Multischeme feedforward artificial neural network architecture for DDoS attack detection
نویسندگان
چکیده
منابع مشابه
DDoS Attack Detection based on Chaos Theory and Artificial Neural Network
DDoS attacks temporarily make the target system unavailable to the legitimate users. They don’t steal anything. But they cause big headache for targeted companies and network engineers. Application layer DDoS attacks are difficult to detect because they mimic normal traffic. This paper proposes a novel method of detection of DDoS attacks based on Chaos theory and Artificial neural networks. Key...
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Distributed denial-of-service (DDoS) attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention systems (IDS/IPS), mostly caused by application-layer DDoS attacks. Within this context, the objective of the paper is to detect a DDoS attack using a m...
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In this sense, DoS, especially DDoS, not only threatens the Internet, but also threatens the civil security, due to its prevalent usage in cyber-crimes. Thus to understand well the characteristics of DDoS problems and investigate corresponding defense mechanisms have significant contributions not only for academia and industry, but also for the social security and emergency management agencies,...
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This paper presents a systematic method for DDoS attack detection. DDoS attack can be considered system anomaly or misuse from which abnormal behavior is imposed on network traffic. Attack detection can be performed via abnormal behavior identification. Network traffic characterization with behavior modeling could be a good guidance of attack detection. Aggregated traffic has been found to be s...
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ژورنال
عنوان ژورنال: Bulletin of Electrical Engineering and Informatics
سال: 2021
ISSN: 2302-9285,2089-3191
DOI: 10.11591/eei.v10i1.2383