Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a Hidden Markov Model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.
An interesting paper by @cite_3 that is quite relevant to our study uses accelerometer, GPS data to develop a movement and behavior model for cattle by using hidden markov models. The authors collect real data for individual cows in the herd and then try to predict their movements using machine learning models. The authors develop a 3 state model which was able to describe animal movement and state transition behavior accurately.
@cite_3
2042690051 2167975231 2154600790 2075005272
The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species. Background The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the activities of 18 healthy subjects and 8 patients with Parkinson’s disease – e.g. walking, standing, sitting, or simply holding the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using crossvalidation across all samples we are able to correctly identify 96.1 of the activities of healthy subjects and 92.2 of the activities of Parkinson’s patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson’s patients, the percent correct lowers to 60.3 , due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise crossvalidation, identifying healthy subject activities with 86.0 accuracy and 75.1 accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities. We present a technique for analyzing a set of animal gaits to predict the gait of a new animal from its shape alone. This method works on a wide range of bipeds and quadrupeds, and adapts the motion style to the size and shape of the animal. We achieve this by combining inverse optimization with sparse data interpolation. Starting with a set of reference walking gaits extracted from sagittal plane video footage, we first use inverse optimization to learn physically motivated parameters describing the style of each of these gaits. Given a new animal, we estimate the parameters describing its gait with sparse data interpolation, then solve a forward optimization problem to synthesize the final gait. To improve the realism of the results, we introduce a novel algorithm called joint inverse optimization which learns coherent patterns in motion style from a database of example animal-gait pairs. We quantify the predictive performance of our model by comparing its synthesized gaits to ground truth walking motions for a range of different animals. We also apply our method to the prediction of gaits for dinosaurs and other extinct creatures.