Fortifying Vehicular Security through Low Overhead Physically Unclonable Functions

Within vehicles, the Controller Area Network (CAN) allows efficient communication between the electronic control units (ECUs) responsible for controlling the various subsystems. The CAN protocol was not designed to include much support for secure communication. The fact that so many critical systems can be accessed through an insecure communication network presents a major security concern. Adding security features to CAN is difficult due to the limited resources available to the individual ECUs and the costs that would be associated with adding the necessary hardware to support any additional security operations without overly degrading the performance of standard communication. Replacing the protocol is another option, but it is subject to many of the same problems. The lack of security becomes even more concerning as vehicles continue to adopt smart features. Smart vehicles have a multitude of communication interfaces an attacker could exploit to gain access to the networks. In this work, we propose a security framework that is based on physically unclonable functions (PUFs) and lightweight cryptography (LWC). The framework does not require any modification to the standard CAN protocol while also minimizing the amount of additional message overhead required for its operation. The improvements in our proposed framework result in major reduction in the number of CAN frames that must be sent during operation. For a system with 20 ECUs, for example, our proposed framework only requires 6.5% of the number of CAN frames that is required by the existing approach to successfully authenticate every ECU.

A Blockchain-Based Efficient, Secure and Anonymous Conditional Privacy-Preserving and Authentication Scheme for the Internet of Vehicles

The rapid advancement in the area of the Internet of Vehicles (IoV) has provided numerous comforts to users due to its capability to support vehicles with wireless data communication. The exchange of information among vehicle nodes is critical due to the rapid and changing topologies, high mobility of nodes, and unpredictable network conditions. Finding a single trusted entity to store and distribute messages among vehicle nodes is also a challenging task. IoV is exposed to various security and privacy threats such as hijacking and unauthorized location tracking of smart vehicles. Traceability is an increasingly important aspect of vehicular communication to detect and penalize malicious nodes. Moreover, achieving both privacy and traceability can also be a challenging task. To address these challenges, this paper presents a blockchain-based efficient, secure, and anonymous conditional privacy-preserving and authentication mechanism for IoV networks. This solution is based on blockchain to allow vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication and voting process. The proposed secure scheme provides conditional privacy to the users and the vehicles. To ensure anonymity, traceability, and unlinkability of data sharing among vehicles, we utilize Hyperledger Fabric to establish the blockchain. The proposed scheme fulfills the requirement to analyze different algorithms and schemes which are adopted for blockchain technology for a decentralized, secure, efficient, private, and traceable system. The proposed scheme examines and evaluates different consensus algorithms used in the blockchain and anonymization techniques to preserve privacy. This study also proposes a reputation-based voting system for Hyperledger Fabric to ensure a secure and reliable leader selection process in its consensus algorithm. The proposed scheme is evaluated with the existing state-of-the-art schemes and achieves better results.

Microsleep Accident Prevention for SMART Vehicle via Image Processing Integrated with Artificial Intelligent

Number of accidents caused by microsleep increases rapidly each day. This is due to the current trend of life, for example high workload, long working hours, traffic jams, having too much caffeine, drinking alcohol, age factor, and many others. This microsleep can lead to major accidents, higher number of deaths, injuries, demolition of property and permanent disability. The creation of SMART Vehicles in the Internet of Things (IoT) increases the technology capabilities in transportation sectors, in addition to reduce the number of crashes on the roads. An integration with Artificial Intelligent (AI) can be a perfect combination on development of a microsleep detection and prevention. While the image processing will be used as the method of detecting the face changes from normal to microsleep symptoms on detecting the eye degree, the head motion and the mouth yawning. This work presented a review of current research that supported the integration of IoT and AI. The analysis and discussion on the best solution and method to prevent microsleep accidents was shown. Lastly, recommendation on development of real sensors for SMART Vehicles will be discussed. A preliminary result on this work also will be shown.

Comparison of Different Clustering Algorithms to Secured VANETs Communication

ITS (Intelligent Transportation Systems) are growing increasingly popular because of the necessity for superior cyber-physical systems and comfort applications and services required for usage in autonomous vehicles. There are two types of Vehicular Ad-Hoc Networks (VANETs) that are vital to ITS: V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle). VANETs are a new technology with several potential uses in the ITS. It comprises smart vehicles and roadside equipment that connect over open-access wireless networks. An attacker may disrupt vehicular communication which can lead to potentially life-threatening scenarios because of the significant expansion in the number of vehicles in use today. VANETs must use robust security and authentication procedures to provide safe vehicular communication. This paper provides a comprehensive analysis ofthe VANET system including its characteristics and challenges. There is a concept of data dissemination that has been provided in brief. Clustering is the most important topic in VANET that is used to cluster the vehicles to secure and safely message transmission over the network. There is a taxonomy of clustering techniques has provided in a detailed manner. Besides, it has also shown the comparison of different clustering parameters-based mechanisms and MAC protocols in VANET.

Role and Merits of Green Based Nanocarriers in Cancer Treatment

The use of nanocarriers for biomedical applications has been gaining interests from researchers worldwide for the delivery of therapeutics in a controlled manner. These “smart” vehicles enhance the dissolution and the bioavailability of drugs and enable their delivery to the target site. Taking the potential toxicity into consideration, the incorporation of natural “green” materials, derived from plants or microbial sources, in the nanocarriers fabrication, improve their safety and biocompatibility. These green components can be used as a mechanical platform or as targeting ligand for the payload or can play a role in the synthesis of nanoparticles. Several studies reported the use of green based nanocarriers for the treatment of diseases such as cancer. This review article provides a critical analysis of the different types of green nanocarriers and their synthesis mechanisms, characterization, and their role in improving drug delivery of anticancer drugs to achieve precision cancer treatment. Current evidence suggests that green-based nanocarriers can constitute an effective treatment against cancer.

Autonomous Driving Control Using the DDPG and RDPG Algorithms

Recently, autonomous driving has become one of the most popular topics for smart vehicles. However, traditional control strategies are mostly rule-based, which have poor adaptability to the time-varying traffic conditions. Similarly, they have difficulty coping with unexpected situations that may occur any time in the real-world environment. Hence, in this paper, we exploited Deep Reinforcement Learning (DRL) to enhance the quality and safety of autonomous driving control. Based on the road scenes and self-driving simulation modules provided by AirSim, we used the Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG) algorithms, combined with the Convolutional Neural Network (CNN), to realize the autonomous driving control of self-driving cars. In particular, by using the real-time images of the road provided by AirSim as the training data, we carefully formulated an appropriate reward-generation method to improve the convergence speed of the adopted DDPG and RDPG models and the control performance of moving driverless cars.