The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoT communication, and most of the IoT devices have limited resources. Enabling a complete security measure for such devices is a challenging task, yet necessary. Many lightweight security solutions have surfaced lately for IoT. The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world. It is also hard to deploy any traditional security protocol on resource-constrained IoT devices. Softwaredefined networking introduces a centralized control in computer networks. SDN has a programmable approach towards networking that decouples control and data planes. An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT. The proposed intrusion detection system does not burden the IoT devices with security profiles. The proposed work is executed on the simulated environment. The results of the simulation test are evaluated using various matrices and compared with other relevant methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Internet of things (IoT) is the network of physical objects connected to provide various services. IoT is expanding rapidly, and is positively influencing many areas. The impact of IoT is evident in medical field, manufacturing units and livestock. The IoT is also vulnerable to many cyber threats, owing to its limited resources and battery operation. In contemporary times the security threats like DDoS, botnet malware, man in the middle, flood attacks and ransomware are affecting the smooth functioning of IoT. Ransomware has emerged as one of the biggest threat in cyber world. Ransomware is a type of malware that stops the access to files by encrypting them and decrypts the files only when a ransom is paid. The negligence towards the IoT ransomware can result in disastrous outcomes. In this paper, the growth of ransomware attacks for past few years is shown with special focus on ransomwares threatening IoT. A detection mechanism for IoT ransomware attack is presented that is designed after study of ransomware for IoT. The proposed model monitors the incoming IoT traffic through Software Defined Network (SDN) gateway. It uses policies framed in SDN controller for detection and alleviation of ransomware in IoT.

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