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

Localisation of partial discharge sources using radio fingerprinting technique

Partial discharge (PD) is a well-known indicator of the failure of insulators in electrical plant. Operators are pushing toward lower operating cost and higher reliability and this stimulates a demand for a diagnostic system capable of accurately locating PD sources especially in ageing electricity substations. Existing techniques used for PD source localisation can be prohibitively expensive. In this paper, a cost-effective radio fingerprinting technique is proposed. This technique uses the Received Signal Strength (RSS) extracted from PD measurements gathered using RF sensors. The proposed technique models the complex spatial characteristics of the radio environment, and uses this model for accurate PD localisation. Two models were developed and compared: k-nearest neighbour and a feed-forward neural network which uses regression as a form of function approximation. The results demonstrate that the neural network produced superior performance as a result of its robustness against noise

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets

Identifying defects in aerospace composite sandwich panels using high-definition distributed optical fibre sensors

Automated methods for detecting defects within composite materials are highly desirable in the drive to increase throughput, optimise repair program effectiveness and reduce component replacement. Tap-testing has traditionally been used for detecting defects but does not provide quantitative measurements, requiring secondary techniques such as ultrasound to certify components. This paper reports on an evaluation of the use of a distributed temperature measurement system—high-definition fibre optic sensing (HD-FOS)—to identify and characterise crushed core and disbond defects in carbon fibre reinforced polymer (CFRP)-skin, aluminium-core, sandwich panels. The objective is to identify these defects in a sandwich panel by measuring the heat transfer through the panel thickness. A heater mat is used to rapidly increase the temperature of the panel with the HD-FOS sensor positioned on the top surface, measuring temperature. HD-FOS measurements are made using the Luna optical distributed sensor interrogator (ODISI) 9100 system comprising a sensor fabricated using standard single mode fibre (SMF)-20 of external diameter 250 µm, including the cladding. Results show that areas in which defects are present modulate thermal conductivity, resulting in a lower surface temperature. The resultant data are analysed to identify the length, width and type of defect. The non-invasive technique is amenable to application in challenging operational settings, offering high-resolution visualisation and defect classification

Chapter 3: Herdsman+: artificial intelligence enabled systems and services for livestock farming : Herdsman+ artificial intelligence enabled systems and services for livestock farming

The application of artificial intelligence coupled with the growth in the availability of cost- effective low power computing platforms, has accelerated the adoption of on-farm technologies that support the decision making of farmers. An exemplar of the evolution is encapsulated by the development of activity monitors for dairy cattle, migrating from simple step counting devices designed to identify the onset of oestrus to systems that continuously monitor individual cattle and provide insights into the time spent eating, ruminating, calving and other key welfare events such as lameness and mastitis. The Chapter illustrates how the use of digital technologies has brought benefit to the livestock farming industry, presenting the current state-of-the-art with emphasis on accentuating the potential for cloud based platforms to support the integration of multiple on-farm data streams, the foundation for the provision of a mix of data-driven animal-centric services that bring further benefits to the livestock community

Predicting feed intake using modelling based on feeding behaviour in finishing beef steers.

Current techniques for measuring feed intake in housed cattle are both expensive and time-consuming making them unsuitable for use on commercial farms. Estimates of individual animal intake are required for assessing production efficiency. The aim of this study was to predict individual animal intake using parameters that can be easily obtained on commercial farms including feeding behaviour, liveweight and age. In total, 80 steers were used, and each steer was allocated to one of two diets (40 per diet) which consisted of (g/kg; DM) forage to concentrate ratios of either 494:506 (MIXED) or 80:920 (CONC). Individual daily fresh weight intakes (FWI; kg/day) were recorded for each animal using 32 electronic feeders over a 56-day period, and individual DM intakes (DMI; kg/day) subsequently calculated. Individual feeding behaviour variables were calculated for each day of the measurement period from the electronic feeders and included: total number of visits to the feeder, total time spent at the feeder (TOTFEEDTIME), total time where feed was consumed (TIMEWITHFEED) and average length of time during each visit to the feeder. These feeding behaviour variables were chosen due to ease of obtaining from accelerometers. Four modelling techniques to predict individual animal intake were examined, based on (i) individual animal TOTFEEDTIME relative expressed as a proportion of the dietary group (GRP) and total GRP intake, (ii) multiple linear regression (REG) (iii) random forests (RF) and (iv) support vector regressor (SVR). Each model was used to predict CONC and MIXED diets separately, giving eight prediction models, (i) GRP_CONC, (ii) GRP_MIXED, (iii) REG_CONC, (iv) REG_MIXED, (v) RF_CONC, (vi) RF_MIXED, (vii) SVR_CONC and (viii) SVR_MIXED. Each model was tested on FWI and DMI. Model performance was assessed using repeated measures correlations (R2_RM) to capture the repeated nature of daily intakes compared with standard R2, RMSE and mean absolute error (MAE). REG, RF and SVR models predicted FWI with R2_RM = 0.1–0.36, RMSE = 1.51–2.96 kg and MAE = 1.19–2.49 kg, and DMI with R2_RM = 0.13–0.19, RMSE = 1.15–1.61 kg and MAE = 0.9–1.28 kg. The GRP models predicted FWI with R2_RM = 0.42–0.49, RMSE = 2.76–3.88 kg and MAE = 2.46–3.47 kg, and DMI with R2_RM = 0.32–0.44, RMSE = 0.32–0.44 kg, MAE = 1.55–2.22 kg. Whilst more simplistic GRP models showed higher R2_RM than regression and machine learning techniques, these models had larger errors, likely due to individual feeding patterns not being captured. Although regression and machine learning techniques produced lower errors associated with individual intakes, overall precision of prediction was too low for practical use

In-line monitoring of particle size and shape from image-based measurements

Within the pharmaceutical industry, particle size and shape distributions are crucial properties of crystalline particles produced in crystallisation processes. They determine the success or otherwise of processes such as granulation, suspension treatment and drying, all involved in the manufacture of the final pharmaceutical product. Some properties of the final pharmaceutical product such as dissolution behaviour are also influenced by the particle size and shape distribution of its ingredients. Therefore, crystallisation processes need to be controlled in order to produce particles with the desired attributes (size and shape). This in turn requires an accurate characterisation of the particle attributes during the crystallisation processes. Traditionally, particle size and shape are determined by means of off-line measurements. However, these techniques only provide information on the final state of the process and involve intermediate processing steps (e.g. sampling, dissolution, drying) that can alter the properties of the particles before the measurement. In recent years, a range of in-line techniques has been developed to obtain in-situ and real-time information on the state of the process in a non-disruptive manner

An Efficient Algorithm for Partial Discharge Localization in High-Voltage Systems Using Received Signal Strength

The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms

Unravelling anomalous mass transport in miscible liquids

The dissolution dynamics between miscible liquids play a key role in many industrial, biological and environmental processes, including solvent-induced phase transformations such as the formation of polymer membranes or antisolvent crystallisation. The “common” current intuition that guides the design of diffusion processes in miscible liquids is rooted in Fick’s law. This hypothesis generally holds when the system is close to equilibrium and behaves like an ideal mixture. However, Fickian diffusion has limited applicability far from equilibrium, and many systems display “anomalous” behaviours such as uphill diffusion [1] or the Ouzo effect [2]. Despite the importance of diffusion processes, the mechanisms underlying anomalous mass transfer are still poorly understood [3]. This work provides a direct microscopic view into highly localized anomalous pathways that can occur during the mixing of miscible fluids. Results will be presented for a model system of glycine-water-ethanol that represents a typical antisolvent crystallisation process where anomalous mass transport can have significant impacts on the critical quality attributes of the resulting crystalline product. We have deployed a novel experimental setup that includes a microfluidic flow cell that is monitored using a confocal Raman microscope, enabling the measurement of spectral maps of the mixing of the solution and antisolvent streams. These maps allow for the evolution of the composition of the multicomponent fluid to be determined as mixing progresses. From the measured spectral maps, the equilibration trajectories of the mixing solution and antisolvent streams can be determined, providing information on what regions of the phase diagrams are accessed during the mixing process, while also revealing the conditions that lead to surprising diffusive behaviours. This work provides new insight into the underlying mechanisms of anomalous mass transport and a better understanding of the equilibration pathways that can occur during antisolvent crystallization. References [1] R. Krishna; Uphill diffusion in multicomponent mixtures, Chem. Soc. Rev., 44, 2812-2836 (2015). [2] S. A. Vitale, and J. L. Katz; Liquid droplet dispersions formed by homogeneous liquid-liquid nucleation: “the ouzo effect”, Langmuir, 19, 4105-4110 (2003) [3] A. Vorobev: Dissolution dynamics of miscible liquid/liquid interfaces, Curr. Opin. Colloid Interface Sci., 19, 300-308 (2014)

Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew’s correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf