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Machine Learning DDoS Detection for Generated Internet of Things Dataset (IoT Dat)

机译：用于生成的物联网数据集（IoT Dat）的机器学习DDoS检测

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Network infrastructure faces a lot of attacks, including attacks on integrity and confidentiality of the network packets along with their destinations and sources as well as attacks on network availability. Distributed Denial of Service (DDoS) emanates from various attack sources and focuses on the network, services, and hosts' availability. DDoS attacks are difficult to trace back to actual attackers, can lead to catastrophic service loss, and are launched with ease, making them one of the most dangerous attacks. This research simulates an Internet of Things network in-home setting of 100 nodes using OMNeT++ simulation tool, including a DDoS attack. Regular and attack-injected traffic is generated to evaluate the accuracy of detecting DDoS attacks in IoT networks using machine learning technqiues. A new IoT Dataset called IoT Dat is generated with different scenarios of normal traffic and traffic with attacks of different intensities of 5, 10, and 20. The authors will make this dataset publicly available. Moreover, machine learning techniques are used to assess the efficiency of attack detection.
机译：网络基础设施面临许多攻击，包括对网络数据包及其目的地和源的完整性和机密性的攻击，以及对网络可用性的攻击。分布式拒绝服务（DDoS）来自各种攻击源，并专注于网络，服务和主机的可用性。 DDoS攻击很难追溯到实际的攻击者，会导致灾难性的服务损失，并且容易发动，使其成为最危险的攻击之一。这项研究使用OMNeT ++模拟工具（包括DDoS攻击）模拟了100个节点的物联网网络家庭设置。生成常规流量和注入攻击的流量，以评估使用机器学习技术在IoT网络中检测DDoS攻击的准确性。在正常流量和攻击强度分别为5、10和20的不同流量情况下，将生成一个称为IoT Dat的新IoT数据集。作者将使此数据集公开可用。此外，机器学习技术用于评估攻击检测的效率。

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