Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Information Fusion Architecture for Variable-Load Scheduling in a Cloud-Assisted CPS

This paper addresses the problem of devising an effective information fusion architecture for a task scheduling algorithm which facilitates data processing of a Cyber Physical System (CPS) under bounded latency for bursty or lossy traffic. Task scheduling traditionally caters to real-time systems where a feedback loop does not exist allowing the serviced application to be independent of the inputs from the server. However, owing to the nature of a near real-time CPS, such liberties cannot be entertained. Additionally, the advent of big data in CPS has necessitated the use of Cloud Computing as a scalable and cost effective alternative. Task scheduling in such CPSs, where inputs from the Cloud complete the feedback loop is a major research issue. Therefore, in this paper, we propose a multi-layered information fusion architecture which integrates such a task scheduling mechanism by accommodating both traffic bursts and packet losses. Our scheduling algorithm ensures that the overall latency always remains under an acceptable upper bound as required by the CPS application

Information Fusion Architecture for Secure Cyber Physical Systems

The security essentially governs the usability and stability of Cyber Physical Systems (CPSs) whose cyber and physical components of the system are integrated. Security solutions to this effect are threefold - detecting attacks, preventing them and finally, maintaining system integrity under attacks. In this paper, we provide a solution to the problem of detecting collaborative attacks by proposing an information fusion architecture, which utilizes the strengths of Bayesian estimation in determining causalities as conditional probabilities. We propose a Time-Varying Dynamic Bayesian Network (TVDBN) to ascertain system state information, eventually enabling the system administrator to make control decisions and maintain system stability under security attacks. The control information to the physical components such as actuators is sent over by the sensors, which are the cyber components. As such, the focus of this paper is to provide a solution to uphold the stability of a CPS based on control theoretic aspects that can be adversely affected by compromised cyber components. Theoretical aspects touched in this paper for this purpose are - bounds on transmission delays, quantization errors and sampling time. Using our proposed information fusion architecture, we were able to detect the presence of both, the stand-alone and collaborative attacks on the CPS. Our experimental results showed that our fusion architecture can detect collaborative attacks and profile them with an average accuracy of 91.7%. Given the difficulty in detecting the presence of collaborative attacks in CPS, this level of accuracy is considered high to protect CPS applications