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With the upcoming disruptive technologies around autonomous driving of cyber-physical systems, the increase of cyber threats against such systems has not yet been matched with appropriate security by design and lacks approaches to incorporate proactive preventative measures. Thus, the aim of this project was to illustrate a novel method of analysing comparable travel routes in real-time to predict anomalies through a use-case of hijacked connected cars. To prove the advancement and benefits with this new approach of predictive modelling in comparison to existing industry-standard signature based machine learning models, multiple simulations have been conducted. This has been done by incorporating different Bayesian estimation techniques to analyse and predict future states based on previous behaviour in order to show a vastly increased time-window for reaction when encountering anomalies. This research paper showed and concluded that detecting real-time deviations for malicious intent with predictive and behavioural methods are far superior in precision and effectiveness than the retrospective comparison of known-good behaviour. Therefore, significantly quicker action can be taken to counter cyber-attacks. By creating profiling based behavioural algorithms to detect anomalies with the use-case of hijacked connected cars, the project demonstrated how to deal with cyber threats by design as early as they occur in the kill chain. Taking these findings into further research, the creation of a proactive warning mechanism and a reactive engagement or interception of command and control could be developed.
Proactive threat detection of cyber-physical systems using Bayesian estimation: connected cars as a case study Adam Reviczky Proactive threat detection of cyber-physical systems using Bayesian estimation: connected cars as a case study book ipad free Proactive threat detection of cyber-physical systems using Bayesian estimation: connected cars as a case study free mobi
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