AI-Based Malware Detection and Prevention in Web Application Ecosystems
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-122Keywords:
AI-Based Malware Detection, Cyber security, Machine Learning, Web Application Security, Anomaly Detection, Deep Learning, Hybrid Security Models, NLP In Cyber securityAbstract
The rapid evolution of web applications has made them a primary target for cybercriminals who exploit vulnerabilities to deploy malware. Traditional security mechanisms, such as signature-based detection, have proven inadequate due to their inability to detect sophisticated and zero-day malware threats. This study investigates the potential of artificial intelligence (AI)-based malware detection and prevention techniques to enhance security within web application ecosystems. Machine learning (ML) and deep learning (DL) algorithms have demonstrated superior efficacy in identifying and mitigating evolving threats. This paper provides a comprehensive analysis of AI-driven approaches, their effectiveness, and challenges in implementation. Various AI techniques, including supervised and unsupervised learning, anomaly detection, and reinforcement learning, are explored. Additionally, hybrid models combining heuristic and behaviour-based methods with AI are examined for their effectiveness. This study also evaluates the role of natural language processing (NLP) in analyzing malicious code patterns and its integration with AI models. The methodology involves dataset preparation, feature extraction, model training, and real-time testing using simulated attacks. Results demonstrate the superiority of AI-based approaches in malware detection over traditional methods, showcasing increased accuracy, reduced false positives, and enhanced real-time threat mitigation capabilities. Challenges such as adversarial attacks, computational overhead, and ethical concerns are discussed, along with potential future directions for improving AI-driven security solutions
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