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This chapter reviews the evolution of the traditional internet into the Internet of Things (IoT). The characteristics and application of the IoT are also reviewed, together with its security concerns in terms of distributed denial of service attacks. The chapter further investigates the state-of-the-art in data mining techniques for Distributed Denial of Service (DDoS) attacks targeting the various infrastructures. The chapter explores the characteristics and pervasiveness of DDoS attacks. It also explores the motives, mechanisms and techniques used to execute a DDoS attack. The chapter further investigates the current data mining techniques that are used to combat and detect these attacks, their advantages and disadvantages are explored. Future direction of the research is also provided.

The paper introduces a monitoring tool that was designed for an existing network of Wi-Fi hotspots. This was done by adding data collection and visualization components to the existing network. Syslog protocol was used for data collection and left running for two months monitoring network's performance. Google Maps was used for Visualizing overall network's performance. The tool was tested with experts and it was compared to existing monitoring tools and It was found that the monitoring tool was more scalable and effective than traditional monitoring tools.

1 This paper addresses the problem of network monitoring by proposing an Artificial Immune System (AIS) system to achieve situation recognition and monitoring in a large network of Wi-Fi hotspots as part of a highly scalable preemptive monitoring tool for wireless networks. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP, we integrated an AIS algorithm into a data collection system to detect anomalous performance and aberrant behavior in the ISP's network. The results reveal the efficiency of the AIS system in terms of both anomaly performance and aberrant behavior on several test case scenarios.