Title: Behavioural Analysis of Android Malware using Machine Learning
Author(s): Lokesh Vaishanav1,Shanu Chauhan1, Hrithik Vaishanav 2, Mahipal Singh Sankhla3, Dr. Rajeev Kumar4
1Students of Bachelor of Technology in Computer Science, School of Computer Science Engineering, Galgotias University, Greater Noida.
2Student of Bachelor of Technology in Information Technology, Techno India NJR, Collage, Udaipur
3Student of M.Sc. Forensic Science, 4Assistant Professor, Division of Forensic Science, School of Basic and Applied Sciences, Galgotias University, Greater Noida.
The arrival of android platforms with increased storage capabilities, better visualisations, computing competencies and the ubiquitous use of these platforms in the field of e-banking, online banking, business, and the storage of sensitive information on these devices, android platform is becoming the most targeted platform by malwares. They are primarily spread via repackaged apps to piggyback payload, update attacks, and drive by downloads. Malware constitutes a severe menace to user privacy, money, device and ﬁle veracity. It is posing benevolence challenges and difficulties to detect such malwares as signature based detection techniques available today are becoming inefficient in sensing new and anonymous malware. Hence we presents machine learning as an emerging era of modified and latest detection techniques. In this paper we will present various machine learning solutions to counter android malwares that analyse features from malicious application and use those features to classify and detect unknown malicious applications. This paper summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the android OS.