Access control (AC) is an important security parameter in industrial networks; a mismanaged AC system leads to security breaches. The existing security solutions significantly consider the AC methods in the Industrial Internet of Things (IIoT); however, falsified identity can bypass the secure AC system. Thus, a centralized AC method leads to risks for data security. We are the first to address the risk factors of granted access in an industrial environment and present a risk-adaptive AC framework for IIoT. Our proposed solution framework uses blockchain to provide secure decentralized AC in the industrial environment with privacy-preserved multiparty data sharing. We name our framework 'adaptive AC for multiparty data computation in industrial decentralization (AALMOND).' AALMOND uses lightweight cryptographic operations to reduce the complexity of the execution and loosen up the tight bounds on resource-constrained industrial devices. Further, the risk-adaptive AC in AALMOND provides a better security analysis of the multiparty sharing data. Our framework uses role-based, attribute-based, and organization-based ACs to map the assets for risk calculation. We put all the required policies in a smart contract for the ease of multiparty data sharing to obtain a transparent AC execution more suitably. We also pioneer in the calculation of the risk adaptivity of AALMOND considering the National Institute of Standards and Technology recommendations of operation risk, security risk, and heuristic risk. We measure the performance of AALMOND with state-of-the-art frameworks based on throughput, latency, complexity analysis, and risk adaptivity factors. We find that AALMOND is efficient for IIoT as it shows 24% reduced latency and 20% better throughput as compared to the other existing models.

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system. In this paper, we introduce a comprehensive ML-based Network Intrusion Detection System (NIDS) against DDoS attacks in vehicular networks. Our proposed NIDS combines a three-tier security model, traffic adaptivity, and heterogeneity traffic provisions. We call our model Vehicular Adaptive Intrusion Detection And Novel System for Heterogeneous Hosts (VAIDANSHH). As mentioned earlier, VAIDANSHH has a three-tier security system: at RSU's hardware, communication channel, and RSU application level. VAIDANSHH combines the Adaptive Alarming Module (AAM) and the Detection Module (DM) for data generation, collection of generated data, flow monitoring, pre-processing, and classification. We use the NS3 simulation tool for our experiments to generate synthetic data and apply ML with WEKA. We run a thorough set of experiments, which show that VAIDANSHH detects UDP flooding, a form of DDoS attack, with 99.9% accuracy within a very short time. We compare VAIDANSHH with other state-of-the-art models; the comparative analysis shows that VAIDANSHH is superior in terms of accuracy and its multi-tier workflow.

The progress of sensor devices, digital communication, and computing techniques enhances the functionalities of e-healthcare, where the medical facilities are manageable based on the Internet. Now a days, e-healthcare not only helps in disease diagnosis, or tele-medicine, but also implies to general medical scope and medical resource management. Thus, Internet-of-Medical Thing (IoMT) becomes an umbrella term to connect e-healthcare, tele-medicine, and medical supply chain management. In the recent past, the outbreak of COVID-19 pandemic has shown the loopholes in the existing medical systems such as the mismanagement of medical resources and the unavailability of the basic requirements for patients. The medical infrastructure urges for the new frameworks to handle accountability and transparent governance in a medical emergency situation. This motivates us to address medical resource accountability in e-healthcare. In the present work, we introduce the first resource accountability framework to balance the demand–supply of medical facilities in an e-healthcare and IoMT ecosystem. Our solution is based on blockchain and we call it bloCkchained framework for resOUrce accouNTability (COUNT). We use a customized Proof of Vote (PoV) for consensus in COUNT. We call this consensus COUNT-PoV, which is another direction of novelty in our solution. The blocks in the proposed COUNT contain the resource requirements and their availability–production–supply status. Multiple stakeholders are involved in the process based on providing pre-attained credentials; thus, COUNT supports consortium blockchain. Existing literature shows a number of studies for blockchain-based frameworks for e-healthcare; however, those works do not address the accountability and transparency issues of medical e-governance and balancing the demand–supply of the medical facilities/resources. Therefore, our proposed COUNT is beneficial for e-healthcare and IoMTs. We use signcryption process to reduce the complexity of the cryptographic processes, which is an add-on to the contribution. We evaluate our proposed framework based on the Hyperledger Caliper benchmark test with latency, throughput, and resource consumption. Additionally, we also analyse the cost of the implementation. The comparative analysis of our consensus with other stake-based consensus protocols on the COUNT framework shows that our consensus, COUNT-PoV is efficient and suitable for the use of e-healthcare and IoMTs. Moreover, being a generic framework, COUNT is helpful for the e-governance of medical facilities, the vaccination process, and COVID passports.

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.