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McBoost: Boosting Scalability in Malware Collection and Analysis Using Statistical Classification of Executables

Roberto Perdisci, Andrea Lanzi, Wenke Lee
2008 2008 Annual Computer Security Applications Conference (ACSAC)  
In this work, we propose Malware Collection Booster (McBoost), a fast statistical malware detection tool that is intended to improve the scalability of existing malware collection and analysis approaches  ...  Given a large collection of binaries that may contain both hitherto unknown malware and benign executables, McBoost reduces the overall time of analysis by classifying and filtering out the least suspicious  ...  Conclusion We presented McBoost, a fast statistical malware detection tool intended to improve the scalability of existing malware collection and analysis techniques.  ... 
doi:10.1109/acsac.2008.22 dblp:conf/acsac/PerdisciLL08 fatcat:2qa4f25mtzh67al52tzc22z4fu

PE-Miner: Mining Structural Information to Detect Malicious Executables in Realtime [chapter]

M. Zubair Shafiq, S. Momina Tabish, Fauzan Mirza, Muddassar Farooq
2009 Lecture Notes in Computer Science  
' set, and (3) select an efficient data mining algorithm for final classification between benign and malicious executables.  ...  We have evaluated PE-Miner on two malware collections, VX Heavens and Malfease datasets which contain about 11 and 5 thousand malicious PE files respectively.  ...  We also thank VX Heavens moderators for making a huge malware collection publicly available and sharing packing statistics of malware.  ... 
doi:10.1007/978-3-642-04342-0_7 fatcat:abduhk6l7rgsdafnk5pd6au2em

Survey on Representation Techniques for Malware Detection System

Gamal Abdel Nassir Mohamed, Norafida Bte Ithnin
2017 American Journal of Applied Sciences  
We have provided a comprehensive bibliography in malware detection, its techniques and analysis methods for malware researchers.  ...  This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic  ...  detection tool that is intended to improve the scalability of existing malware collection and analysis approaches.  ... 
doi:10.3844/ajassp.2017.1049.1069 fatcat:5nl4tt3zyneklelajprm5v7ohi

The arms race: Adversarial search defeats entropy used to detect malware

Héctor D. Menéndez, Sukriti Bhattacharya, David Clark, Earl T. Barr
2019 Expert systems with applications  
The promise of entropy as a malware detector is that it works on executables as binary strings, without needing pre-processing, disassembly, dynamic analysis, reverse engineering, or manual analysis.  ...  String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level.  ...  Acknowledgments This work has been supported by the next research projects: Se-MaMatch EP/K032623/1, DAASE EP/J017515/1, LUCID EP/P005659/1 and InfoTestSS EP/P006116/1 from EPSRC.  ... 
doi:10.1016/j.eswa.2018.10.011 fatcat:s74eu2rucfeczl2cern2aeydni

MAST

Saurabh Chakradeo, Bradley Reaves, Patrick Traynor, William Enck
2013 Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks - WiSec '13  
We then use MAST to perform triage on three third-party markets of different size and malware composition-36,710 applications in total.  ...  Using MAST ordered ranking, malware-analysis tools can find 95% of malware at the cost of analyzing 13% of the non-malicious applications on average across multiple markets, and MAST triage processes markets  ...  This work was supported in part by the US National Science Foundation under grant numbers DGE-1148903, CNS-0916047, CNS-0952959, and TWC-1222699.  ... 
doi:10.1145/2462096.2462100 dblp:conf/wisec/ChakradeoRTE13 fatcat:byandg53yvg4dc5nr4vepmb4dy