Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.

Conventional energy generation sources mainly provide energy supply to remote areas nowadays. However, because of growing concerns over greenhouse gas emissions, the integration of renewable energy sources is mandatory to meet power demands and reduce climatic effects. The advancements in renewable generation sources and battery storage systems pave the way for microgrids (MGs). As a result, MGs are becoming a viable solution for power supply shortage problems in remote-area applications, such as oceanic islands. In this paper, an islanded MG, which consists of PV system, tidal turbine (TT), diesel generator (DG), and Li-ion battery, is considered for Ouessant island in Brittany region in France. The economic operation of the MG is achieved by including battery degradation cost, levelized costs of energy of the PV system and TT, operating and emission costs of DG, and network constraints. The developed model leads to a non-linear and non-convex problem, which unfortunately can converge to a local optimum solution. The problem has, therefore, been relaxed and converted to a convex second-order cone model to achieve an optimal decision strategy for islanded MG operations with a global or near-global solution. Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG. It is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost.