Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security

A survey on cyber security of CAV

With the ever fast developments of technologies in science and engineering, it is believed that CAV (connected and autonomous vehicles) will come into our daily life soon. CAV could be used in many different aspects in our lives such as public transportation and agriculture, and so on. Although CAV will bring huge benefits to our lives and society, issues such as cyber security threats, which may reveal drivers’ private information or even pose threat to driver’s life, present significant challenges before CAV can be utilised in our society. In computer science, there is a clear category of cyber security attacks while there is no specific survey on cyber security of CAV. This paper overviews different passive and active cyber security attacks which may be faced by CAV. We also present solutions of each of these attacks based on the current state-of-the-art, and discuss future improvements in research on CAV cyber security

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection

High expression of ubiquitin-conjugating enzyme 2C (UBE2C) correlates with nasopharyngeal carcinoma progression

BACKGROUND: Overexpression of ubiquitin-conjugating enzyme 2C (UBE2C) has been detected in many types of human cancers, and is correlated with tumor malignancy. However, the role of UBE2C in human nasopharyngeal carcinoma (NPC) is unclear. In this study, we investigated the role of aberrant UBE2C expression in the progression of human NPC. METHODS: Immunohistochemical analysis was performed to detect UBE2C protein in clinical samples of NPC and benign nasopharyngeal tissues, and the association of UBE2C expression with patient clinicopathological characteristics was analyzed. UBEC2 expression profiles were evaluated in cell lines representing varying differentiated stages of NPC and immortalized nasopharyngeal epithelia NP-69 cells using quantitative RT-PCR, western blotting and fluorescent staining. Furthermore, UBE2C was knocked down using RNA interference in these cell lines and proliferation and cell cycle distribution was investigated. RESULTS: Immunohistochemical analysis revealed that UBE2C protein expression levels were higher in NPC tissues than in benign nasopharyngeal tissues (P<0.001). Moreover, high UBE2C protein expression was positively correlated with tumor size (P=0.017), lymph node metastasis (P=0.016) and distant metastasis (P=0.015) in NPC patients. In vitro experiments demonstrated that UBE2C expression levels were inversely correlated with the degree of differentiation of NPC cell lines, whereas UBE2C displayed low level of expression in NP-69 cells. Knockdown of UBE2C led to significant arrest at the S and G2/M phases of the cell cycle, and decreased cell proliferation was observed in poorly-differentiated CNE2Z NPC cells and undifferentiated C666-1 cells, but not in well-differentiated CNE1 and immortalized NP-69 cells. CONCLUSIONS: Our findings suggest that high expression of UBE2C in human NPC is closely related to tumor malignancy, and may be a potential marker for NPC progression

The complete chloroplast genome of Mimusops elengi (Sapotaceae: Sapoteae)

The first complete chloroplast genome sequences of Mimusops elengi Linaeus, 1753 (Sapotaceae: Sapoteae) were reported in this study. The cpDNA of M. elengi is 159,719 bp in length, contains a large single-copy region (LSC) of 88,935 bp and a small single-copy region (SSC) of 18,606 bp, which were separated by a pair of inverted repeat (IR) regions of 26,089 bp. The genome contains 132 genes, including 87 protein-coding genes, 8 ribosomal RNA genes, and 37 transfer RNA genes. The overall GC content of the whole genome is 36.8%. Phylogenetic analysis of 8 chloroplast genomes within the tribe Sapoteae suggests that the sister relationship of Autranella and Tieghemella are strongly supported. Minusops genus is close to Autranella and Tieghemella, although the support value is still low

A survey on cyber security of CAV

With the ever fast developments of technologies in science and engineering, it is believed that CAV (connected and autonomous vehicles) will come into our daily life soon. CAV could be used in many different aspects in our lives such as public transportation and agriculture, and so on. Although CAV will bring huge benefits to our lives and society, issues such as cyber security threats, which may reveal drivers’ private information or even pose threat to driver’s life, present significant challenges before CAV can be utilised in our society. In computer science, there is a clear category of cyber security attacks while there is no specific survey on cyber security of CAV. This paper overviews different passive and active cyber security attacks which may be faced by CAV. We also present solutions of each of these attacks based on the current state-of-the-art, and discuss future improvements in research on CAV cyber security