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Recently (2011) Chen et al. found that Wang et al.’s scheme (2007) is vulnerable to impersonation attacks and parallel session attacks; and then proposed a security enhancement of Wang et al.’s scheme. Chen et al. claimed to inherit the merits and eradicate the flaws of the original scheme through their improved scheme. Unfortunately, we found that Chen et al.’s scheme inherits some flaws of the original scheme, like the known-key attack, smart card loss attack and its serious consequences. In addition, Chen et al.’s scheme is not easily reparable and is unable to provide forward secrecy. Thus Chen et al.’s scheme still has scope for security enhancement. Finally, we propose an improved scheme with better security strength. Moreover, we analyze the performance of our scheme and prove that ours is suitable for applications with high security requirements.

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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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