CTMBIDS：基于卷积Tsetlin机器的SDN环境中DDoS攻击入侵检测系统
CTMBIDS: Convolutional Tsetlin Machine Based Intrusion Detection System for DDoS attacks in an SDN environment
AI总结：
针对SDN环境中的DDoS攻击，提出基于卷积Tsetlin机器的轻量级入侵检测系统CTMBIDS，生成三个DDoS数据集，相比深度学习模型内存消耗更低且准确性相当或更优。
摘要：
软件定义网络（SDN）如今面临许多安全挑战。在该领域的入侵检测系统（IDS）方面已经进行了大量研究。然而，许多方法仍然依赖深度学习算法。这些算法在实现复杂性、高处理能力和高内存消耗方面存在不足。除了安全问题外，首先，基于SDN协议的数据集数量非常少。其次，现有的数据集包含网络中的多种攻击，并未专注于单一攻击。因此，为了引入一个专注于分布式拒绝服务（DDoS）攻击的基于SDN的入侵检测系统，有必要生成一个面向DDoS的数据集，其特征能够训练出高质量的IDS。在本工作中，为了解决SDN中的两个重要挑战，我们首先基于三种常见且不同的网络拓扑生成了三个DDoS攻击数据集。第二步，使用卷积Tsetlin机器（CTM），我们引入了一个轻量级的DDoS攻击入侵检测系统，称为CTMBIDS。CTMBIDS的轻量级特性源于其低内存消耗以及相对于现有复杂深度学习模型的可解释性。CTMBIDS对系统资源的低使用率使其成为消耗SDN控制器最少内存的最优软件的理想选择。此外，为了验证生成数据集的质量，我们还将CTMBIDS的实证结果与KDDCup99基准数据集的DDoS攻击进行了比较。由于本工作的主要重点是轻量级IDS，结果显示CTMBIDS比基于深度学习的方法效率更高。此外，结果还表明，在大多数数据集中，所提出的方法具有相对相等或更好的准确性，并且比现有方法消耗的内存少得多。
英文摘要：
Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely on deep learning algorithms. These algorithms suffer from complexity in implementation, high processing power and high memory consumption. In addition to security issues, firstly, the number of datasets that are based on SDN protocols are very small. Secondly, the ones that are available encompass numerous attacks in the network and do not focus on a single attack. For this reason, to introduce an SDN-based IDS with a focus on Distributed Denial of Service (DDoS) attacks, it is necessary to generate a DDoS-oriented dataset whose features can train a high-quality IDS. In this work, in order to address two important challenges in SDNs, initially, we generate three DDoS attack datasets based on three common and different network topologies. In the second step, using the Convolutional Tsetlin Machine (CTM), we introduce a lightweight IDS for DDoS attack dubbed CTMBIDS. The lightweight nature of the CTMBIDS stems from its low memory consumption and also its interpretability compared to the existing complex deep learning models. The low usage of system resources for the CTMBIDS makes it an ideal choice for an optimal software that consumes the SDN controllers least amount of memory. Also, in order to ascertain the quality of the generated datasets, we compare the CTMBIDS empirical results with the DDoS attacks of the KDDCup99 benchmark dataset as well. Since the main focus of this work is on a lightweight IDS, the results show the CTMBIDS performs much more efficiently than deep learning based approaches. Furthermore, the results also show in most datasets, the proposed method has relatively equal or better accuracy and also consumes much less memory than the existing methods.
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