Cyber-security internals of a Skoda Octavia vRS:a hands on approach

The convergence of information technology and vehicular technologies are a growing paradigm, allowing information to be sent by and to vehicles. This information can further be processed by the Electronic Control Unit (ECU) and the Controller Area Network (CAN) for in-vehicle communications or through a mobile phone or server for out-vehicle communication. Information sent by or to the vehicle can be life-critical (e.g. breaking, acceleration, cruise control, emergency communication, etc. . . ). As vehicular technology advances, in-vehicle networks are connected to external networks through 3 and 4G mobile networks, enabling manufacturer and customer monitoring of different aspects of the car. While these services provide valuable information, they also increase the attack surface of the vehicle, and can enable long and short range attacks. In this manuscript, we evaluate the security of the 2017 Skoda Octavia vRS 4x4. Both physical and remote attacks are considered, the key fob rolling code is successfully compromised, privacy attacks are demonstrated through the infotainment system, the Volkswagen Transport Protocol 2.0 is reverse engineered. Additionally, in-car attacks are highlighted and described, providing an overlook of potentially deadly threats by modifying ECU parameters and components enabling digital forensics investigation are identified

Machine learning approach for detection of nonTor traffic

Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset

Exploring the capability of text-to-image diffusion models with structural edge guidance for multi-spectral satellite image inpainting

The paper investigates the utility of text-to-image inpainting models for satellite image data. Two technical challenges of injecting structural guiding signals into the generative process as well as translating the inpainted RGB pixels to a wider set of MSI bands are addressed by introducing a novel inpainting framework based on StableDiffusion and ControlNet as well as a novel method for RGB-to-MSI translation. The results on a wider set of data suggest that the inpainting synthesized via StableDiffusion suffers from undesired artefacts and that a simple alternative of self-supervised internal inpainting achieves higher quality of synthesis

On models and approaches for human vital signs extraction from short range radar signals

The paper centres on an assessment of the modelling approaches for the processing of signals in CW and FMCW radar-based systems for the detection of vital signs. It is shown that the use of the widely adopted phase extraction method, which relies on the approximation of the target as a single point scatterer, has limitations in respect of the simultaneous estimation of both respiratory and heart rates. A method based on a velocity spectrum is proposed as an alternative with the ability to treat a wider range of application scenarios

Structural integrity monitoring of onshore wind turbine concrete foundations

Signs of damage around the bottom flange of the embedded ring were identified in a large number of existing onshore concrete foundations. As a result, the embedded ring experienced excessive vertical displacement. A wireless structural integrity monitoring (SIM) technique was developed and installed in the field to monitor the stability of these turbines by measuring the displacement patterns and subsequently alerting any significant movements of the embedded ring. This was achieved by using wireless displacement sensors located in the bottom of the turbine. A wind turbine was used as a test bed to evaluate the performance of the SIM system under field operating conditions. The results obtained from the sensors and supervisory control and data acquisition (SCADA) showed that the embedded ring exhibited significant vertical movement especially during periods of turbulent wind speed and during shut down and start up events. The measured displacement was variable around the circumference of the foundation as a result of the wind direction and the rotor uplift forces. The excessive vertical movement was observed in the side where the rotor is rotating upwards. The field test demonstrated that the SIM technique offers great potential for improving the reliability and safety of wind turbine foundations

Wireless monitoring of scour and re-deposited sediment evolution at bridge foundations based on soil electromagnetic properties

Hydraulic structures constitute the most vulnerable elements of transportation infrastructure. Recent increases in precipitation have resulted in severe and more frequent flash flooding incidents. This has put bridges over waterways at higher risk of failure due to scour. This study presents a new sensor for measuring scour depth variation and sediment deposition processes in the vicinity of the foundations to underpin systems for early warning of impending structural failure. The monitoring system consists of a probe with integrated electromagnetic sensors designed to detect changes in the dielectric permittivity of the surrounding bridge foundation. The probe is equipped with a wireless interface and was evaluated to assess its ability to detect scour and sediment deposition in various soil types and under temperature and water salinity conditions that would commonly occur in a practical installation environment. A novel methodology is also developed enabling discrimination between in-situ and re-deposited sediment delivering vital information about the load bearing capacity of the foundation. The experimental approach was validated using ‘static’ scour simulations and real-time open channel flume experiments. Results indicate that the sensor is highly sensitive to underwater bed level variations and can provide an economical and accurate structural health monitoring alternative to existing instruments

Developing a Siamese Network for Intrusion Detection Systems

Machine Learning (ML) for developing Intrusion Detection Systems (IDS) is a fast-evolving research area that has many unsolved domain challenges. Current IDS models face two challenges that limit their performance and robustness. Firstly, they require large datasets to train and their performance is highly dependent on the dataset size. Secondly, zero-day attacks demand that machine learning models are retrained in order to identify future attacks of this type. However, the sophistication and increasing rate of cyber attacks make retraining time prohibitive for practical implementation. This paper proposes a new IDS model that can learn from pair similarities rather than class discriminative features. Learning similarities requires less data for training and provides the ability to flexibly adapt to new cyber attacks, thus reducing the burden of retraining. The underlying model is based on Siamese Networks, therefore, given a number of instances, numerous similar and dissimilar pairs can be generated. The model is evaluated using three mainstream IDS datasets; CICIDS2017, KDD Cup'99, and NSL-KDD. The evaluation results confirm the ability of the Siamese Network model to suit IDS purposes by classifying cyber attacks based on similarity-based learning. This opens a new research direction for building adaptable IDS models using non-conventional ML techniques.</p

Structural health monitoring for wind turbine foundations

The construction of onshore wind turbines has rapidly been increasing as the UK attempts to meet its renewable energy targets. As the UK’s future energy depends more on wind farms, safety and security are critical to the success of this renewable energy source. Structural integrity of the tower and its components is a critical element of this security of supply. With the stochastic nature of the load regime a bespoke low cost structural health monitoring system is required to monitor integrity of the concrete foundation supporting the tower. This paper presents an assessment of ‘embedded can’ style foundation failure modes in large onshore wind turbines and proposes a novel condition based monitoring solution to aid in early warning of failure. The most common failure modes are discussed and a low-cost remote monitoring system is presented