Improved spider monkey optimization‐based multi‐objective software‐defined networking routing with block chain technology for Internet of Things security. (1st February 2022)

• Record Type: Journal Article
• Authors: Manocha, Prabhjot Singh; Kumar, Rajiv
• Is Part Of: Concurrency and computation. Volume 34:Number 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-01
• Subjects: attack detection -- block chain technology -- cluster head selection -- Internet of Things -- neighborhood‐based spider monkey optimization -- optimal routing -- optimized deep neural network -- software‐defined networking; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6861
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Internet of Things (IoT) can be connected with remote smart devices to offer global connectivity, which consists of handling, sensing, and transmission of real‐time data gathered from different devices with human intervention. The conventional security schemes are also faced with limitations like collecting and monitoring data through the IoT network to provide defense against cyber‐attacks for delivering the optimal security. To address the current challenges, this proposed model plans for integrating the energy‐efficient software‐defined networking (SDN) and block chain‐based IoT networks. The proposed model adopts a new meta‐heuristic‐based clustering protocol for optimal communication. The architecture uses the private and public block chains for offering the peer to peer communication to eliminate the proof‐of‐work (POW) issue. The contribution of a deep learning algorithm called optimized deep neural network (ODNN) is employed for detecting the malicious IoT device based on the energy characteristics of each node. Once the malicious nodes are detected, the optimal cluster head selection and optimal routing is accomplished by the adoption of neighborhood‐based spider monkey optimization (N‐SMO). The experimentation results show that the proposed model ensures the efficient cluster head selection and optimal routing protocol on the cluster structure with the traditional meta‐heuristic‐based algorithms.