Publication Type : Conference Paper
Publisher : Machine Learning and Metaheuristics Algorithms, and Applications, Singapore, 2021.
Source : Machine Learning and Metaheuristics Algorithms, and Applications, Springer Singapore, Singapore (2021)
Url : https://link.springer.com/chapter/10.1007/978-981-16-0419-5_18
ISBN : 9789811604195
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2021
Abstract : Obfuscation techniques are used by malware authors to conceal malicious code and surpass the antivirus scanning. Machine Learning techniques especially deep learning techniques are strong enough to identify obfuscated malware samples. Performance of deep learning model on obfuscated malware detection is compared with conventional machine learning models like Random Forest (RF), Classification and Regression Trees (CART) and K Nearest Neighbour (KNN). Both Static (hardware and permission) and dynamic features (system calls) are considered for evaluating the performance. The models are evaluated using metrics which are precision, recall, F1-score and accuracy. Obfuscation transformation attribution is also addressed in this work using association rule mining. Random forest produced best outcome with F1-Score of 0.99 with benign samples, 0.95 with malware and 0.94 with obfuscated malware with system calls as features. Deep learning network with feed forward architecture is capable of identifying benign, malware, obfuscated malware samples with F1-Score of 0.99, 0.96 and 0.97 respectively.
Cite this Research Publication : K. A. Dhanya, Dheesha, O. K., Dr. Gireesh K. T., and Vinod, P., “Detection of Obfuscated Mobile Malware with Machine Learning and Deep Learning Models”, in Machine Learning and Metaheuristics Algorithms, and Applications, Singapore, 2021.