ItemConference Paper

Data-Driven Malware Detection: Exploring Supervised Machine Learning Approaches

Title
Data-Driven Malware Detection: Exploring Supervised Machine Learning Approaches
Creator
Joseph, Helna; Manjus, Erin; Arun Kokatnoor, Sujatha; Bindu Madavi, K.P.
Description
Malicious software must be detected in order to protect sensitive data and systems in the digital era, as sophisticated malware is posing serious risks to cybersecurity. By examining supervised machine learning approaches with a particular focus on Random Forest, Logistic Regression, and Decision Trees, this research proposes a data-driven approach to malware detection. These algorithms are trained to recognize patterns indicating malware by using labeled datasets containing four types of malwares, Ransomware, Trojan, Virus, and Worm. The performance of these algorithms is comprehensively investigated in the paper, with comparisons made between their accuracy, precision, recall, and F1-score. Based on the experimental results, Random Forest (96% accuracy) performed better in terms of robustness and accuracy of detection than both Logistic Regression (91%) and Decision Trees (84%). Logistic Regression provided faster computation at the expense of less accurate detection. Decision trees, while relatively simple to comprehend, performed moderately and they overfit the data. The studys conclusion highlights the significance of choosing the appropriate model in accordance with particular cyber security requirements, outlining the advantages and disadvantages of every approach as well as their practical applicability in real-time malware detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.465-476
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Decision trees and supervised; Logistic regression; Machine learning; Malware detection; Random forest
Coverage
Joseph H., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Manjus E., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Arun Kokatnoor S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Bindu Madavi K.P., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981964879-5;
Format
online
Language
English
Type
Conference paper

Citation

Joseph, Helna; Manjus, Erin; Arun Kokatnoor, Sujatha; Bindu Madavi, K.P., “Data-Driven Malware Detection: Exploring Supervised Machine Learning Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25541.

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