Detection of injection attacks on in-vehicle network using data analytics

We investigate the possibility of detection of injection attacks using data analytics techniques in this thesis. The automotive industry is innovating the modern vehicles towards connectivity by interfacing them with various external entities. These entities are exposing the automobile to cyber attacks instead of ensuring its safety. Therefore it is important to consider the security aspect while developing these interfaces. Firstly, we try understand the automobile network architecture and the possible security threats associated with it. Next, we examine the various possible cyber-attacks on automobiles described in the literature. We experiment and analyze the attack scenarios by performing injection attacks on a vehicle. We collect the data during the injection attacks and apply multiple data analysis techniques. These techniques build a model based on data during normal operation. The observations from the data collected during injection attacks is fit into these techniques. The data points that do not fit the model are termed as attack points. Finally we examine and analyze the results and their accuracy in detecting injection attacks

Fine-Grained Privacy Guarantees for Coverage Problems

We introduce a new notion of neighboring databases for coverage problems such as Max Cover and Set Cover under differential privacy. In contrast to the standard privacy notion for these problems, which is analogous to node-privacy in graphs, our new definition gives a more fine-grained privacy guarantee, which is analogous to edge-privacy. We illustrate several scenarios of Set Cover and Max Cover where our privacy notion is desired one for the application. Our main result is an $\epsilon$-edge differentially private algorithm for Max Cover which obtains an $(1-1/e-\eta,\tilde{O}(k/\epsilon))$-approximation with high probability. Furthermore, we show that this result is nearly tight: we give a lower bound show that an additive error of $\Omega(k/\epsilon)$ is necessary under edge-differential privacy. Via group privacy properties, this implies a new algorithm for $\epsilon$-node differentially private Max Cover which obtains an $(1-1/e-\eta,\tilde{O}(fk/\epsilon))$-approximation, where $f$ is the maximum degree of an element in the set system. When $f\ll k$, this improves over the best known algorithm for Max Cover under pure (node) differential privacy, which obtains an $(1-1/e,\tilde{O}(k^2/\epsilon))$-approximation.Comment: 14 pages; abstract shortened to fit requirement

ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms

Connected components is a fundamental kernel in graph applications due to its usefulness in measuring how well-connected a graph is, as well as its use as subroutines in many other graph algorithms. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 37.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. Finally, to guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies in terms of their work and locality

Breeding for Resilience: Interpreting Animal Behaviour With Machine Learning

Smallholder livestock systems face increasing challenges due to climate variability, particularly heat stress, which impacts animal health, welfare and productivity. Traditional productivity measurements, such as milk yield or growth rate, are labour-intensive, costly and fail to capture an animal’s overall adaptability. In response, the International Livestock Research Institute (ILRI) and Scotland’s Rural College (SRUC) are pioneering a novel phenotyping approach using low-cost sensors, video analysis and artificial intelligence (AI). By integrating data on animal movement, behaviour, physiological responses and environmental conditions, they are developing digital twins: real-time digital representations of animals’ health and comfort. This method provides a scalable, cost-effective proxy for fitness and resilience, enabling more accurate and rapid genetic selection suited to smallholder environments. Beyond breeding, the system supports animal management and policy planning by offering timely, actionable insights. This approach to phenotyping could revolutionise livestock improvement strategies in resource-constrained settings

Breeding for Resilience: Interpreting Animal Behaviour With Machine Learning

Smallholder livestock systems face increasing challenges due to climate variability, particularly heat stress, which impacts animal health, welfare and productivity. Traditional productivity measurements, such as milk yield or growth rate, are labour-intensive, costly and fail to capture an animal’s overall adaptability. In response, the International Livestock Research Institute (ILRI) and Scotland’s Rural College (SRUC) are pioneering a novel phenotyping approach using low-cost sensors, video analysis and artificial intelligence (AI). By integrating data on animal movement, behaviour, physiological responses and environmental conditions, they are developing digital twins: real-time digital representations of animals’ health and comfort. This method provides a scalable, cost-effective proxy for fitness and resilience, enabling more accurate and rapid genetic selection suited to smallholder environments. Beyond breeding, the system supports animal management and policy planning by offering timely, actionable insights. This approach to phenotyping could revolutionise livestock improvement strategies in resource-constrained settings

Breeding for Resilience: Interpreting Animal Behaviour With Machine Learning

Smallholder livestock systems face increasing challenges due to climate variability, particularly heat stress, which impacts animal health, welfare and productivity. Traditional productivity measurements, such as milk yield or growth rate, are labour-intensive, costly and fail to capture an animal’s overall adaptability. In response, the International Livestock Research Institute (ILRI) and Scotland’s Rural College (SRUC) are pioneering a novel phenotyping approach using low-cost sensors, video analysis and artificial intelligence (AI). By integrating data on animal movement, behaviour, physiological responses and environmental conditions, they are developing digital twins: real-time digital representations of animals’ health and comfort. This method provides a scalable, cost-effective proxy for fitness and resilience, enabling more accurate and rapid genetic selection suited to smallholder environments. Beyond breeding, the system supports animal management and policy planning by offering timely, actionable insights. This approach to phenotyping could revolutionise livestock improvement strategies in resource-constrained settings