Optimized Monitoring and Detection of Internet of Things resources-constraints Cyber Attacks

This research takes place in the context of the optimized monitoring and detec- tion of Internet of Things (IoT) resource-constraints attacks. Meanwhile, the In- ternet of Everything (IoE) concept is presented as a wider extension of IoT. How- ever, the IoE realization meets critical challenges, including the limited network coverage and the limited resources of existing network technologies and smart devices. The IoT represents a network of embedded devices that are uniquely identifiable and have embedded software required to communicate between the transient states. The IoT enables a connection between billions of sensors, actu- ators, and even human beings to the Internet, creating a wide range of services, some of which are mission-critical. However, IoT networks are faulty; things are resource-constrained in terms of energy and computational capabilities. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and device reliability, which requires proactive device state moni- toring. This dissertation presents an approach to optimize the monitoring and detection of resource-constraints attacks in IoT and IoE smart devices. First, it has been shown that smart devices suffer from resource-constraints problems; therefore, using lightweight algorithms to detect and mitigate the resource-constraints at- tack is essential. Practical analysis and monitoring of smart device resources’ are included and discussed to understand the behaviour of the devices before and after attacking real smart devices. These analyses are straightforwardly extended for building lightweight detection and mitigation techniques against energy and memory attacks. Detection of energy consumption attacks based on monitoring the package reception rate of smart devices is proposed to de- tect energy attacks in smart devices effectively. The proposed lightweight algo- rithm efficiently detects energy attacks for different protocols, e.g., TCP, UDP, and MQTT. Moreover, analyzing memory usage attacks is also considered in this thesis. Therefore, another lightweight algorithm is also built to detect the memory-usage attack once it appears and stops. This algorithm considers mon- itoring the memory usage of the smart devices when the smart devices are Idle, Active, and Under attack. Based on the presented methods and monitoring analysis, the problem of resource-constraint attacks in IoT systems is systemat- ically eliminated by parameterizing the lightweight algorithms to adapt to the resource-constraint problems of the smart devices.