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On a Mathematical Model for Low-Rate Shrew DDoS

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TLDR
A mathematical model for estimating attack effect of this stealthy type of DDoS, originally capturing the adjustment behaviors of victim TCPs congestion window, which reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment.
Abstract
The shrew distributed denial of service (DDoS) attack is very detrimental for many applications, since it can throttle TCP flows to a small fraction of their ideal rate at very low attack cost. Earlier works mainly focused on empirical studies of defending against the shrew DDoS, and very few of them provided analytic results about the attack itself. In this paper, we propose a mathematical model for estimating attack effect of this stealthy type of DDoS. By originally capturing the adjustment behaviors of victim TCPs congestion window, our model can comprehensively evaluate the combined impact of attack pattern (i.e., how the attack is configured) and network environment on attack effect (the existing models failed to consider the impact of network environment). Henceforth, our model has higher accuracy over a wider range of network environments. The relative error of our model remains around 10% for most attack patterns and network environments, whereas the relative error of the benchmark model in previous works has a mean value of 69.57%, and it could be more than 180% in some cases. More importantly, our model reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment, such as the minimum cost formula to launch a successful attack, and the maximum effect formula of a shrew attack. With them, we are able to find out how to adaptively tune the attack parameters (e.g., the DoS burst length) to improve its attack effect in a given network environment, and how to reconfigure the network resource (e.g., the bottleneck buffer size) to mitigate the shrew DDoS with a given attack pattern. Finally, based on our theoretical results, we put forward a simple strategy to defend the shrew attack. The simulation results indicate that this strategy can remarkably increase TCP throughput by nearly half of the bottleneck bandwidth (and can be higher) for general attack patterns.

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Citations
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Journal ArticleDOI

Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS

TL;DR: An invariant feature based approach that performs low rate attack detection and improves the performance of the methods used in detecting low rate attacks for invariant network conditions.
Journal ArticleDOI

Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges

TL;DR: This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms with a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment.
Journal ArticleDOI

Low-Rate DoS Attacks Detection Based on Network Multifractal

TL;DR: This paper targets at exploiting and estimating the changes in multifractal characteristics of network traffic for detecting LDoS attack flows and the algorithm of multifractals detrended fluctuation analysis (MF-DFA) is used to explore the change in terms of multifractional characteristics over a small scale of network Traffic.
Journal ArticleDOI

An SDN-Enabled Pseudo-Honeypot Strategy for Distributed Denial of Service Attacks in Industrial Internet of Things

TL;DR: A new attack that can identify honeypots to invalidate their protection is revealed, and several groups of Bayesian–Nash Equilibrium in the PHG strategy can achieve the optimal equilibrium between legitimate users and attackers.
Journal ArticleDOI

Low-Rate DDoS Attack Detection Using Expectation of Packet Size

TL;DR: A measurement—expectation of packet size—that is based on the distribution difference of the packet size to distinguish two typical low-rate DDoS attacks, the constant attack and the pulsing attack, from legitimate traffic is proposed.
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