The increase in popularity of connected features in intelligent transportation systems, has led to a greater risk of cyber-attacks and subsequently, requires a more robust validation of cybersecurity in vehicle design. This article explores three such cyber-attacks and the weaknesses in the connected networks. A review is carried out on current vulnerabilities and key considerations for future vehicle design and validation are highlighted. This article addresses the vehicle manufactures desire to add unnecessary remote connections without appropriate security analysis and assessment of the risks involved. The modern vehicle is All Connected and only as strong as its weakest link.
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Advances of emerging Information and Communications Technology (ICT) technologies push the boundaries of what is possible and open up new markets for innovative ICT products and services. The adoption of ICT products and systems with security properties depends on consumers' confidence and markets' trust in the security functionalities and whether the assurance measures applied to these products meet the inherent security requirements. Such confidence and trust are primarily gained through the rigorous development of security requirements, validation criteria, evaluation, and certification. Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC) is an international standard (ISO/IEC 15408) for cyber security certification. In this paper, we conduct a systematic review of the CC standards and its adoptions. Adoption barriers of the CC are also investigated based on the analysis of current trends in security evaluation. Specifically, we share the experiences and lessons gained through the recent Development of Australian Cyber Criteria Assessment (DACCA) project that promotes the CC among stakeholders in ICT security products related to specification, development, evaluation, certification and approval, procurement, and deployment. Best practices on developing Protection Profiles, recommendations, and future directions for trusted cybersecurity advancement are presented.
The 5G wireless networks are potentially revolutionizing future technologies. The 5G technologies are expected to foresee demands of diverse vertical applications with diverse requirements including high traffic volume, massive connectivity, high quality of service, and low latency. To fulfill such requirements in 5G and beyond, new emerging technologies such as SDN, NFV, MEC, and CC are being deployed. However, these technologies raise several issues regarding transparency, decentralization, and reliability. Furthermore, 5G networks are expected to connect many heterogeneous devices and machines which will raise several security concerns regarding users' confidentiality, data privacy, and trustworthiness. To work seamlessly and securely in such scenarios, future 5G networks need to deploy smarter and more efficient security functions. Motivated by the aforementioned issues, blockchain was proposed by researchers to overcome 5G issues because of its capacities to ensure transparency, data reliability, trustworthiness, immutability in a distributed environment. Indeed, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized technologies. In this chapter, we discuss the integration of the blockchain with 5G networks and beyond. We then present how blockchain applications in 5G networks and beyond could facilitate enabling various services at the edge and the core.
From denial-of-service attacks to spreading of ransomware or other malware across an organization's network, it is possible that manually operated defenses are not able to respond in real time at the scale required, and when a breach is detected and remediated the damage is already made. Autonomous cyber defenses therefore become essential to mitigate the risk of successful attacks and their damage, especially when the response time, effort and accuracy required in those defenses is impractical or impossible through defenses operated exclusively by humans. Autonomous agents have the potential to use ML with large amounts of data about known cyberattacks as input, in order to learn patterns and predict characteristics of future attacks. Moreover, learning from past and present attacks enable defenses to adapt to new threats that share characteristics with previous attacks. On the other hand, autonomous cyber defenses introduce risks of unintended harm. Actions arising from autonomous defense agents may have harmful consequences of functional, safety, security, ethical, or moral nature. Here we focus on machine learning training, algorithmic feedback, and algorithmic constraints, with the aim of motivating a discussion on achieving trust in autonomous cyber defenses.
Today's cyber defense tools are mostly watchers. They are not active doers. To be sure, watching too is a demanding affair. These tools monitor the traffic and events; they detect malicious signatures, patterns and anomalies; they might classify and characterize what they observe; they issue alerts, and they might even learn while doing all this. But they don't act. They do little to plan and execute responses to attacks, and they don't plan and execute recovery activities. Response and recovery - core elements of cyber resilience are left to the human cyber analysts, incident responders and system administrators. We believe things should change. Cyber defense tools should not be merely watchers. They need to become doers - active fighters in maintaining a system's resilience against cyber threats. This means that their capabilities should include a significant degree of autonomy and intelligence for the purposes of rapid response to a compromise - either incipient or already successful - and rapid recovery that aids the resilience of the overall system. Often, the response and recovery efforts need to be undertaken in absence of any human involvement, and with an intelligent consideration of risks and ramifications of such efforts. Recently an international team published a report that proposes a vision of an autonomous intelligent cyber defense agent (AICA) and offers a high-level reference architecture of such an agent. In this paper we explore this vision.
The COVID-19 pandemic has significantly influenced all modes of transportation. However, it is still unclear how the pandemic affected the demand for ridesourcing services and whether these effects varied between small towns and large cities. We analyzed over 220 million ride requests in the City of Chicago (population: 2.7 million), Illinois, and 52 thousand in the Town of Innisfil (population: 37 thousand), Ontario, to investigate the impact of the COVID-19 pandemic on the ridesourcing demand in the two locations. Overall, the pandemic resulted in fewer trips in areas with higher proportions of seniors and more trips to parks and green spaces. Ridesourcing demand was adversely affected by the stringency index and COVID-19-related variables, and positively affected by vaccination rates. However, compared to Innisfil, ridesourcing services in Chicago experienced higher reductions in demand, were more affected by the number of hospitalizations and deaths, were less impacted by vaccination rates, and had lower recovery rates.
Public opinion surveys constitute a powerful tool to study peoples' attitudes and behaviors in comparative perspectives. However, even worldwide surveys provide only partial geographic and time coverage, which hinders comprehensive knowledge production. To broaden the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or years. The resulting new datasets can be analyzed as a single source, which can be flexibly accessed through many data portals. However, such portals offer little guidance to explore the data in-depth or query data with user-customized needs. As a result, it is still challenging for social scientists to efficiently identify related data for their studies and evaluate their theoretical models based on the sliced data. To overcome them, in the Survey Data Recycling (SDR) international cooperation research project, we propose SDRQuerier and apply it to the harmonized SDR database, which features over two million respondents interviewed in a total of 1,721 national surveys that are part of 22 well-known international projects. We design the SDRQuerier to solve three practical challenges that social scientists routinely face. First, a BERT-based model provides customized data queries through research questions or keywords. Second, we propose a new visual design to showcase the availability of the harmonized data at different levels, thus helping users decide if empirical data exist to address a given research question. Lastly, SDRQuerier discloses the underlying relational patterns among substantive and methodological variables in the database, to help social scientists rigorously evaluate or even improve their regression models. Through case studies with multiple social scientists in solving their daily challenges, we demonstrated the novelty, effectiveness of SDRQuerier.
Autonomous Vehicles (AVs) i.e., self-driving cars, operate in a safety critical domain, since errors in the autonomous driving software can lead to huge losses. Statistically, road intersections which are a part of the AVs operational design domain (ODD), have some of the highest accident rates. Hence, testing AVs to the limits on road intersections and assuring their safety on road intersections is pertinent, and thus the focus of this paper. We present a situation coverage-based (SitCov) AV-testing framework for the verification and validation (V&V) and safety assurance of AVs, developed in an open-source AV simulator named CARLA. The SitCov AV-testing framework focuses on vehicle-to-vehicle interaction on a road intersection under different environmental and intersection configuration situations, using situation coverage criteria for automatic test suite generation for safety assurance of AVs. We have developed an ontology for intersection situations, and used it to generate a situation hyperspace i.e., the space of all possible situations arising from that ontology. For the evaluation of our SitCov AV-testing framework, we have seeded multiple faults in our ego AV, and compared situation coverage based and random situation generation. We have found that both generation methodologies trigger around the same number of seeded faults, but the situation coverage-based generation tells us a lot more about the weaknesses of the autonomous driving algorithm of our ego AV, especially in edge-cases. Our code is publicly available online, anyone can use our SitCov AV-testing framework and use it or build further on top of it. This paper aims to contribute to the domain of V&V and development of AVs, not only from a theoretical point of view, but also from the viewpoint of an open-source software contribution and releasing a flexible/effective tool for V&V and development of AVs.
Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.
The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we have an aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.
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