A novel optical passive router ring architecture using MAGNet protocol

This paper introduces a family of bidirectional multi-fibre passive photonic ring architectures that may serve as a high-capacity network backbone for supporting next-generation data-centric services. We introduce a novel dual-router node design that avoids several non-ideal routing phenomena typically associated with passive networks based on cyclic graphs. Our design also achieves the requisite single-hop full-mesh connectivity needed for arbitrary node-to-node communications. A ring enlargement strategy is presented that allows this architecture to scale across a wide range of networking domains. A medium access protocol will also briefly elaborated

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

Implementation of a herd management system with wireless sensor networks

This paper investigates an adaptation of Wireless Sensor Networks (WSNs) to cattle monitoring applications. The proposed solution facilitates the requirement for continuously assessing the condition of individual animals, aggregating and reporting this data to the farm manager. There are several existing approaches to achieving animal monitoring, ranging from using a store and forward mechanism to employing GSM-based techniques; these approaches only provide sporadic information and introduce a considerable cost in staffing and physical hardware. The core of this study is to overcome the aforementioned drawbacks by using alternative cheap, low power consumption sensor nodes capable of providing real-time communication at a reasonable hardware cost. In this paper, both the hardware and software has been designed to provide a solution which can obtain real-time data from dairy cattle whilst conforming to the limitations associated with WSNs implementations

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

Data remanence and digital forensic investigation for CUDA Graphics Processing Units

This paper investigates the practicality of memory attacks on commercial Graphics Processing Units (GPUs). With recent advances in the performance and viability of using GPUs for various highly-parallelised data processing tasks, a number of security challenges are raised. Unscrupulous software running subsequently on the same GPU, either by the same user, or another user, in a multi-user system, may be able to gain access to the contents of the GPU memory. This contains data from previous program executions. In certain use-cases, where the GPU is used to offload intensive parallel processing such as pattern matching for an intrusion detection system, financial systems, or cryptographic algorithms, it may be possible for the GPU memory to contain privileged data, which would ordinarily be inaccessible to an unprivileged application running on the host computer. With GPUs potentially yielding access to confidential information, existing research in the field is built upon, to investigate the practicality of extracting data from global, shared and texture memory, and retrieving this data for further analysis. These techniques are also implemented on various GPUs using three different Nvidia CUDA versions. A novel methodology for digital forensic examination of GPU memory for remanent data is then proposed, along with some suggestions and considerations towards countermeasures and anti-forensic technique

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

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

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