The Internet-of-Vehicles (IoV) has gained significant attention from both academia and industry, driven by its potential to enhance road safety and traffic efficiency. To fully realize this potential, securing IoV communications is essential, where detecting malicious Basic Safety Messages (BSMs) is a key challenge. To this end, machine learning (ML) has been widely employed to design Misbehavior Detection Systems (MDSs) for identifying manipulated BSMs. However, the existing MDSs mainly focus on improving detection accuracy, with little attention given to time efficiency. As safety in IoV is time-sensitive, designing a time-efficient MDS is urgently necessary. In this work, we propose BatchMDS, a time-efficient MDS using Convolutional Neural Network (CNN) and data-to-image transformation. By processing multiple BSMs simultaneously rather than individually, BatchMDS significantly reduces detection latency. Furthermore, this work proposes a novel combination of a sliding window mechanism and a Learned Feature Cache (LFC) to eliminate redundant computation during continuous detection. This work conducts extensive simulation over the VeRiMe dataset that contains five attacks. The experimental results demonstrate the effectiveness of the proposed BatchMDS, improving time efficiency of all attacks by at least 50% while remaining remarkable detection accuracy (≥ 97%).

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 proposeVisUnpac, 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 ofVisUnpacbased 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,VisUnpacreveals 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.

Basic Safety Messages (BSMs) exchanged among vehicles and roadside units through vehicular communications can significantly enhance road safety and improve traffic efficiency. Protecting the integrity of BSMs, which are transmitted wirelessly in plaintext, is critical for the proper operation of vehicular networks. As a result, various machine learning-based misbehavior detection systems have been proposed to identify corrupted BSMs. Recent studies have applied federated learning methods to further preserve user privacy while facilitating detection model updates. However, supervised federated learning cannot be directly applied since BSMs received by vehicles are unlabeled. In this paper, we propose a semi-supervised federated learning framework that enables the federated training process between vehicles and the server without transmitting any datasets. Our experimental results show that the performance of the proposed semi-supervised framework is very close to the centralized method, while preserving user privacy and reducing communication costs.

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 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 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 blocks for robust malware classification, making our approach a promising solution for real-world malware detection and prevention.

As the key element of the Intelligent Transportation System, Internet of Vehicles (IoV) is expected to reduce the traffic congestion and improve the road safety. For road safety, connected vehicles need to broadcast basic safety messages frequently, which contain location, speed, acceleration, steering information, etc. Due to the wireless environment and ad-hoc nature, the vehicular communications are vulnerable to various attacks. It is vital to protect the correctness of the exchanged messages, in which the misbehavior detection mechanisms play an important role. In this paper, we propose a data-centric misbehavior detection method based on a multi-objective learning model. Different from current machine learning based misbehavior detection system, our work exploits a new direction to improve the detection performance by maximizing the recall and specificity. To show the effectiveness of the proposed model, we conduct experiments on the VeReMi Dataset and compare the detection results with existing approaches.

Vehicular networks are expected to significantly improve the efficiency and safety of transportation system. The number of connected vehicles is estimated to exceed 200 millions by 2020. With the rapid developed technologies such as the fifth generation cellular networks, various real-time network applications will be applied in vehicular networks. Considering the limited capability of devices installed on vehicles, cloud computing can be used to provide sufficient storage and computational power. However, the numerous vehicles with massive applications will generate tremendous volume of data and exhaust finite bandwidth. It is difficult for the conventional cloud computing paradigm to satisfy stringent quality of service requirements of vehicular networks, especially for delay-sensitive applications. Then vehicular fog computing, in which servers are allocated close to vehicles, has been proposed to reduce the latency. Vehicular fog computing is still in its early stage. How to provide reliable and secure fog service to client vehicles has not been well addressed. In this paper, we propose a scalable and efficient security provision scheme based on Chinese Remainder Theory. Simulation results demonstrate that the proposed scheme significantly reduces the authentication delay.

With the emerging applications of vehicular networks, how to provide sufficient communication and computation supports are the two most important challenges for vehicular communication systems. Cloud-based vehicular networks and mobile edge computing frameworks have been proposed to relieve the computing burden of vehicles. However, for time-sensitive and computation-intensive applications with large input data size, e.g. image aided navigation, the data transmission process occupies large bandwidth, which may degrade the quality of service of all network users, especially in high density scenarios. Thus, in this paper we formulate a computation offloading problem for these time-sensitive and computation-intensive applications to minimize the data transmitted to the server. We propose two approaches to solve the formulated problem, i.e. a graph theory based method and a heuristic algorithm. Simulation results demonstrate that both algorithms can achieve near optimal solutions and greatly reduce the data volume transmitted to the server.

Public safety communication aims to provide efficient mission critical and first responder communication scenarios. Device-to-device (D2D) proximity services are designed to offload massive traffic from base stations and extend the coverage area. Utilizing relay to provide network services for user equipment (UE) that out of coverage is one of the most important attributes of proximity services. Existing works mainly focus on the transmission rate and energy efficiency for relay selection. In this paper, we propose a relay selection scheme that targets to extend the connection time in public safety communications. In particular, the proposed scheme takes into consideration the remaining battery capacity and communication capability of each UE. The system level simulation results show that the proposed scheme can prolong the connection time for the UE that is out of coverage.