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.
Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.
In this contemporary study, theoritical investigation of nanofluidic model is thought-out. Two-dimensional nanomaterials based mixed flow is considered here. Convective solar radiative heat transport properties have been investigated over a nonlinearly stretched wall in the presence of magneto-hydrodynamic (MHD), by innovative application of semi analytical "optimal homotopy asymptotic method (OHAM)". OHAM does not require any discretization, linearization and small parameter assumption. OHAM describes extremely precise 1 st /2 nd order solutions without the need of computing further higher order terms, therefore, fast convergence is observed. Nanofluidic governing model is transformed into system of ordinary differential equations (ODEs) by exploitation of similarity transformation. To study the significance of radiation parameter along with thermophoresis parameter, a semi analytical solver is applied to the transformed system. In this work, Brownian motion , influence of magnetic field, Lewis number, Prandtl number, Eckert number and Biot number have investigated on velocity, temperature and nanoparticle concentration profiles. The study provides sufficient number of graphical representations to demonstrate the inspiration of mentioned parameters.
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