An intrusion detection scheme for driverless vehicles based gyroscope sensor profiling

Vehicular ad-hoc networks of self-driving vehicles are potentially exposed to both internal and external attacks. The privacy and security of these networks is paramount for effective protection of communication systems from possible attacks. We propose an intelligent intrusion detection system in this paper that is based on Integrated Circuit Metrics (ICMetrics), which has significant defensive capability against unexpected attacks. The proposed security system shows good performance in identifying and blocking malicious vehicles in vehicular ad-hoc networks of driverless vehicles and semi driverless vehicles

An intelligent intrusion detection system for external communications in autonomous vehicles

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification

Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of the latter depends on the resources afforded onboard and remotely, as well as the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research

Improving Mobility and Safety in Traditional and Intelligent Transportation Systems Using Computational and Mathematical Modeling

In traditional transportation systems, park-and-ride (P&R) facilities have been introduced to mitigate the congestion problems and improve mobility. This study in the second chapter, develops a framework that integrates a demand model and an optimization model to study the optimal placement of P&R facilities. The results suggest that the optimal placement of P&R facilities has the potential to improve network performance, and reduce emission and vehicle kilometer traveled. In intelligent transportation systems, autonomous vehicles are expected to bring smart mobility to transportation systems, reduce traffic congestion, and improve safety of drivers and passengers by eliminating human errors. The safe operation of these vehicles highly depends on the data they receive from their external and on-board sensors. Autonomous vehicles like other cyber-physical systems are subject to cyberattacks and may be affected by faulty sensors. The consequent anomalous data can risk the safe operation of autonomous vehicles and may even lead to fatal crashes. Hence, in the third chapter, we develop an unsupervised/semi-supervised machine learning approach to address this gap. Specifically, this approach incorporates an additional autoencoder module into a generative adversarial network, which enables effective learning of the distribution of non-anomalous data. We term our approach GAN-enabled autoencoder for anomaly detection (GAAD). We evaluate the proposed approach using the Lyft Level 5 dataset and demonstrate its superior performance compared to state-of-the-art benchmarks. The prediction of a safe collision-free trajectory is probably the most important factor preventing the full adoption of autonomous vehicles in a public road. Despite recent advancements in motion prediction utilizing machine learning approaches for autonomous driving, the field is still in its early stages and necessitates further development of more effective methods to accurately estimate the future states of surrounding agents. Hence, in the fourth chapter, we introduce a novel deep learning approach for detecting the future trajectory of surrounding vehicles using a high-resolution semantic map and aerial imagery. Our proposed approach leverages integrated spatial and temporal learning to predict future motion. We assess the efficacy of our proposed approach on the Lyft Level 5 prediction dataset and achieve a comparable performance on various motion prediction metrics

Applying Machine Learning to enhance payments systems security

Ph. D. Thesis.During the last two decades, the economic losses because fraudulent card payment transactions have tripled. The significant percentage of losses is because of fraud on e-commerce transactions. Nowadays, there is a clear trend to use more and more mobile devices to make electronic purchases, and it is estimated that this trend will continue in the coming years. In the card payment scheme, big financial institutions process millions of transactions every day; thus, they can model the processed transactions to predict fraud. On the other hand, merchants process a much lower number of transactions, but they have access to valuable information that they can collect from the devices that users utilise during the transaction. In this thesis, we propose a series of measures to enhance the security of these two scenarios based on past transactional data and information collected from the users’ device. Most of the approaches proposed so far to model processed transactions were based on supervised Machine Learning techniques. We propose a fraud detection system for card payments based on an unsupervised machine learning technique; thus, the system may be able to recognise new patterns of fraud. On the other hand, we are looking far ahead, and because of the increment of use of mobile devices to conduct payments, we propose a series of measures to enhance the security of the mobile payment system. We have proposed a user identification and verification systems for smartphones. We base the identification and verification systems on motion data, so the systems will not require any explicit action from users

Counter Unmanned Aircraft Systems Technologies and Operations

As the quarter-century mark in the 21st Century nears, new aviation-related equipment has come to the forefront, both to help us and to haunt us. (Coutu, 2020) This is particularly the case with unmanned aerial vehicles (UAVs). These vehicles have grown in popularity and accessible to everyone. Of different shapes and sizes, they are widely available for purchase at relatively low prices. They have moved from the backyard recreation status to important tools for the military, intelligence agencies, and corporate organizations. New practical applications such as military equipment and weaponry are announced on a regular basis – globally. (Coutu, 2020) Every country seems to be announcing steps forward in this bludgeoning field. In our successful 2nd edition of Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets (Nichols, et al., 2019), the authors addressed three factors influencing UAS phenomena. First, unmanned aircraft technology has seen an economic explosion in production, sales, testing, specialized designs, and friendly / hostile usages of deployed UAS / UAVs / Drones. There is a huge global growing market and entrepreneurs know it. Second, hostile use of UAS is on the forefront of DoD defense and offensive planners. They are especially concerned with SWARM behavior. Movies like “Angel has Fallen,” where drones in a SWARM use facial recognition technology to kill USSS agents protecting POTUS, have built the lore of UAS and brought the problem forefront to DHS. Third, UAS technology was exploding. UAS and Counter- UAS developments in navigation, weapons, surveillance, data transfer, fuel cells, stealth, weight distribution, tactics, GPS / GNSS elements, SCADA protections, privacy invasions, terrorist uses, specialized software, and security protocols has exploded. (Nichols, et al., 2019) Our team has followed / tracked joint ventures between military and corporate entities and specialized labs to build UAS countermeasures. As authors, we felt compelled to address at least the edge of some of the new C-UAS developments. It was clear that we would be lucky if we could cover a few of – the more interesting and priority technology updates – all in the UNCLASSIFIED and OPEN sphere. Counter Unmanned Aircraft Systems: Technologies and Operations is the companion textbook to our 2nd edition. The civilian market is interesting and entrepreneurial, but the military and intelligence markets are of concern because the US does NOT lead the pack in C-UAS technologies. China does. China continues to execute its UAS proliferation along the New Silk Road Sea / Land routes (NSRL). It has maintained a 7% growth in military spending each year to support its buildup. (Nichols, et al., 2019) [Chapter 21]. They continue to innovate and have recently improved a solution for UAS flight endurance issues with the development of advanced hydrogen fuel cell. (Nichols, et al., 2019) Reed and Trubetskoy presented a terrifying map of countries in the Middle East with armed drones and their manufacturing origin. Guess who? China. (A.B. Tabriski & Justin, 2018, December) Our C-UAS textbook has as its primary mission to educate and train resources who will enter the UAS / C-UAS field and trust it will act as a call to arms for military and DHS planners.https://newprairiepress.org/ebooks/1031/thumbnail.jp

Towards a Legal end Ethical Framework for Personal Care Robots. Analysis of Person Carrier, Physical Assistant and Mobile Servant Robots.

Technology is rapidly developing, and regulators and robot creators inevitably have to come to terms with new and unexpected scenarios. A thorough analysis of this new and continuosuly evolving reality could be useful to better understand the current situation and pave the way to the future creation of a legal and ethical framework. This is clearly a wide and complex goal, considering the variety of new technologies available today and those under development. Therefore, this thesis focuses on the evaluation of the impacts of personal care robots. In particular, it analyzes how roboticists adjust their creations to the existing regulatory framework for legal compliance purposes. By carrying out an impact assessment analysis, existing regulatory gaps and lack of regulatory clarity can be highlighted. These gaps should of course be considered further on by lawmakers for a future legal framework for personal care robot. This assessment should be made first against regulations. If the creators of the robot do not encounter any limitations, they can then proceed with its development. On the contrary, if there are some limitations, robot creators will either (1) adjust the robot to comply with the existing regulatory framework; (2) start a negotiation with the regulators to change the law; or (3) carry out the original plan and risk to be non-compliant. The regulator can discuss existing (or lacking) regulations with robot developers and give a legal response accordingly. In an ideal world, robots are clear of impacts and therefore threats can be responded in terms of prevention and opportunities in form of facilitation. In reality, the impacts of robots are often uncertain and less clear, especially when they are inserted in care applications. Therefore, regulators will have to address uncertain risks, ambiguous impacts and yet unkown effects