In recent studies, vehicular networks have been considered as a promising solution to achieve better traffic management and to improve the driving experience of drivers. Dedicated Short Range Communications is specifically designed for V2X communications, and recently the cellular network has shown great potential to support V2X with better performance and more applications. Due to the wireless nature of V2X communications, how to secure V2X communications and guarantee the privacy of users are great challenges which have hampered the implementation of vehicular services. Many solutions have been proposed by researchers in last two decades. In this paper, we present a comprehensive survey on the state-of-the-art solutions concerning security and privacy for V2X communications. For security, detailed discussions on cryptography based schemes and trust based schemes are provided. For privacy, we summarize and compare general solutions in preserving identity privacy and location privacy. Cellular based V2X communications have shown many advantages over DSRC, and the oncoming fifth generation cellular technology are going to provide more possibilities to V2X. Thus, security architectures and solutions for cellular based communications are also illustrated and discussed. Finally, we summarize the remaining challenges and point out future research directions.

Vehicular networks provide various applications for vehicles to improve road safety and traffic efficiency as well as infotainment. Security and privacy are of great important for the deployment of vehicular networks. However, how to efficiently secure vehicular networks with privacy-preserving remains a big challenge. Many solutions have been proposed in the past decade, but most of them either rely too much on the ideal tamper-proofed devices or not being efficient enough for scenarios with high vehicle density. Meanwhile, in recent years, great progresses have been made in both the Long-Term Evolution (LTE) and the fifth-generation (5G) wireless network based vehicular networks. The 5G enabled vehicular networks are envisioned to support higher data transmission rate and a larger number of connected devices than the 802.11p and LTE based networks. Noting the great potential of 5G technology, in this paper we explore a new architecture of 5G software defined vehicular network and propose a secure and efficient privacy preserving authentication scheme for vehicular networks. The proposed scheme uses elliptic-curve public-key cryptography and a registration list to achieve efficient message authentication and to avoid the usage of ever-growing certificate revocation list. The security analysis shows that the proposed scheme has strong security guarantees without using the ideal tamper-proofed devices. Simulation results exhibit that the proposed scheme has ultra-low computational overhead and packet loss ratio.

Despite continuous advancements in defense mechanisms, attackers often find ways to circumvent security measures. Windows operating systems, in particular, are vulnerable due to fewer restrictions on downloading software from unknown sources, facilitating the spread of malware. To address this challenge, researchers have focused on developing techniques to identify Windows malware, crucial for mitigating potential damage. Traditional approaches typically categorize threats into broad classes such as trojans or adware, often failing to capture the full spectrum of malicious behaviors exhibited by diverse malware variants. In response, we propose a novel approach to malware categorization that incorporates both the general malware family and subfamily for each sample. Our method leverages self-similarity techniques to extract local semantics and similarities within the basic blocks of malware binaries while preserving correlations between these blocks. We utilize a VGG11 model to capture these features, enabling accurate classification. Central to our approach is the conversion of malware binaries into self-similarity descriptors, facilitating space savings while capturing essential semantics within basic blocks. By focusing on local self-similarities and their geometric layouts across malware, our method effectively identifies repetitive patterns indicative of malware behavior. Our proof-of-concept implementation demonstrates the effectiveness of our framework, achieving an impressive average precision of 98.2% on a newly gathered dataset with over 25,000 samples. Moreover, our method offers significant space savings, outperforming recent research efforts by a factor of over 96. These results underscore the efficacy of incorporating self-similarities and correlations within basic blocks for robust malware classification, making our approach a promising solution for real-world malware detection and prevention.

Sophisticated malware families exploit the openness of the Android platform to enable large-scale disruption, data exfiltration, and denial-of-service attacks, including the infiltration of IoT infrastructures. This systematic literature review examines cutting-edge approaches to Android malware analysis, with implications for securing resource-constrained environments. We analyze feature extraction techniques across static, dynamic, hybrid, and graph-based methods, highlighting their respective trade-offs. Static analysis offers efficiency but is easily circumvented through obfuscation, whereas dynamic analysis provides stronger resistance to evasive behaviors at the cost of higher computational overhead, often unsuitable for lightweight devices. Hybrid approaches aim to balance accuracy with resource efficiency, while graph-based methods deliver enhanced semantic modeling and adversarial robustness. This survey provides a structured comparison of existing techniques, identifies open research gaps, and outlines a roadmap for future work to improve scalability, adaptability, and long-term resilience in Android malware detection.

The widespread usage of Microsoft Windows has unfortunately led to a surge in malware, posing a serious threat to the security and privacy of millions of users. In response, the research community has mobilized, with numerous efforts dedicated to strengthening defenses against these threats. The primary goal of these techniques is to detect malicious software early, preventing attacks before any damage occurs. However, many of these methods either claim that packing has minimal impact on malware detection or fail to address the reliability of their approaches when applied to packed samples. Consequently, they are not capable of assisting victims in handling packed programs or recovering from the damages caused by untimely malware detection. To address these challenges, we propose VisUnpac, a static analysis-based data visualization framework for bolstering attack prevention while aiding recovery post-attack by unveiling malware patterns and offering more detailed information including both malware class and family. Our method includes unpacking packed malware programs, calculating local similarity descriptors based on basic blocks, enhancing correlations between descriptors, and refining them by minimizing noises to obtain self-analysis descriptors. Moreover, we employ machine learning to learn the correlations of self-analysis descriptors through architectural learning for final classification. Our comprehensive evaluation of VisUnpac based on a freshly gathered dataset with over 27,106 samples confirms its capability in accurately classifying malware programs with a precision of 99.7%. Additionally, VisUnpac reveals that most antivirus products in VirusTotal can not handle packed samples properly or provide precise malware classification information. We also achieve over 97% space savings compared to existing data visualization based methods.

Modern software systems are developed in diverse programming languages and often harbor critical vulnerabilities that attackers can exploit to compromise security. These vulnerabilities have been actively targeted in real-world attacks, causing substantial harm to users and cyberinfrastructure. Since many of these flaws originate from the code itself, a variety of techniques have been proposed to detect and mitigate them prior to software deployment. However, a comprehensive comparative study that spans different programming languages, program representations, bug types, and analysis techniques is still lacking. As a result, the relationships among programming languages, abstraction levels, vulnerability types, and detection approaches remain fragmented, and the limitations and research gaps across the landscape are not clearly understood. This article aims to bridge that gap by systematically examining widely used programming languages, levels of program representation, categories of vulnerabilities, and mainstream detection techniques. The survey provides a detailed understanding of current practices in vulnerability discovery, highlighting their strengths, limitations, and distinguishing characteristics. Furthermore, it identifies persistent challenges and outlines promising directions for future research in the field of software security.