Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
- Title
- Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
- Creator
- Ravindra Babu S.; Leisha R.; Medows K.J.; Sreekumar K.; Girish G.P.
- Description
- Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-874 LNNS, pp. 277-286.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Binomial logistic regression; KNN; Malware; Random Forest; XGB classifier
- Coverage
- Ravindra Babu S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Leisha R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Medows K.J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Sreekumar K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Girish G.P., Department of Finance, ICFAI Business School, IFHE University (a Deemed to-be-University under Sec 3 of UGC Act 1956), Hyderabad, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303150886-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Ravindra Babu S.; Leisha R.; Medows K.J.; Sreekumar K.; Girish G.P., “Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 29, 2025, https://archives.christuniversity.in/items/show/18988.