Abstract
The large-scale deployment of data driven internet of things (IoT) leads to employing a number of self-directed mobile or static sensor nodes in various areas of interest where the mobile nodes specifically operates on collecting data and cooperatively transmit that data back to the integrated cloud systems. The cloud enabled IoT has its wide range of applicability into various business, commercial and military applications. The wireless links can be easily disrupted by the adversaries due to their large attack surface. Adversaries in this regard ranges from hackers with a laptop to corporations and government officials. Due to the complex nature of localization of dynamic IoT nodes specifically the low power sensor devices, it becomes compelled to make themselves vulnerable for reprogramming and capture by an unauthorized user. On the other hand the deployment of low power sensor devices implicates operational constraints on conventional high level strong encryption approaches due to their limited processing power and computational capability. Owing to these issues the security challenges in current and futuristic IoTs are more. The study conceptualized an analytical system well-capable of protecting data and IoT driven cloud systems and also detect network intrusion considering artificial intelligence (AI) systems. The study also theoretically exhibits the extensive analysis of AI algorithms in cloud network intrusion detection. The performance analysis further conveyed the superiority of the proposed model.
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Shahabadkar, R., Shahabadkar, K.R. (2019). Implication of Artificial Intelligence to Enhance the Security Aspects of Cloud Enabled Internet of Things (IoT). In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_2
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DOI: https://doi.org/10.1007/978-3-030-19807-7_2
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