利用 SDN 和新指标实时监控和缓解 SDoS 攻击

IF 7.4 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-18 DOI:10.1109/TCCN.2023.3306358
Dan Tang;Siyuan Wang;Siqi Zhang;Zheng Qin;Wei Liang;Sheng Xiao
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引用次数: 0

摘要

慢速拒绝服务(SDoS)攻击是一种攻击速率较低的拒绝服务(DoS)攻击。它们具有闪烁群发的特性,可以很好地隐藏在合法流量中,因此很难被反DoS机制识别。现有的解决方案存在部署困难、实时性差、可扩展性差等缺点。我们提出了一种基于软件定义网络(SDN)和新流量指标的 SDoS 攻击实时监控和缓解方案。新的流量指标是波动系数(CoF)和脉冲周期系数(PPC),它们可以帮助我们识别网络中的 SDoS 攻击,并快速准确地定位攻击者。基于这两个指标,该方案使用高斯混合模型(GMM)来预测和聚类网络流量,并获取攻击者 IP。缓解模块安装流量规则,以摒弃攻击流量。通过黑名单和加权 IP,缓解模块降低了误报情况下合法流量被丢弃的概率。实验表明,我们的方案部署成本低,能快速准确地识别攻击和定位攻击者。该缓解策略能在 4 到 6 秒内高概率地缓解 SDoS 攻击。
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Real-Time Monitoring and Mitigation of SDoS Attacks Using the SDN and New Metrics
Slow-rate denial-of-service (SDoS) attacks are a type of denial-of-service (DoS) attacks with a low attack rate. They have a flash-crowd nature and can be well concealed in legitimate traffic, so it is difficult to identify them by anti-DoS mechanisms. Existing solutions have drawbacks such as difficult deployment, poor real-time performance, and poor scalability. We propose a scheme for real-time monitoring and mitigation of SDoS attacks on the basis of a software-defined network (SDN) and new traffic metrics. The new traffic metrics are the coefficient of fluctuation (CoF) and pulse period coefficient (PPC), which can help us identify SDoS attacks in the network and locate the attackers quickly and accurately. Based on the two metrics, the scheme uses a Gaussian mixture model (GMM) to predict and cluster network traffic and obtain attacker IPs. The mitigation module installs flow rules to discard attacking flows. With blacklisting and weighted IPs, the mitigation module reduces the probability of dropping legitimate flows in case of false positives. Experiments show that our scheme is inexpensive to deploy and can identify attacks and locate attackers quickly and accurately. The mitigation strategy can mitigate SDoS attacks within 4 to 6 seconds with high probability.
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
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