Location
Science Center Auditorium, University of Dayton
Start Date
23-4-2016 2:15 PM
Description
Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the Android OS.
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
A Study of Android Malware Detection Techniques and Machine Learning
Science Center Auditorium, University of Dayton
Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the Android OS.
Comments
Copyright © 2016 by the authors. This paper was presented at the 2016 Modern Artificial Intelligence and Cognitive Science Conference, held at the University of Dayton April 22-23, 2016. Permission documentation is on file.