Biblio

With the explosion of Industry 4.0, industrial facilities and critical infrastructures are transforming into “smart” systems that dynamically adapt to external events. The result is an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers, which are more and more exposed to cyber-physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes at the core of industrial control systems. We apply runtime enforcement techniques, based on an ad-hoc sub-class of Ligatti et al.'s edit automata, to enforce specification compliance in networks of potentially compromised controllers, formalised in Hennessy and Regan's Timed Process Language. We define a synthesis algorithm that, given an alphabet P of observable actions and an enforceable regular expression e capturing a timed property for controllers, returns a monitor that enforces the property e during the execution of any (potentially corrupted) controller with alphabet P and complying with the property e. Our monitors correct and suppress incorrect actions coming from corrupted controllers and emit actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical properties, such as transparency and soundness, the proposed enforcement ensures non-obvious properties, such as polynomial complexity of the synthesis, deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers.

Every public or private area today is preferred to be under surveillance to ensure high levels of security. Since the surveillance happens round the clock, data gathered as a result is huge and requires a lot of manual work to go through every second of the recorded videos. This paper presents a system which can detect anomalous behaviors and alarm the user on the type of anomalous behavior. Since there are a myriad of anomalies, the classification of anomalies had to be narrowed down. There are certain anomalies which are generally seen and have a huge impact on public safety, such as explosions, road accidents, assault, shooting, etc. To narrow down the variations, this system can detect explosion, road accidents, shooting, and fighting and even output the frame of their occurrence. The model has been trained with videos belonging to these classes. The dataset used is UCF Crime dataset. Learning patterns from videos requires the learning of both spatial and temporal features. Convolutional Neural Networks (CNN) extract spatial features and Long Short-Term Memory (LSTM) networks learn the sequences. The classification, using an CNN-LSTM model achieves an accuracy of 85%.