Multilayer Approach to Internet of Things (IoT) Security The Internet of Things (IoT) is a huge, global, distributed network of interlinked heterogeneous devices armed with embedded sensors, actuators and processors enabling them to connect to the internet, exchange data, communicate and interact seamlessly with one another in real time. It enjoys wide range of applications where it holds important data, hence becoming exposed to varying degrees of security and privacy threats which require careful architectural design to address. Consequently, we present in this paper a multilayer security aware IoT architecture that aims to secure the entire system by providing security at all layers to ensure secure data collection, transfer, analysis, storage and usage. That is necessary because each layer of the IoT has vulnerabilities and a successful attack on any layer can have farreaching impact on the whole system. We then proposed a novel security algorithm to protect the IoT system against Denial of Service (DoS) attacks at the application layer, the weakest link in IoT. The algorithm logs record of user activities and actions at given times of the day which it employs to regulate access and prevent DoS attacks. Finally, we evaluated our proposed approach against IoT security and privacy requirements, and it demonstrated better level of security.

IDSAIoT: An Intrusion Detection System Architecture for IoT Networks The Internet of Things (IoT) allows large amounts and variety of devices to connect, interact and exchange data. The IoT network creates numerous opportunities for novel attacks that can compromise information and systems integrity. Intrusion detection systems have been studied over two decades, mostly employing traditional data mining and machine learning techniques that require an offline phase for model training on large amounts of data. This paper presents three data stream novelty detection techniques applied to the intrusion detection problem and proposes IDSAIoT, a novel implementation architecture, which combines the use of resources at the edge of the network and a public cloud. After an extensive empirical evaluation, results show that it is possible to identify new attack patterns soon after their emergence and to adapt the models in an efficient way.