Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research Abstract   As the Internet of Things (IoT) recently attains tremendous popularity, this promising technology leads to a variety of security challenges. The traditional solutions do not fit the new challenges brought by the IoT ecosystem. Although the developmentu0027s area of Artificial Immune Systems (AIS) provides an opportunity to improve security issues and create a fertile and exciting environment for further research and experiments, there is not any systematic and comprehensive study about analyzing its important for IoT environment. Therefore, This work aims to identify, evaluate, and perform a comprehensive study of empirical research on the studies of AIS approaches to secure the IoT environment. The relevant and highquality studies are addressing using three research questions about the main research motivations, existing solutions, and future gaps and directions. The AIS approaches have been divided into three main categories based on IoT layers, and detailed classifications have also been included based on different parameters. To achieve this aim, the authors use a systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected studies and their main techniques, as well as their benefits and drawbacks in general. This research process strives to build a knowledge base for AIS solutions under the umbrella of IoT security and suggest directions for future research.

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.