• T.NavyaDeepthi
• Annam Purnima
• Borra Chinmayi
• Gunji Charmila
• Nagireddy Sireesha

The proliferation of Android malicious applications dangerously injure users information, property, and privacy. Aiming at the problem that the characteristics of malware dynamic analysis and detection aren’t excellent, and the detection efficiency and classifier performance are insufficient, this paper proposes a multi-dimensional feature fusion malicious application detection method based on Logistic Regression .The method provided non-invasively extract the framework layer Application Programming Interface(API) call information of the Android application, apply the Logistic Regression to train the N-gram modelled API call sequence, fuse the obtained probability feature with the basic statistical feature, The experimental results show that the method effectively improves the accuracy of Android malicious application detection and decreases the time expense of it.

Logistic Regression,AndroidMalware,Detection,Programming Interface, N-gram Modelling, API call

"ANDROID MALWARE DETECTION MODEL USING LOGISTIC REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.306-313, October-2024, Available :http://www.jetir.org/papers/JETIRGN06035.pdf

"ANDROID MALWARE DETECTION MODEL USING LOGISTIC REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp306-313, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06035.pdf