With the global energy crisis, increasing demand, and a national-level emphasis on electric vehicles (EVs), numerous innovations are being witnessed throughout the EV industry. EVs are equipped with sensors that maintain a sustainable environment for the betterment of society and enhance human sustainability. However, at the same time, as is the case for any new digital technology, they are susceptible to threats to security and privacy. Recent incidents demonstrate that these sensors have been misused for car and energy theft, financial fraud, data compromise, and have caused severe health and safety problems, amongst other things. To the best of our knowledge, this paper provides a first systematic analysis of EV sustainability, digital technologies that enhance sustainability, their potential cybersecurity threats, and corresponding defense. Firstly, three robust taxonomies have been presented to identify the dangers that can affect long-term sustainability domains, including (1) life and well-being, (2) safe environment, and (3) innovation and development. Second, this research measures the impact of cybersecurity threats on EVs and correspondingly to their sustainability goals. Third, it details the extent to which specific security controls can mitigate these threats, thereby allowing for a smooth transition toward secure and sustainable future smart cities.

The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of concern. This is because AI language models are far from neutral or objective; they inherit biases from their training data and the individuals who design and utilize them, which can sway voter decisions and affect global elections and democracy. In this research paper, we explore how AI can directly impact election outcomes through various techniques. These include the use of generative AI for disseminating false political information, favoring certain parties over others, and creating fake narratives, content, images, videos, and voice clones to undermine opposition. We highlight how AI threats can influence voter behavior and election outcomes, focusing on critical areas, including political polarization, deepfakes, disinformation, propaganda, and biased campaigns. In response to these challenges, we propose a Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time. It leverages the transparency of blockchain technology to reinforce electoral integrity. Finally, we also propose comprehensive countermeasures, including enhanced legislation, technological solutions, and public education initiatives, to mitigate the risks associated with AI in electoral contexts, proactively safeguard democracy, and promote fair elections.

There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including safe access and data privacy, which are necessary with the advent of Metaverse technology. Traditional, centralized, and network-centered solutions fail to provide a resilient identity management solution. There are multifaceted security and privacy issues that hinder the secure adoption of this game-changing technology in contemporary cyberspace. Moreover, there is a need to dedicate efforts towards a secure-by-design Metaverse that protects the confidentiality, integrity, and privacy of the personally identifiable information (PII) of users. In this research paper, we propose a logical substitute for established centralized identity management systems in compliance with the complexity of the Metaverse. This research proposes a sustainable Self-Sovereign Identity (SSI), a fully decentralized identity management system to mitigate PII leaks and corresponding cyber threats on all multiverse platforms. The principle of the proposed framework ensures that the users are the only custodians and proprietors of their own identities. In addition, this article provides a comprehensive approach to the implementation of the SSI principles to increase interoperability and trustworthiness in the Metaverse. Finally, the proposed framework is validated using mathematical modeling and proved to be stringent and resilient against modern-day cyber attacks targeting Metaverse platforms.

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand for and usage of Virtual Private Networks (VPNs), which permit remote employees to access corporate offices effectively. However, the technology raises concerns, including security threats, latency, throughput, and scalability, among others. These newer-generation threats are more complex and frequent, which makes the legacy approach to security ineffective. This research paper gives an overview of contemporary technologies used across enterprises, including the VPNs, Zero Trust Network Access (ZTNA), proxy servers, Secure Shell (SSH) tunnels, the software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZT-VPN), which is a VPN solution based on Zero Trust principles. The proposed framework aims to enhance IT security and privacy for modern enterprises in remote work environments and address concerns of latency, throughput, scalability, and security. Finally, this paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZT-VPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape.

This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose significant challenges in various domains, including social media, politics, and entertainment. Current methodologies primarily rely on visual features that are specific to the dataset and fail to generalize well across varying manipulation techniques. However, these techniques focus on either spatial or temporal features individually and lack robustness in handling complex deepfake artifacts that involve fused facial regions such as eyes, nose, and mouth. Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. The research incorporates data augmentation with GANs to enhance model performance and proposes an innovative fusion of artifact inspection and facial landmark detection for improved accuracy. The experimental results show near-perfect detection accuracy across diverse datasets, demonstrating the effectiveness of these models. However, challenges remain, such as the difficulty of detecting deepfakes in compressed video formats, the need for handling noise and addressing dataset imbalances. The research presents an enhanced hybrid model that improves detection accuracy while maintaining performance across various datasets. Future work includes improving model generalization to detect emerging deepfake techniques better. The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.