Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

Review on Machine Learning Based Malware Detection


Affiliations
1 Student, SoCSE, SMVDU, Katra, India
2 Assistant Professor, SoCSE, SMVDU, Katra, India
 

Malware detection using machine learning has gained significant attention in recent years due to the increasing number of malware attacks. With the increasing use of mobile devices, the need for effective malware detection techniques has become even more critical. Machine learning has emerged as a promising approach for detecting malware, as it can learn to identify patterns in large datasets and classify them as either benign or malicious. Previous research in this area has mainly focused on the detection of Android malware using static and dynamic analysis techniques. This review paper examines the efficiency of machine learning for malware identification, with a focus on the latest research in the field. The paper presents an analysis of the various machine learning algorithms used for identification of malware, their strengths and limitations, and the evaluation metrics used for measuring the performance of these methods. Overall, this review paper provides insights into the novelty in machine learning-based malware identification and highlights the need for further research in this field to build more potent and effective techniques for detecting unknown or zero-day attacks.

Keywords

Malware Detection, Machine Learning, Benign, Malicious Files.
User
Notifications
Font Size

  • D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, ‘DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket’, 2014. [Online]. Available: http://dx.doi.org/doi-info-to-be-provided-later

  • J. Li et al., ‘Networked human motion capture system based on quaternion navigation’, in BodyNets International Conference on Body Area Networks, 2017. doi: 10.1145/0000000.0000000.

  • J. Velasco-Mata, V. Gonzalez-Castro, E. F. Fernandez, and E. Alegre, ‘Efficient Detection of Botnet Traffic by Features Selection and Decision Trees’, IEEE Access, vol. 9, pp. 120567–120579, 2021, doi: 10.1109/ACCESS.2021.3108222.

  • N. Martins, J. M. Cruz, T. Cruz, and P. Henriques Abreu, ‘Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review’, IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. 35403–35419, 2020. doi: 10.1109/ACCESS.2020.2974752.

  • E. Odat and Q. M. Yaseen, ‘A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features’, IEEE Access, vol. 11, pp. 15471–15484, 2023, doi: 10.1109/ACCESS.2023.3244656.

  • K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, and M. Xu, ‘A Survey on Machine Learning Techniques for Cyber Security in the Last Decade’, IEEE Access, vol. 8, pp. 222310–222354, 2020, doi: 10.1109/ACCESS.2020.3041951.

  • R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, and S. Venkatraman, ‘Robust Intelligent Malware Detection Using Deep Learning’, IEEE Access, vol. 7, pp. 46717–46738, 2019, doi: 10.1109/ACCESS.2019.2906934.

  • T.-L. Wan et al., ‘Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files’, IEEE Open Journal of the Computer Society, vol. 1, pp. 262–275, Oct. 2020, doi: 10.1109/ojcs.2020.3033974.

  • H. Yang, S. Li, X. Wu, H. Lu, and W. Han, ‘A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning’, IEEE Access, vol. 7, pp. 148853–148860, 2019, doi: 10.1109/ACCESS.2019.2946482.

  • A. Mahindru and A. L. Sangal, ‘SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches’, International Journal of Machine Learning and Cybernetics, vol. 12, no. 5, pp. 1369–1411, May 2021, doi: 10.1007/s13042-020-01238-9.

  • V. Kouliaridis and G. Kambourakis, ‘A comprehensive survey on machine learning techniques for android malware detection’, Information (Switzerland), vol. 12, no. 5, 2021, doi: 10.3390/info12050185.


Abstract Views: 195

PDF Views: 0




  • Review on Machine Learning Based Malware Detection

Abstract Views: 195  |  PDF Views: 0

Authors

Lubna Javaid
Student, SoCSE, SMVDU, Katra, India
Sudesh Kumar
Assistant Professor, SoCSE, SMVDU, Katra, India

Abstract


Malware detection using machine learning has gained significant attention in recent years due to the increasing number of malware attacks. With the increasing use of mobile devices, the need for effective malware detection techniques has become even more critical. Machine learning has emerged as a promising approach for detecting malware, as it can learn to identify patterns in large datasets and classify them as either benign or malicious. Previous research in this area has mainly focused on the detection of Android malware using static and dynamic analysis techniques. This review paper examines the efficiency of machine learning for malware identification, with a focus on the latest research in the field. The paper presents an analysis of the various machine learning algorithms used for identification of malware, their strengths and limitations, and the evaluation metrics used for measuring the performance of these methods. Overall, this review paper provides insights into the novelty in machine learning-based malware identification and highlights the need for further research in this field to build more potent and effective techniques for detecting unknown or zero-day attacks.

Keywords


Malware Detection, Machine Learning, Benign, Malicious Files.

References