In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision.@en

The energy conservative extension of the Internet of Things (IoT), green IoT, is a revolutionary approach in connecting people, processes, and things in an energy-efficient way. The existing research works in the domain of green IoT forbid the use of decentralized management (e.g., blockchain) of data due to its intrinsic disadvantages of block mining, transaction incentives, and less throughput. However, the advantages of blockchains urge the development of new decentralized strategies utilizing the architecture of green IoT. The infancy stage of the conjunction between green IoT and blockchains, and the need of decentralization in energy management in green IoT motivate us for the present research. In this article, we address the problems of decentralization, energy conservation, and privacy simultaneously. We introduce the first blockchain-based privacy-preserving framework for green IoT. We name this framework 'Blockchain-based energy-efficient and privacy-preserving data management scheme for GREEN-iot (BENIGREEN)' for smart cities. BENIGREEN uses weight metrics for energy-efficient cluster heads (CHs) selection. The use of weight metrics is a novel contribution in the field of green IoT. Furthermore, we integrate a decentralized blockchain framework with an authentication scheme for secure transmission among base station (BS) and sensor nodes by employing registration, certification, and revocation phases. Consequently, BS allocates the collected information from CHs to decentralized blockchain and cloud storage. The BS eliminates all malicious nodes from the network by employing a certificate revocation process. We execute thorough experiments in terms of the operation time, throughput, average energy consumption, and computational latency. The comparative analysis with the state-of-the-art schemes show that BENIGREEN is efficient for IoT paradigm.@en

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for 'Industry 4.0.' Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we propose DHACS, a Decentralized Hybrid Access Control for Smart contract, for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs.@en

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL’s accuracy and energy saving mechanism make it efficient for IIoTs.@en

Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices' connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm. @en

Internet of Things (IoT) composed of large number of sensing devices with a variety of features applicable for various applications. In such scenarios, due to low data handling capabilities, limited storage, and security aspects, it is quite challenging to protect networks against illegal information access and utilizes storage efficiently. Though researchers provide various solutions for security and data storage, but a few solutions are appropriate for wireless sensor networks (WSNs)-enabled IoTs. Therefore, a blockchain-based decentralized framework integrated with authentication and privacy-preserving schemes is developed for the secure communication in WSNs-enabled IoTs. Registration, certification, and revocation process are employed for the communication with sensor nodes and base station (BS) in a cloud computing environment. In this scheme cluster heads forward the collected information to the BS. Consequently, BS records all the key parameters on the distributed blockchain and large data is forwarded to clouds for the storage. The revoked certificates of all malicious nodes are eliminated from blockchain by BS. The performance of the proposed scheme is scrutinized in terms of detection accuracy, certification delay, computational, and communicational overheads. The simulated results, comparative analysis, and security validation support the superiority of the proposed solution over the existing approaches. @en

Blockchain is an emerging decentralized and distributed technology. Along with the beneficial features of decentralization, transparency, and security the consensus algorithms of blockchains form key building blocks for this technology. Consensus protocol/algorithm helps to provide a decentralized decision making process. An efficient consensus algorithm is inclusive that engages all the participants to make their decision based on the conflicts of the blockchain networks. These consensus decisions lead to better quality outcomes of the blockchains and help to obtain the finality. Rigorous research is in process to upgrade or optimize the existing consensus protocols. The optimized or enhanced consensus protocols objectify to be suitable for Internet-of-Thing (IoT) as the current versions of the protocols are not suitable for the resource-constrained environments due their complexity, hard configurations, mining techniques, high resource consumption, and explicit security loophole. In this paper, we present a survey of consensus protocols with a purpose to identify and discuss the existence of various consensus protocols available in literature. We emphasize on the genesis of the consensus protocols, particularly for Proof-of-X, byzantine fault tolerance, Paxos, and RAFT; we also include Directed Acyclic Graph (DAG) orientation of some contemporary algorithms. We discuss the variants of these genesis protocols. Our survey analyzes the advantages, disadvantages, and their applicability in IoTs. We enlist the categorical use of consensus algorithms in blockchains and other applications. Finally, we present several research trends and open issues emphasizing for consensus protocols emphasizing on IoTs. Compared to the other surveys in the field, our present survey objectifies to provide a more thorough summary of the most relevant protocols and application issues; this survey helps the researchers and the application developers to obtain an insight on the current status of the consensus protocols’ suitability to deliver the desired functionalities in IoTs. The notified disadvantages of each of the protocol provide future scope for the industries and academia. To the best of our knowledge, such a comprehensive and summarized survey of consensus protocols including DAG-based protocols is unavailable in the literature and thus, our contribution claims are significant.@en