Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients.

The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain.

Internet of things (IoT) is being applied in every aspect of daily living to improve the quality of life. Waste management is a critical issue, especially for developing countries. The purpose of this study is to propose an IoT-driven state-of-the-art system for solid waste management for developing countries and urban areas. The lack of planning and availability of resources in urban areas have made a living in densely populated surroundings even more difficult. This research work provides a framework based on blockchain-enabled vehicular ad-hoc networks (VANETs) in the realm of IoT. The step-by-step methods are proposed for the decentralized solution for solid waste management using blockchain-enabled VANET. Advanced ultra-high frequency (UHF) technology is used along with IoT devices for the real-time location of waste vehicles and the detection of waste bins. Geo-fencing techniques are also applied for the monitoring and timely collection of waste from the dump spots. Lastly, blockchain technology is applied in the proposed solution to make machine-to-machine (M2M) communication more secure, reliable and trustworthy across IoT devices. The experimental results are also obtained by deploying a pilot project in Karachi, Pakistan. The real-time dashboard is also obtained to demonstrate the daily statistics of the waste collection and performance of the waste vehicles. The results indicate the successful implementation of SSWMS to achieve real-time tracking, intelligent identification of waste bins, trash weighing, and keeping tabs on waste collection from dump spots using geofencing. In future, the blockchain-enabled VANETs can be applied for route management, intelligent transportation and fleet management systems (FMS) due to the inherent characteristics of blockchain and IoT.

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge IIoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.