation
Vaddeswaram, AP, India
2Professor, Dept. of EEE, Mailam Engineering College, Villupuram, TN, India
3Associate Professor, Department of Computer Science and Engineering, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, (C G), India
4Senior Assistant Professor, School of Computer Science Engineering and Artificial intelligence (SCAI),
VIT-Bhopal University, India
5Assistant Professor (Computer Science), College of Community Science, Central Agricultural University, Tura, Meghalaya, India
6Assistant professor, Department of Computer Science and Engineering, Bhilai Institute of Technology Durg, Chhattisgarh, 491001, India
Emails: mnvkiranbabu@gmail.com; jeraldinevijieee@mailamengg.com; chandanan.amit@ggu.ac.in; vijaybirchha@gmail.com; tusharkumarpandey@gmail.com; sumitsar@gmail.com
Abstract
As AI is deployed increasingly in defensive systems, hostile assaults have increased. AI-driven defensive systems are vulnerable to attacks that exploit flaws. This article examines the approaches used to resist AI-based cybersecurity systems and their effects on security. This paper examines existing literature and case studies to demonstrate how attackers modify AI models. These include avoidance, poisoning, and data-driven assaults. It also considers data breaches, system failures, and unauthorized access if a hostile effort succeeds. The report recommends adversarial training, model testing, and input sanitization to address these issues. It also stresses the need for monitoring and updating AI algorithms to adapt to changing opponent tactics. This paper emphasizes the need to limit hostile strike threats using real-life examples and statistics. To defend AI-driven cybersecurity systems from complex threats, cybersecurity specialists, AI researchers, and policymakers must collaborate across domains. This article provides full guidance for cybersecurity and AI professionals. It describes the complex issues adversarial assaults create and proposes a flexible and robust architecture to safeguard AI-driven cybersecurity systems from emerging threats.
Keywords: Adversarial attacks; AI-driven; cybersecurity systems; challenges; threats; vulnerabilities; defense mechanisms; data confidentiality; interdisciplinary collaboration; resilient framework