Visible to the public Optimized Activation Function on Deep Belief Network for Attack Detection in IoT

TitleOptimized Activation Function on Deep Belief Network for Attack Detection in IoT
Publication TypeConference Paper
Year of Publication2019
AuthorsSarma, Subramonian Krishna
Conference Name2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Keywordsactivation function optimization, attack detection, attack detection system, belief networks, classification, Classification algorithms, composability, Conferences, deep belief network, DP management, feature classification process, feature extraction, feature extraction process, higher-order statistical features, Internet of Things, IoT, Lion Algorithm, Neurons, operational security, optimisation, Optimization, pattern classification, Proposed Feature Extraction, pubcrawl, Resiliency, security, security of data, statistical analysis, Training
AbstractThis paper mainly focuses on presenting a novel attack detection system to thread out the risk issues in IoT. The presented attack detection system links the interconnection of DevOps as it creates the correlation between development and IT operations. Further, the presented attack detection model ensures the operational security of different applications. In view of this, the implemented system incorporates two main stages named Proposed Feature Extraction process and Classification. The data from every application is processed with the initial stage of feature extraction, which concatenates the statistical and higher-order statistical features. After that, these extracted features are supplied to classification process, where determines the presence of attacks. For this classification purpose, this paper aims to deploy the optimized Deep Belief Network (DBN), where the activation function is tuned optimally. Furthermore, the optimal tuning is done by a renowned meta-heuristic algorithm called Lion Algorithm (LA). Finally, the performance of proposed work is compared and proved over other conventional methods.
DOI10.1109/I-SMAC47947.2019.9032633
Citation Keysarma_optimized_2019