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Furthermore, we quantify the damage of DoS/DDoS attacks at three different levels protocol (Transmission Control Protocol-TCP), device’s resources (bandwidth, CPU, memory), and network (infection and recovery speed). In addition, this article provides a thorough review and comparison of the existing attack models, in particular we explain, analyze and simulate different aspects of three prominent models congestion window, queuing, and epidemic models (same model used for corona virus spread analysis).
ANYSEND O SIMILAR SOFTWARE
In this survey, we present a classification approach for existing DoS/DDoS models in different kinds of networks traditional networks, Software Defined Networks (SDN) and virtual networks. A deeper understanding of DoS/DDoS attacks would lead to the development of more efficient solutions and countermeasures to mitigate their impact. Modelling DoS/DDoS attacks is necessary to get a better understanding of their behaviour at each step of the attack process, from the Botnet recruitment up to the dynamics of the attack. Experiments are conducted in an evaluating network with a FPGA-based OpenFlow switch prototype and the Ryu controller, which reveal that our proposed OverWatch framework and flow monitoring algorithm can greatly improve the detection efficiency, as well as reduce the detection delay and southboundĭenial of Service and Distributed Denial of Service (DoS/DDoS) attacks have been one of the biggest threats against communication networks and applications throughout the years.
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In OverWatch, we propose a lightweight flow monitoring algorithm to capture the key features of DDoS attack traffics on the data plane by polling the values of counters in OpenFlow switches. It leverages computational capabilities thatĬurrently underutilized on OpenFlow switches to shrink the detection range for fine-grained DDoS attack detections. In this paper, we propose a SDN-based DDoS attack detection framework with cross-plane collaboration called OverWatch, which performs a two-stage granularity filtering procedure between coarse-grained detection data plane and fine-grained detection control plane for abnormal flows. Traditional DDoS attack detection mechanisms are based on middle-boxĭevices or SDN controllers, which either lack network-wide monitoring information or suffer with serious southbound communication overhead and detection delay. Distributed Denial of Service (DDoS) attack is one of the biggest concerns for security professionals.