Runtime Malware Detection Using Hardware Features (Record no. 237115)

MARC details
000 -LEADER
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Sanjith, S.
245 #0 - TITLE STATEMENT
Title Runtime Malware Detection Using Hardware Features
Remainder of title Sivaraman, E.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. Jan-May 2019
Name of publisher, distributor, etc. PES University
Place of publication, distribution, etc. Bengaluru
510 ## - CITATION/REFERENCES NOTE
Name of source 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
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Compact Disks
Holdings
Date last seen Total checkouts Accession number Price effective from Category Lost status Damaged status Withdrawn status Home library Current library Entry date Grants Currency symbol Conversion rate Discount
26/01/2024   CD7242 26/01/2024 Compact Disks       Central Library Central Library   CS/PG RS 1  

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