A Novel Approach to Malware Detection using Machine Learning and Image Processing
Author:
Affiliation:
1. Bayan University, Al-Ayen University, Iraq
2. Al-Ma moon University, Iraq
3. Al Hikma University, Iraq
4. Al-Kitab University, Iraq
5. Baghdad University, Iraq
6. Taibah University, Saudi Arabia
7. Al-Farahidi University, Afghanistan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3660853.3660931
Reference31 articles.
1. A Study on Malware and Malware Detection Techniques
2. A. Ghaleb F.; Saeed F.; Al-Sarem M.; Ali Saleh Al-rimy B.; Boulila W.; Eljialy A.E.M.; Aloufi K.; Alazab M. Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET. Electronics 2020 9 1411. https://doi.org/10.3390/electronics9091411
3. Chen, Y., Ding, Z., & Wagner, D. (2023). Continuous learning for android malware detection. In 32nd USENIX Security Symposium (USENIX Security 23) (pp. 1127-1144).
4. Security importance assessment for system objects and malware detection
5. A Comprehensive Review on Malware Detection Approaches
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