Intelligent OS X malware threat detection with code inspection

Author:

Pajouh Hamed Haddad,Dehghantanha AliORCID,Khayami Raouf,Choo Kim-Kwang Raymond

Funder

European Council International Incoming Fellowship

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Hardware and Architecture,Software,Computer Science (miscellaneous)

Reference50 articles.

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2. Bisio, F., Gastaldo, P., Meda, C, Nasta, S., Zunino, R.: Machine learning-based system for detecting unseen malicious software. In: Gloria A.D. (eds) Applications in Electronics Pervading Industry, Environment and Society [Internet], p. 915. Springer International Publishing (2016) [cited 2016 Nov 28]. (Lecture Notes in Electrical Engineering). http://link.springer.com/chapter/10.1007/978-3-319-20227-3_2

3. Kaspersky Lab: Overall statistics for 2015 [Internet]. Kaspersky Lab, Russia (2016). https://securelist.com/files/2015/12/KSB_2015_Statistics_FINAL_EN.pdf

4. Panda Lab: Pandalabs annual report 2015 [Internet], p. 30. (2016) [cited 2016 Nov 30]. Report No.: 4. http://www.pandasecurity.com/mediacenter/src/uploads/2014/07/Pandalabs-2015-anual-EN.pdf

5. Beek, C., Frosst, D., Greve, P., Gund, Y., Moreno, F., Peterson, E., Schmugar, C., Simon, R., Sommer, D., Sun, B., Tiwari, R., Weafer, V.: McAfee Labs Threats Report [Internet], p. 49. McAfee Lab (April 2017). https://www.mcafee.com/us/resources/reports/rp-quarterly-threats-mar-2017.pdf

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