AUTHORS: Worawat Choensawat and Komal Narang
ABSTRACT: This research presents a model for malware detection on mobile operating system based on machine learning technique. The objective is to reduce the risk of installing harmful application when the user did not update the anti-virus program in time. The proposed model is different to other anti-virus is that most of anti-virus software used virus signature to identify malware. However, the virus signature-based detection approach requires frequent updates of the virus signature dictionary. The signature-based approaches are not effective against new, unknown viruses while the proposed model based on machine learning can detect new malware even some parts of the code have been modified.
Keywords: Antivirus, Android, Feature extraction, Term Frequency-Inverse-Document Frequency (TF-IDF), Principal Component Analysis (PCA)
|MLA||Sachdeva, Shefali, Romuald Jolivot, and Worawat Choensawat. "Android Malware Classification based on Mobile Security Framework." IAENG International Journal of Computer Science 45.4 (2018).|
|APA||Sachdeva, S., Jolivot, R., & Choensawat, W. (2018). Android Malware Classification based on Mobile Security Framework. IAENG International Journal of Computer Science, 45(4).|
|ISO 690||SACHDEVA, Shefali; JOLIVOT, Romuald; CHOENSAWAT, Worawat. Android Malware Classification based on Mobile Security Framework. IAENG International Journal of Computer Science, 2018, 45.4.|