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Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit. [ABSTRACT FROM AUTHOR]

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles' security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks. [ABSTRACT FROM AUTHOR]

With the advancement of cyber threats, blockchain technology has evolved to have a significant role in providing secure and reliable decentralized applications. One of these applications is a remote voting system that allow voters to participate in elections remotely. This work proposes a privacy-preserving e-voting system supporting score voting using blockchain technology. The main challenge with score voting compared to the regular yes/no voting approach is that a voter is allowed to assign a score from a defined range for each candidate. To preserve privacy, votes shall be encrypted before submission to the Blockchain, however, a malicious voter can modify the score value before encrypting it to manipulate the elections result for the favor of a certain candidate. To address this challenge, the proposed scheme allows voters to first prove that the submitted score lies in the predefined range before the vote is added to the Blockchain to ensure fairness of the election. The performance of our scheme is evaluated against a set of comprehensive experiments designed to determine optimal bounds for workload and transaction send rates and measure the impact of exceeding these bounds on critical performance metrics. The results of these simulations and their implications therefore indicate that the proposed scheme is secure while being able to handle up to 10,000 transactions at a time. [ABSTRACT FROM AUTHOR]

Wireless sensors and actuator networks (WSNs) are the physical layer implementation used for many smart applications in this decade in the form of the Internet of Things (IoT) and cyber-physical systems (CPS). Even though many research concerns in WSNs have been answered, the evolution of the WSN into an IoT network has exposed it to many new technical issues, including data security, multi-sensory multi-communication capabilities, energy utilization, and the age of information. Cluster-based data collecting in the Internet of Things has the potential to address concerns with data freshness and energy efficiency. However, it may not offer reliable network data security. This research presents an improved method for data sharing and cluster head (CH) selection using the hybrid Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method in conjunction with glowworm swarm optimization (GSO) strategies based on the energy, trust value, bandwidth, and memory to address this security-enabled, cluster-based data aggregation in the IoT. Next, we aggregate the data after the cluster has been built using a genetic algorithm (GA). After aggregation, the data are encrypted and delivered securely using the TIGSO-EDS architecture. Cuckoo search is used to analyze the data and choose the best route for sending them. The proposed model's analysis of the results is analyzed, and its uniqueness has been demonstrated via comparison with existing models. TIGSO-EDS reduces energy consumption each round by 12.71–19.96% and increases the percentage of successfully delivered data packets from 2.50% to 5.66%. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed. FL allows for performing machine learning tasks while adhering to these challenges. As with the emergence of any new technology, there will be challenges and benefits. A challenge that exists in FL is the communication costs: as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates, this can create a communication bottleneck. This paper presents the state-of-the-art of the conducted works on communication constraints of FL while maintaining the secure and smart properties that federated learning is known for. Overall, current challenges and possible methods for enhancing the FL models' efficiency with a perspective on communication are discussed. This paper aims to bridge the gap in all conducted review papers by solely focusing on communication aspects in FL environments. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoT) is a fast-growing network of Internet-connected sensing and actuating devices aimed to enhance manufacturing and industrial operations. This interconnection generates a high volume of data over the IIoT network and raises serious security (e.g., the rapid evolution of hacking techniques), privacy (e.g., adversaries performing data poisoning and inference attacks), and scalability issues. To mitigate the aforementioned challenges, this article presents, a new privacy-preserved threat intelligence framework (P2TIF) to protect confidential information and to identify cyber-threats in IIoT environments. There are two major elements in the proposed P2TIF framework. First, a scalable blockchain module that enables secure communication of IIoT data and prevents data poisoning attacks. Second, a deep learning module that transforms actual data into a new format and protects data from inference attacks using a deep variational autoencoder (DVAE) technique. The encoded data are then employed by a threat detection system using attention-based deep gated recurrent neural network (A-DGRNN) to recognize malicious patterns in IIoT environments. The proposed framework is validated using two different network data sources, i.e., ToN-IoT and IoT-Botnet. Security analysis and experimental results revealed the high efficiency and scalability of the proposed P2TIF framework. [ABSTRACT FROM AUTHOR]

Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple access (NOMA) transmission scheme with the high-altitude platform (HAP) and low-altitude platforms (LAPs) cooperated to securely provide delay-sensitive medical-care services. In the proposed scheme, we first design a novel HAP–LAP secure transmission framework to provide NOMA communication services to multiple hotspots. Constrained by the limited power and spectrum, we formulate an optimization problem, such that the privacy information delay is minimized. For thisnonconvex optimization problem, we design an alternating optimization framework, where the power, spectrum, and LAPs’ location are tackled in turn. In addition, we theoretically analyze the performance superiority compared with the orthogonal multiple access scheme and derive the secrecy outage probability closed-form expression. Finally, numerical results show the performance superiority of the proposed scheme compared with the current works with respect to the secure information delay. [ABSTRACT FROM AUTHOR]

The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack. [ABSTRACT FROM AUTHOR]

In 2017, the Government of India launched the goods and services tax (GST), referred to as "one tax, one nation, one market". This tax all Indian businesses are subject to this tax. GST was framed with the objective of bringing tax handling for all businesses onto a single platform and developing a transparent and effective system in which all businesses will pay taxes. This paper identifies and addresses GST implementation challenges and proposes a solution, GSTChain, using blockchain network technology. Currently, GST is collected at the sellers end and bifurcated between the Indian state and central governments. GSTChain is a blockchain system based on trust and autonomy with the objective of making taxpayers' lives easy and tax collection efficient and transparent for the government. [ABSTRACT FROM AUTHOR]

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The application of Artificial Intelligence (AI) based techniques has strong potential to improve safety and efficiency in data-driven Intelligent Transportation Systems (ITS) as well as in the emerging Internet of Vehicles (IoV) services. This paper deals with the practical implementation of deep learning methods for increasing safety and security in a specific ITS scenario: railway crossings. This research work presents our proposed system called Artificial Intelligence-based Surveillance System for Railway Crossing Traffic (AISS4RCT) that is based on a combination of detection and classification methods focusing on various image processing inputs: vehicle presence, pedestrian presence, vehicle trajectory tracking, railway barriers at railway crossings, railway warnings, and light signaling systems. The designed system uses cameras that are suitably positioned to capture an entire crossing area at a given railway crossing. By employing GPU accelerated image processing techniques and deep neural networks, the system autonomously detects risky and dangerous situations at railway crossing in real-time. In addition, camera modules send data to a central server for further processing as well as notification to interested parties (police, emergency services, railway operators). Furthermore, the system architecture employs privacy-by-design and security-by-design best practices in order to secure all communication interfaces, protect personal data, and to increase personal privacy, i.e., pedestrians, drivers. Finally, we present field-based results of detection methods, and using the YOLO tiny model method we achieve average recall 89%. The results indicate that our system is efficient for evaluating the occurrence of objects and situations, and it’s practicality for use in railway crossings. [ABSTRACT FROM AUTHOR]

Vehicle to vehicle (V2V) communication has gained importance in recent times because of the increasing number of traffic accidents and advancements in information sharing. A secure and reliable data transfer has become important to ensure the safety and trust of vehicular network users. Because of scalability of V2V communication, proposed solutions must have low computational complexities and free from latency issues. In this paper, we utilize the channel characteristics of wireless networks in V2V communication, which are used to generate link fingerprints. By using blockchain technology, data authentication among vehicles can be achieved in real time. The proposed algorithms are used to address the time complexity and delay issues in the Internet of Vehicles (IoV), which are lightweight and provide real time adversary detection within the network. Blockchain technology is used to generate blocks in which each hash is generated and shared with corresponding vehicles. The hash itself is not generated if an adversary affects the communication among vehicles. The Pearson Correlation Coefficient is calculated for each link and it is calculated as 0.9749 when there is no adversary and 0.1282 when an adversary is introduced into the network. The time complexity is computed as low as O(1) for the network. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent years, several tracker systems have been developed to monitor a 3-dimensional skull position for facial action whereas, various tracker systems simultaneously analyze the single sequence of video, which can be provided with low-quality cameras and less security. Initially, implementing a 2-D face detector and an unrepentance system has been suggested; furthermore, it has been improved using an integrated 3-D face initialized scheme for the real-time tracker in the present face recognition systems. OBJECTIVES: To overcome the present setbacks of the conventional systems, Face Pose Detection assisted controlled FACE++ tool position of Three-Dimensional Robot (FPLF3D) has been proposed in this article. Furthermore, the suggested proposed configuration has a high-end monitoring approach, which is used to improve the reliability of the robot's human-machine contact in the workplace environment for security assistance. Additionally, the robot's direction can be controlled by the operator's head position assessment of the camera (or any active viewing system) using a three-dimensional robot. RESULTS: Besides, the applications that are imitated by headers like telepresence, computer-generated reality, and video competitions will directly take advantage of the strategies introduced in this paper. CONCLUSION: Finally, real video tests at the lab-scale level show the accuracy and usefulness of the approaches proposed in this research outperform the existing methods used for tracking. [ABSTRACT FROM AUTHOR]

BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance. [ABSTRACT FROM AUTHOR]

Securing a wireless sensor system is a hard task for researchers today. Strengthening the authentication system before connection establishment is the right way to enhance the architecture and also to provide secured communication from eavesdroppers. In this paper, we present a novel algorithm to enhance the security of data using a hybrid model which uses an adaptive encoding technique alongside Chaotic Hopfield Neural Network. The proposed computation upgrades the security of a key shared between any nodes. Our experimental results show that the security of transmitted data is better than traditional algorithms. Moreover, we also show that the computational time for the proposed algorithm is less than known traditional algorithms. [ABSTRACT FROM AUTHOR]

The utility of virtual circuit (VC) based devices - UAVs, Drones, and similar other IoT based devices have gained immense momentum in the present day and age. These devices are predominantly used for aerial surveying in sensitive and remote areas. It is alarming that issues pertaining to stalking and information control have increased with the growth of technology. This paper presents a Blockchain Technology (BCT) based solution to improve the security and privacy of VC based device data. The proposed design is evaluated by implementing an IoT based application in a virtual vehicle monitoring system. The technical information about the instructions to the vehicle (devices), authentication, integrity, and vehicle reactions are stored in a cloud platform wherein Pentatope based Elliptic curve cryptography and SHA are used to ensure privacy in data storage. The data is later stored in an Ethereum based public blockchain to enable seamless BCT transactions. This system uses the Ganache platform for BCT that ensures data protection and privacy. Furthermore, metamask wallet for E t h balance is required to perform transactions over BCT. The proposed methodology thus helps to protect data from stalkers, plaintext attacks as well as ciphertext attacks. The results, when compared with the state-of-the-art, justify the efficiency and security aspects of the proposed approach. [ABSTRACT FROM AUTHOR]

The top priority of today's healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain. [ABSTRACT FROM AUTHOR]

The new developments in smart cyber-physical systems can be shown to include smart cities, Internet of things (IoT), and for the most part smart anything. To improve the security of sensitive personal information (SPI) in cyber-physical systems, we present some novel ideas related to the encryption of SPI. Currently, there are issues in traditional encryption methods, such as low speed of information acquisition, low recognition rate, low utilization rate of effective information resources, and high delay of information query. To address these issues, we propose a novel efficient encryption algorithm for the security of incremental SPI. First, our proposed method analyzes user information resources and determines valid data to be encrypted. Next, it uses adaptive acquisition methods to collect information, and uses our encryption method to complete secure encryption of SPI according to the acquisition results. Our experimental analysis clearly shows that the algorithm effectively improves the speed of information acquisition as well as effective information recognition rate, thus enhancing the security of SPI. The encryption model in turn can provide a strong guarantee for user information security. [ABSTRACT FROM AUTHOR]

Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some amount of modification in images and videos originally uploaded by the copyright holders such as photographers, graphic designers, and video providers. Copyright violators, who are not the original creators of multimedia content modify them using image processing and frame modification techniques such as grayscale conversion, cropping, rotation, frame compression, and frame speed manipulation. Then, upload the tampered images and videos. To address this problem, we propose an IPFS-based (InterPlanetary File System-based) decentralized peer-to-peer image and video sharing platform built on blockchain technology. We use a perceptual hash (pHash) technique to detect copyright violations of multimedia. When multimedia is to be uploaded to the IPFS, the pHash of the same content is determined and checked against existing pHash values in the blockchain network. Similarity with existing pHash values would result in the multimedia being detected as tampered with. Blockchain technology offers the advantage of non-involvement of a third party and consequently the avoidance of a single point of failure. • We present a platform of blockchain and IPFS to store multimedia objects as transactions. • The proposed framework ensures multimedia availability, immutability, and transparency. • The proposed approach is fully distributed where copyright information can be verified. [ABSTRACT FROM AUTHOR]

Due to the drastic increase in the number of vehicles, searching for available parking spots has become a major problem for drivers, especially in crowded and big cities. Thus, a smart parking system is crucial not only to facilitate drivers' ability to find parking spaces, but also to reduce traffic congestion, pollution, and vehicles' gas consumption. A growing number of public parking lots have been constructed to provide parking spots for drivers. Meanwhile, homeowners and landowners leave their private spaces that can be utilized and rented for other drivers seeking parking spaces. This paper proposes a decentralized smart private-parking scheme built on top of Blockchain. Blockchain is used to run and organize the developed parking scheme to help drivers find nearby parking spots owned by private homeowners and landowners. However, parking owners and drivers should send requests/offers that include the location/time information of spots and/or desired locations to park at. This information can be used to track daily live activities for both drivers and parking owners such as whether a parking owner is at home or not, drivers' destinations, etc. To preserve privacy, in the proposed scheme, drivers/parking owners submit their requests/offers in an encrypted format, and the Blockchain can find feasible matches among the encrypted requests and offers without the need to get access to original sensitive information. In addition, an overlapping and partitioning technique is used to generate the requests/offers to increase matching results by finding more nearby drivers to park and thus maximizing parking owners' profits. Finally, the proposed scheme has been implemented on Hyperledger Blockchain and extensive evaluations have been conducted using different simulation scenarios. The results indicate that the scheme is efficient in terms of scalability and throughput under different case scenarios. Also, security and privacy analysis are conducted to demonstrate that the scheme can run the services securely while ensuring drivers/parking owners' privacy. • Propose a smart private-parking system built on Blockchain to ensure security and reliability. • Data from drivers and parking owners is encrypted using a lightweight matching technique. • An overlapping and partitioning technique is used so that owners can maximize their profits. • Caliper framework used on test cases and results indicate efficiency and scalability with low latency. [ABSTRACT FROM AUTHOR]