Runtime Malware Detection Using Hardware Features Sivaraman, E.
Sanjith, S.
Runtime Malware Detection Using Hardware Features Sivaraman, E. - Bengaluru PES University Jan-May 2019
The low-level micro architectural malware prediction methods have shown good results in finding the viruses than the earlier software-based techniques. But these methods have failed to make an impact on the false detection rates which affect the precision of the detection scheme largely. When a person installs an application from a third-party website like filehippo.com or download.com, these methods tend to ignore significant attributes of an application like checksum, signature of the app creator that in turn makes an impact on the precision of the prediction methods due to more false prediction rates. In order to achieve a lesser false alarm rates, the impact made on the hardware features by the applications are tracked and the basic ML classifiers are trained and their efficiency is boosted using ensemble methods and a clear performance optimization of the ML classifiers using minimal number of HPCs is represented, thereby, minimizing the required number of runs for which
Runtime Malware Detection Using Hardware Features Sivaraman, E. - Bengaluru PES University Jan-May 2019
The low-level micro architectural malware prediction methods have shown good results in finding the viruses than the earlier software-based techniques. But these methods have failed to make an impact on the false detection rates which affect the precision of the detection scheme largely. When a person installs an application from a third-party website like filehippo.com or download.com, these methods tend to ignore significant attributes of an application like checksum, signature of the app creator that in turn makes an impact on the precision of the prediction methods due to more false prediction rates. In order to achieve a lesser false alarm rates, the impact made on the hardware features by the applications are tracked and the basic ML classifiers are trained and their efficiency is boosted using ensemble methods and a clear performance optimization of the ML classifiers using minimal number of HPCs is represented, thereby, minimizing the required number of runs for which