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首页> 外文期刊>Journal of computer systems, networks, and communications >Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN)
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Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN)

机译：基于高级支持向量机（ASVM-）对软件定义网络的分布式拒绝服务（DDOS）攻击的检测（SDN）

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Software Defined Networking (SDN) has many advantages over a traditional network. The great advantage of SDN is that the network control is physically separated from forwarding devices. SDN can solve many security issues of a legacy network. Nevertheless, SDN has many security vulnerabilities. The biggest issue of SDN vulnerabilities is Distributed Denial of Service (DDoS) attack. The DDoS attack on SDN becomes an important problem, and varieties of methods had been applied for detection and mitigation purposes. The objectives of this paper are to propose a detection method of DDoS attacks by using SDN based technique that will disturb the legitimate user's activities at the minimum and to propose Advanced Support Vector Machine (ASVM) technique as an enhancement of existing Support Vector Machine (SVM) algorithm to detect DDoS attacks. ASVM technique is a multiclass classification method consisting of three classes. In this paper, we can successfully detect two types of flooding-based DDoS attacks. Our detection technique can reduce the training time as well as the testing time by using two key features, namely, the volumetric and the asymmetric features. We evaluate the results by measuring a false alarm rate, a detection rate, and accuracy. The detection accuracy of our detection technique is approximately 97% with the fastest training time and testing time.
机译：软件定义的网络（SDN）与传统网络相比具有许多优点。 SDN的巨大优点是网络控制与转发装置物理分离。 SDN可以解决传统网络的许多安全问题。尽管如此，SDN有许多安全漏洞。最大的SDN漏洞问题是分布式拒绝服务（DDOS）攻击。对SDN的DDOS攻击成为一个重要问题，并申请了各种方法进行检测和缓解目的。本文的目的是通过使用基于SDN的技术提出DDOS攻击的检测方法，这些技术将在最低的最低和第＃X27; S的活动中扰乱合法用户的活动，并提出高级支持向量机（ASVM）技术作为增强现有支持矢量机（SVM）算法检测DDOS攻击。 ASVM技术是由三类组成的多级分类方法。在本文中，我们可以成功检测两种类型的洪水的DDOS攻击。我们的检测技术可以通过使用两个关键特征来减少训练时间以及测试时间，即体积和不对称特征。我们通过测量误报率，检测率和准确性来评估结果。我们的检测技术的检测精度约为97＆＃x0025;具有最快的培训时间和测试时间。

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