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Abstract-WSNs are multihop networks, which depend on the intermediate nodes to relay the data packet to the destination. These nodes are equipped with lesser memory, limited battery power, little computation capability, small range of communication and need a secured and efficient routing path to forward the incoming packet. In this paper, we propose a secure cluster based multipath routing protocol (SCMRP). Researchers have proposed clustered sensor networks to increase the efficiency (i.e. increase system throughput, save energy and decrease system delay by data aggregation) and multipath sensor networks to increase the resilience and reliability of the network. The SCMRP is the combination of these two sensor networks; therefore, it provides efficiency as well as reliability and the proper use of cryptographic algorithm provides sufficient security to the sensor network. SCMRP provides security against various attacks like altering the routing information, selective forwarding attack, sinkhole attack, wormhole attack, Sybil attack etc. Further, we have provided a brief analysis to various issues related to key management, orphan nodes, security and energy efficiency.

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The Internet of Things (IoT) has become an integral requirement to equip common life. According to IDC, the number of IoT devices may increase exponentially up to a trillion in near future. Thus, their cyberspace having inherent vulnerabilities leads to various possible serious cyber-attacks. So, the security of IoT systems becomes the prime concern for its consumers and businesses. Therefore, to enhance the reliability of IoT security systems, a better and real-time approach is required. For this purpose, the creation of a real-time dataset is essential for IoT traffic analysis. In this paper, the experimental testbed has been devised for the generation of a real-time dataset using the IoT botnet traffic in which each of the bots consists of several possible attacks. Besides, an extensive comparative study of the proposed dataset and existing datasets are done using popular Machine Learning (ML) techniques to show its relevance in the real-time scenario.

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