MALWARE DETECTION USING ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE RESEARCH STUDY

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RAGHUPATHYRAJA M; SHEEBA MARY D; Dr.N.Saranya, Assistant Professor and Head

MALWARE DETECTION USING ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE RESEARCH STUDY

Authors: RAGHUPATHYRAJA M, SHEEBA MARY D, Dr.N.Saranya, Assistant Professor and Head

Unique Paper ID: 176301
Volume: 11
Issue: 11
PageNo: 6990-6995

Keywords: No Keywords Found

Abstract:
The ever-growing sophistication of malware attacks presents a significant challenge to cybersecurity. Traditional signature-based detection methods, which rely on predefined patterns, are no longer sufficient in combating modern malware, including polymorphic, metamorphic, and zero-day threats. These advanced malware types continuously modify their structure to evade detection, rendering static rule-based security measures ineffective. To address this challenge, Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a transformative approach for proactive threat detection. AI-driven malware detection leverages vast datasets and advanced algorithms to recognize malicious patterns based on both static and dynamic characteristics. Unlike traditional security mechanisms that require frequent manual updates, AI models adapt and learn from evolving cyber threats, making them highly effective against new malware variants. By analyzing file attributes, behavioral patterns, network activity, and system interactions, AI models can detect suspicious anomalies that indicate malware presence. Supervised learning techniques such as Decision Trees, Support Vector Machines (SVMs), and Random Forests are commonly used for feature-based classification, while deep learning architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs) offer higher precision in sequence and pattern recognition.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6990-6995

MALWARE DETECTION USING ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE RESEARCH STUDY

Available:https://ijirt.org/article?manuscript=176301

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