PurposeNowadays, more interest is found among the researchers in MANETs in practical and theoretical areas and their performance under various environments. WSNs have begun to combine with the IoT via the sensing capability of Internet-connected devices and the Internet access ability of sensor nodes. It is essential to shelter the network from attacks over the Internet by keeping the secure router.Design/methodology/approachThis paper plans to frame an effective literature review on diverse intrusion detection and prevention systems in Wireless Sensor Networks (WSNs) and Mobile Ad hoc NETworks (MANETs) highly suitable for security in Internet of Things (IoT) applications. The literature review is focused on various types of attacks concentrated in each contribution and the adoption of prevention and mitigation models are observed. In addition, the types of the dataset used, types of attacks concentrated, types of tools used for implementation, and performance measures analyzed in each contribution are analyzed. Finally, an attempt is made to conclude the review with several future research directions in designing and implementing IDS for MANETs that preserve the security aspects of IoT.FindingsIt observed the different attack types focused on every contribution and the adoption of prevention and mitigation models. Additionally, the used dataset types, the focused attack types, the tool types used for implementation, and the performance measures were investigated in every contribution.Originality/valueThis paper presents a literature review on diverse contributions of attack detection and prevention, and the stand of different machine learning and deep learning models along with the analysis of types of the dataset used, attacks concentrated, tools used for implementation and performance measures on the network security for IoT applications.

Mobile ad-hoc networks (MANETs) play an important role in the future of the industrial internet-of-things communication, where smart devices will be connected in a completely distributed manner. In the digital properties owing to the digital data properties, there exist difficulties in directly applying the encryption schemes to the one-dimension data. Thus, it is necessary to develop secure lightweight key frame extraction technique for improving privacy in the e-healthcare. This paper plans to develop the robust and reliable security protocol in MANET IoT application. A chaotic cryptography-based privacy preservation model is proposed in this paper for the purpose of improving the security in the MANET IoT. The key generation process in the chaotic map is optimized by generating optimal key pairs through the newly developed SA-SFO algorithm. The key selected from the chaotic map is influenced by selecting the optimal parameters through the proposed Self Adaptive Sail fish Optimization (SA-SFO). Finally, the experimental analysis is conducted, where for the case of character length as 100; the proposed SA-SFO eventually surpassed the existing ones with the cost function 22% as higher than PSO, 20% higher than GWO, 19% higher than WOA, and 21% higher than SFO respectively. The comparative analysis over the conventional models ensures the efficient performance of the proposed model in terms of diverse analysis in MANET and IoT platform.

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