In the coming years, massive amounts of data are likely to be traded in vehicle-to-grid (V2G) networks to enhance traffic efficiency and safety through vehicular communications. However, several challenges need to be addressed before realizing the full potential of V2G networks. These challenges include the privacy-preservation of users, secure caching, scalability in deep environments, unreliability in high mobility events, and low efficiency in large networks. To overcome these challenges in V2G networks, named data networking (NDN) offers a good solution. It provides a new future Internet architecture: “named content-based” rather than “host addresses.” The main focus of NDN in V2G networks is to provide data availability, network performance, data retrieval, and data distribution. However, the presence of NDN in V2G networks introduces several issues like privacy and trust among vehicular nodes. Hence, this article proposes a system model based on blockchain technology in NDN-enabled V2G networks. This model provides secure and fast named content searching and enhances the trust among vehicular nodes. The simulation results show that the block propagation latency of NDN-based blockchain is less than the IP-based blockchain systems. In addition, the performance of the proposed scheme outperforms an existing scheme. The reason is to use the proof-of-authority consensus mechanism compared to proof of work, which uses high mining computation power. Furthermore, the proposed scheme increases the trust and transparency in NDN-enabled V2G networks.

Vehicle Road Cooperation Systems (VRCS) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Internet of Vehicles (IoV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. At the same time, cyber attacks have become more complex, persistent, organized, and weaponized in IoV network. Threat Intelligence (TI) has emerged as a prominent security approach to obtain a complete view of the dynamically growing cyber threat environment. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs requires a large investment of resources and use hand-crafted features, making the entire process error-prone and time-consuming. To tackle these challenges, this paper presents TIMIF, a deep-learning-based threat intelligence modeling and identification framework for Intelligent IoV and is based on three key modules: first, the proposed TIMIF adopts an Automated Pattern Extractor (APE) module to extract hidden patterns from IoV networks. Employing its output, we design a TI-Based Detection (TIBD) module to detect abnormal behavior and TI-Attack Type Identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources namely HCRL-car hacking, ToN-IoT and CICIDS-2017 to illustrate the utility of TIMIF framework over some commonly used baselines and state-of-the-art techniques.

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

Recent advancements in the Internet of Vehicles (IoV) technology have pathed the way for the use of various smart services for the management of urban traffic, including authentication and key management. Key management protocols are an important means of addressing security and privacy concerns. However, they can be resource-intensive in terms of network traffic and workload management, particularly at times of traffic congestion, which in turn can increase the ECU and RSU processing load and adversely impact network communications. This paper introduces a more efficient key management method, named AI-enabled and Layered Key Management (ALKM), which uses an Artificial Intelligence (AI) approach and a layered workflow to reduce network traffic and workload. Specifically, the ALKM distributes dynamic synchronous time-dependent keys among Road Side Units (RSUs) rather than static cryptographic keys. It provides three layers of secure communications: public, tunnel, and hierarchy. The public layer creates a flat secure layer between the Traffic Management Center (TMC) and all RSUs. Using AI-enabled features, the tunnel layer predicts short-term and long-term congestion areas by analysis of the acquired trajectory data, and then establishes a secure communication channel between the selected RSUs and the TMC. In the hierarchy layer, in multiple tiers, the TMC and higher-level RSUs assist lower RSUs in message decryption (but not vice-versa). Our extensive analysis shows that the ALKM generates overall between 48% and 99% less network traffic per generated key than the Master Key method, depending on the operational lifetime of the keys used.

Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.

Smart grids are an improvement of the traditional electric grids. They allow a much higher degree of automation and more efficient power distribution. Nonetheless, due to automation, these grids become more vulnerable to cyber attacks. Hence, cyber security becomes a major milestone to overcome before we can permanently shift to smart grids. Electric theft is one of the most dangerous cyber attacks in a smart grid. It allows users to lie about their load profiles and decrease their electricity bills. Several research studies have been conducted regarding the detection of such cyber attacks in a smart grid, but none of them consider weather information as a feature. This paper proposes a novel machine learning-based approach to smart grid electricity theft detection using both the load profile of a household and the weather features. The results show that our current approach using both load and weather information perform much better than previous approaches that only use load information.

Recognizing human activities is considered a vital research challenge because of its essential significance for improving human–machine collaboration in the Internet of Things environments. The present deep learning (DL) literature focused on studying human activities (HAs) from one subject, with several schemes differing in the recognition method and sensing strategy. However, few research interests have been dedicated to situations where numerous individuals interact to perform common activities. This challenge is termed human-to-human interaction (H2HI) recognition. This study addresses the H2HI problem by a novel device-free DL model, named H2HI-NET, for modeling the HA representation of the Channel State Information of Wireless Fidelity devices. In H2HI-NET, a bi-directional temporal learning module is introduced to capture temporal representation from historical and future information. Simultaneously, the residual spatial learning module is designed to combine residual learning and transformer network capabilities for the efficient extraction of complex spatial features of HAs. The experimental evaluations reveal the efficiency of the H2HI-NET with 96.39% accuracy overcoming cutting-edge studies.

Algebraic structures are well studied mathematical structures in abstract algebra with applications in many fields of computer security such as cryptography and authentication. Generating such structures is computationally very expensive because of the huge number of permutations. Also, many of these permutations are redundant as they are symmetrically equivalent. The symmetry breaking (finding symmetrically equivalent structures) is also a computationally challenging task. In this paper, we present a mining based approach for symmetry breaking in algebraic structures. The approach reduces the number of redundant structures by identifying rules based on recurring patterns in the previously known structures. These rules are then used as constraints in a leading constraint solver (Google's or-tools). When applied to IP loop, a special class of algebraic structures, these rules reduced the number of redundant solutions resulting in significant time improvement.