DETECT ROGUE loT BASED ON THE BEHAVIOUR ANALYSIS OF DEVICE WORKFLOW PATTERNS

Considering various business workflows or deployment scope, printer manufacturers provide various solutions/configurations to counter misuse of resources, document security, along with ease of use. It is a challenge to track/flag a compromised device. Existing malicious activity detection approaches use either signature‐based detection or require a prior knowledge of specific IoC (indicators of compromise) characteristics or behaviours from manual identification based on network anomalies or SIEM (Security Information and Event Management) logs, etc. The proposed idea contributes to extended detection and response (XDR) within ecosystem of deployment. Solution is to keep monitoring all outgoing network traffic within the host, uniquely assess their integrity with user job flow data and classify any malicious activity with more precision, alert device user and admin through SIEM for actionable security response

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented