Summary With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can communicate with each other and share data easily. However, the IoT paradigm is prone to security concerns as many attackers try to hit the network and make it vulnerable. In this scenario, security concerns are the most important and to address them various models have been designed to overcome these security issues, but still there exist many emerging variants of botnet attacks such as Mirai, Persirai, and Bashlite that exploits the security breaches. This research article aims to investigate cyber security in the advent of B‐IDS, DDOS, and malware attacks. For this purpose, different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection. For performance measures, these algorithms have been tested on nine sensor devices over N‐BaIoT datasets to measure the security and accuracy of the intrusion detection system. The results show that the tree‐based algorithm achieved more than 99% accuracy which is quite higher as compared to other tested methods on the same sensor devices.

A distributed power system operation and control node privacy and security are attractive research questions that deliver electrical energy systems to the participating stakeholders without being physically connected to the grid system. The increased use of renewable energy in the power grid environment creates serious issues, for example, connectivity, transmission, distribution, control, balancing, and monitoring volatility on both sides. This poses extreme challenges to tackle the entire bidirectional power flow throughout the system. To build distributed monitoring and a secure control operation of node transactions in the real-time system that can manage and execute power exchanging and utilizing, balancing, and maintaining energy power failure. This paper proposed a blockchain Hyperledger Sawtooth enabling a novel and secure distributed energy transmission node in the EPS-ledger network architecture with a robust renewable power infiltration. The paper focuses on a cyber–physical power grid control and monitoring system of renewable energy and protects this distributed network transaction on the blockchain and stores a transparent digital ledger of power. The Hyperledger Sawtooth-enabled architecture allows stakeholders to exchange information related to power operations and control monitoring in a private ledger network architecture and investigate the different activities, preserved in the interplanetary file systems. Furthermore, we design, create, and deploy digital contracts of the cyber–physical energy monitoring system, which allows interaction between participating stakeholders and registration and presents the overall working operations of the proposed architecture through a sequence diagram. The proposed solution delivers integrity, confidentiality, transparency, availability, and control access of the distribution of the power system and maintains an immutable operations and control monitoring ledger by secure blockchain technology.

Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Due to globalization and worldwide connectivity, multimedia data exchange has increased significantly over the Internet in the last decade. The life cycle of multimedia content is also getting more multifaceted as more people are accessing, sharing, modifying and re-using multimedia information. This poses serious challenges for the multimedia industry to provide integrity, reliability and trustworthiness for multimedia investigations against the growing cybersecurity threats. This paper bridges this gap by enabling a secure and transparent digital forensic investigations process using blockchain technology. MF-Ledger a Blockchain Hyperledger sawtooth-enabled novel, secure and efficient digital forensic investigation architecture is proposed where participating stakeholders create a private network to exchange and agree on different investigation activities before being stored on the blockchain ledger. We have created digital contracts (smart contracts) and implemented them using sequence diagrams to handle the stakeholders' secure interaction in the investigation process. The proposed architectural solution delivers robust information integrity, prevention, and preservation mechanism to permanently and immutably store the evidence (chain of custody) in a private permissioned encrypted blockchain ledger.

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