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. 

              The research processes are as follows: (1) achieving of both malicious and benign codes on android operating system, (2) Extracting features based on the distribution of n-grams frequency and TF Inverse Document Frequency (TFIDF) where the parameter n = 3 is used, and (3) constructing a model for classification the malicious codes using the extracted features for both malicious and benign codes. In the experiment, 304 malicious codes and 553 benign codes were using to construct the model. The experiment shows that the model achieved more than 85.52% accuracy. For the sensitivity and specificity, the model achieved 90.52% and 71.26%, respectively.

 

Keywords: Antivirus, Android, Feature extraction, Term Frequency-Inverse-Document Frequency (TF-IDF), Principal Component Analysis (PCA)

LINK: http://mit.itu.bu.ac.th/publications/Malicious_Code.pdf

REFERENCES: 

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.