Artificial Intelligence Powered Malware Detection and Classification using Machine Learning Algorithms
Abstract
The rapid growth of digital technologies and internet-connected systems has significantly increased the threat of cyberattacks, especially malware infections. Traditional malware detection techniques, such as signature-based methods, are no longer sufficient to identify sophisticated and evolving malware variants. Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged as powerful solutions for improving malware detection and classification by enabling systems to identify malicious patterns, learn from large datasets, and detect unknown threats in real time. This study explores the application of AI-powered malware detection and classification using machine learning algorithms. The research focuses on analyzing static and dynamic malware features and evaluating the performance of machine learning algorithms such as Decision Trees, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and Neural Networks in detecting malware activities. The study also highlights the importance of feature extraction, dataset preprocessing, model training, and performance evaluation metrics including accuracy, precision, recall, and F1-score. Furthermore, the research discusses challenges such as dataset imbalance, adversarial attacks, and computational complexity. The proposed AI-based framework demonstrates how intelligent systems can improve cybersecurity defenses by providing faster, scalable, and more accurate malware detection and classification mechanisms. The findings indicate that machine learning-based malware detection systems outperform traditional approaches in identifying both known and unknown malware threats effectively
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 3 No. 2 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
3230-3239 |
Published |
March 10, 2018 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Pavan Srikanth Patchamatla (%2018). Artificial Intelligence Powered Malware Detection and Classification using Machine Learning Algorithms. International Journal of Emerging Trends in Engineering and Management Research , Vol. 3 No. 2 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 3230-3239. https://doi.org/10.15662/ijetemr.2018.0302001 |
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