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» Detecting worm variants using machine learning
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TDSC
2010
172views more  TDSC 2010»
13 years 1 days ago
Proactive Detection of Computer Worms Using Model Checking
Although recent estimates are speaking of 200,000 different viruses, worms, and Trojan horses, the majority of them are variants of previously existing malware. As these variants m...
Johannes Kinder, Stefan Katzenbeisser, Christian S...
BIBE
2007
IEEE
162views Bioinformatics» more  BIBE 2007»
13 years 11 months ago
An Investigation into the Feasibility of Detecting Microscopic Disease Using Machine Learning
— The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. We are investigating the possibility of de...
Mary Qu Yang, Jack Y. Yang
RAID
2004
Springer
13 years 10 months ago
HoneyStat: Local Worm Detection Using Honeypots
Worm detection systems have traditionally used global strategies and focused on scan rates. The noise associated with this approach requires statistical techniques and large data s...
David Dagon, Xinzhou Qin, Guofei Gu, Wenke Lee, Ju...
AINA
2009
IEEE
14 years 5 days ago
Similarity Search over DNS Query Streams for Email Worm Detection
Email worms continue to be a persistent problem, indicating that current approaches against this class of selfpropagating malicious code yield rather meagre results. Additionally,...
Nikolaos Chatzis, Nevil Brownlee
DIMVA
2008
13 years 6 months ago
Learning and Classification of Malware Behavior
Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variant...
Konrad Rieck, Thorsten Holz, Carsten Willems, Patr...