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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.
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  • Review on Machine Learning Based Malware Detection

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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