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» Machine Learning for Automatic Defence Against Distributed D...
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IDEAL
2003
Springer
13 years 10 months ago
Detecting Distributed Denial of Service (DDoS) Attacks through Inductive Learning
As the complexity of Internet is scaled up, it is likely for the Internet resources to be exposed to Distributed Denial of Service (DDoS) flooding attacks on TCP-based Web servers....
Sanguk Noh, Cheolho Lee, Kyunghee Choi, Gihyun Jun...
DIMVA
2009
13 years 6 months ago
Defending Browsers against Drive-by Downloads: Mitigating Heap-Spraying Code Injection Attacks
Drive-by download attacks are among the most common methods for spreading malware today. These attacks typically exploit memory corruption vulnerabilities in web browsers and brows...
Manuel Egele, Peter Wurzinger, Christopher Kruegel...
ICC
2009
IEEE
150views Communications» more  ICC 2009»
13 years 11 months ago
Modeling Human Behavior for Defense Against Flash-Crowd Attacks
—Flash-crowd attacks are the most vicious form of distributed denial of service (DDoS). They flood the victim with service requests generated from numerous bots. Attack requests...
Georgios Oikonomou, Jelena Mirkovic
NOSSDAV
2010
Springer
13 years 9 months ago
RTP-miner: a real-time security framework for RTP fuzzing attacks
Real-time Transport Protocol (RTP) is a widely adopted standard for transmission of multimedia traffic in Internet telephony (commonly known as VoIP). Therefore, it is a hot poten...
M. Ali Akbar, Muddassar Farooq
ARESEC
2011
117views more  ARESEC 2011»
12 years 4 months ago
Non-Parallelizable and Non-Interactive Client Puzzles from Modular Square Roots
—Denial of Service (DoS) attacks aiming to exhaust the resources of a server by overwhelming it with bogus requests have become a serious threat. Especially protocols that rely o...
Yves Igor Jerschow, Martin Mauve