Anatolij Bezemskij

TL;DR: This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature.

TL;DR: This work classifies applicable cyber threats according to a novel taxonomy, focusing not only on the attack vectors that can be used, but also the potential impact on the systems and ultimately on the occupants and their domestic life.

TL;DR: This work has developed an intrusion detection system that takes into account not only cyber input features, such as network traffic and disk data, but also physical input features such as speed, physical jittering and power consumption, which can markedly reduce the false positive rate and increase the overall accuracy of the detection.

TL;DR: MAGPIE is the first smart home intrusion detection system that is able to autonomously adjust the decision function of its underlying anomaly classification models to a smart home’s changing conditions and is available in open-source format, together with its evaluation datasets, to benefit from future advances in unsupervised and reinforcement learning.

TL;DR: A detection mechanism, which monitors real-time data from a large number of sources onboard the vehicle, including its sensors, networks and processing, and approach the problem as a binary classification problem of whether the robot is able to self-detect when and whether it is under attack.