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CCS
2006
ACM
15 years 1 months ago
Can machine learning be secure?
Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. H...
Marco Barreno, Blaine Nelson, Russell Sears, Antho...
DATE
2008
IEEE
182views Hardware» more  DATE 2008»
15 years 4 months ago
An adaptable FPGA-based System for Regular Expression Matching
In many applications string pattern matching is one of the most intensive tasks in terms of computation time and memory accesses. Network Intrusion Detection Systems and DNA Seque...
Ivano Bonesana, Marco Paolieri, Marco D. Santambro...
CONEXT
2007
ACM
14 years 11 months ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until ...
Oliver Sharma, Mark Girolami, Joseph S. Sventek
AINA
2009
IEEE
15 years 4 months 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
ICC
2007
IEEE
164views Communications» more  ICC 2007»
15 years 4 months ago
A Framework of Attacker Centric Cyber Attack Behavior Analysis
—Cyber attack behavior analysis can be roughly classified as “network centric” and “attacker centric” approaches. Compared with traditional “network centric” approach...
Xuena Peng, Hong Zhao