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@article{191946,
        author = {Janvi Shirke and Vedanth Kokkula and Kalaivani Murugan and Purva Pandere},
        title = {Malware Detection Using Machine Learning.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8555-8560},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191946},
        abstract = {Contemporary cybersecurity faces mounting challenges as malware continues to evolve beyond traditional detection capabilities [1][2]. While signature-based detection methods remain useful for known threats, they consistently fail against zero-day vulnerabilities and polymorphic malware attacks [3]. This research presents a practical approach using machine learning algorithms to automate detection and classic cation of malicious software [4]. Our implementation leverages multiple classifiers —specially Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression—trained on industry-standard datasets including Microsoft Malware Classi cation Dataset, EMBER, and Kaggle malware repositories [4][13]. The methodology encompasses feature extraction, preprocessing, exploratory analysis, model optimization, and performance assessment through key metrics such as accuracy, precision, recall, F1-score, and ROC-AUC analysis [4] [7]. Results indicate that ensemble-based methods, particularly Random Forest, demonstrate superior classification capabilities compared to individual algorithms [6][15], offering practical insights for organizations developing adaptive cybersecurity defense mechanisms capable of recognizing previously unknown threats [4][7].},
        keywords = {Malware detection, Machine learning, Classi cation algorithms, Cybersecurity, Feature extraction, Zero-day attacks, Ensemble methods.},
        month = {January},
        }