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Summary: Internet of Things (IoT) is an intelligent technology and service that mutually communicates information between human and devices or between Internet‐based devices. In IoT security, the authentication should be distributed and lightweight. Token generation should be based on the trust worthiness of the devices, and it needs to ensure fast authentication and authorization. In this paper, we propose an Energy Efficient Distributed Lightweight Authentication and Encryption (EEDLAE) technique for IoT security. In our proposed technique, the receiver generates the token for each sender. The token expiration time is determined based on the trust value of each sender, and sleep period of the receiver radio is determined based on its remaining energy. For encryption, Counter with Cipher Block Chaining‐Message Authentication Code (CCM) is applied. Experimental results show that EEDLAE technique can hold out against affected packets, higher resilience against node capture, increased throughput, and residual energy, compared with the existing technique. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) involve large volumes of data generated from the interactions between devices and people, and security is a main alarm in IoT. Most of the anomaly detection techniques in IoT use supervised machine learning technique which involve huge overhead and high false positives. It is observed that severity of attack response was not considered. In this study, the authors propose to develop an authorisation, attack detection and avoidance framework for IoT devices. Initially, traffic collection agent continuously gathers packet level and flow level information for a given time interval. Then detection agent (DA) first checks the collected information with the attack rules table. If any matching attack pattern is found, it informs the attack type to response agent (RA). On the other hand, if no matching pattern is found, then the classification agent applies multi‐class support vector machine algorithm. Once the RA obtains the attack type from DA, then it estimates the severity of attack by computing the attack frequency over different time windows and appropriate action will be performed. Experimental results show that the proposed framework reduces 13% of unauthorised access and 19% false positive rate thereby increasing the detection accuracy by 0.6% and throughput. [ABSTRACT FROM AUTHOR]