Automatic modelling for diagnosis.

Much of the past work on fault diagnosis did not pay enough attention to model construction and its important role in aiding problem solving. It was generally accepted that a model was available or was assumed to be present in a certain format before starting the diagnosis process. However in practice a model which can be constructed from an engineering or commercial system is often different from the model on which diagnostic algorithms have been developed. Our paper aims at filling this gap between the model construction and model-based fault diagnosis, providing a framework to integrate them coherently

An introduction to interval-based constraint processing.

Constraint programming is often associated with solving problems over finite domains. Many applications in engineering, CAD and design, however, require solving problems over continuous (real-valued) domains. While simple constraint solvers can solve linear constraints with the inaccuracy of floating-point arithmetic, methods based on interval arithmetic allow exact (interval) solutions over a much wider range of problems. Applications of interval-based programming extend the range of solvable problems from non-linear polynomials up to those involving ordinary differential equations. In this text, we give an introduction to current approaches, methods and implementations of interval-based constraint programming and solving. Special care is taken to provide a uniform and consistent notation, since the literature in this field employs many seemingly different, but yet conceptually related, notations and terminology

A methodology for the elicitation of redesign knowledge.

We present MADAM, a methodology which allows the elicitation, capture, analysis and management of redesign knowledge. This area is characterised by the high reusability of problem solutions and is represented using three views: physical, functional and process. The methodology supports the analysis of the knowledge elicited and, therefore, the inconsistencies are detected. In addition, the knowledge is normalised so unnecessary (subsumed) parts and technical solutions can be removed with the aid of the expert. MADAM thus contributes towards better and faster redesign

DynABT: dynamic asynchronous backtracking for dynamic DisCSPs.

Constraint Satisfaction has been widely used to model static combinatorial problems. However, many AI problems are dynamic and take place in a distributed environment, i.e. the problems are distributed over a number of agents and change over time. Dynamic Distributed Constraint Satisfaction Problems (DDisCSP) [1] are an emerging field for the resolution problems that are dynamic and distributed in nature. In this paper, we propose DynABT, a new Asynchronous algorithm for DDisCSPs which combines solution and reasoning reuse i.e. it handles problem changes by modifying the existing solution while re-using knowledge gained from solving the original (unchanged) problem. The benefits obtained from this approach are two-fold: (i) new solutions are obtained at a lesser cost and; (ii) resulting solutions are stable i.e. close to previous solutions. DynABT has been empirically evaluated on problems of varying difficulty and several degrees of changes has been found to be competitive for the problem classes tested

Detection of false command and response injection attacks for cyber physical systems security and resilience.

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of false control and configuration commands into a control system and ii) response injection (RI) attacks that modifies the response from server to client, thereby providing false information about system state. In this project, we implemented deep learning (DL) multi-layered security model approach for securing industrial control system (ICS) against malicious CI and RI attacks. We validated this approach with two case studies: i) network transactions between a Remote Terminal Unit (RTU) and a Master Control Unit (MTU) in-house SCADA gas pipeline control system and ii) a case study of command and response injection attacks. Based on this project result, we show that the proposed approach achieved a significant attacks detection capability of 96.50%. Also, demonstrated that performance of attack detection techniques applied can be influences by the nature of network transactions with respect to the domain of application. Hence, robustness and resilience of operational CPS state and performance are influenced by the safety and security measures in place which is specific to the CPS device in question

CSP: there is more than one way to model it.

In this paper, we present an approach for conceptual modelling of con- straint satisfaction problems (CSP). The main objective is to achieve a similarly high degree of modelling support for constraint problems as it is already available in other disciplines. The approach uses diagrams as operational basis for the development of CSP models. To facilitate a broader scope, the use of available mainstream modelling languages is adapted. In particular, the structural aspects of the problem are visually expressed in UML, complemented by a textual representation of rela- tions and constraints in OCL. A case study illustrates the expositions and deployment of the approach

DisBO-wd: a distributed constraint satisfaction algorithm for coarse-grained distributed problems.

We present a distributed iterative improvement algorithm for solving coarse-grained distributed constraint satisfaction problems (DisCSPs). Our algorithm is inspired by the Distributed Breakout for coarse-grained DisCSPs where we introduce a constraint weight decay and a constraint weight learning mechanism in order to escape local optima. We also introduce some randomisation in order to give the search a better chance of finding the right path to a solution. We show that these mechanisms improve the performance of the algorithm considerably and make it competitive with respect to other algorithms

Handling minority class problem in threats detection based on heterogeneous ensemble learning approach.

Multiclass problem, such as detecting multi-steps behaviour of Advanced Persistent Threats (APTs) have been a major global challenge, due to their capability to navigates around defenses and to evade detection for a prolonged period of time. Targeted APT attacks present an increasing concern for both cyber security and business continuity. Detecting the rare attack is a classification problem with data imbalance. This paper explores the applications of data resampling techniques, together with heterogeneous ensemble approach for dealing with data imbalance caused by unevenly distributed data elements among classes with our focus on capturing the rare attack. It has been shown that the suggested algorithms provide not only detection capability, but can also classify malicious data traffic corresponding to rare APT attacks

Escaping local optima: constraint weights vs value penalties.

Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the constructive search techniques on large problems, but they have a propensity to converge to local optima. Therefore, a key research topic on iterative improvement search is the development of effective techniques for escaping local optima, most of which are based on increasing the weights attached to violated constraints. An alternative approach is to attach penalties to the individual variable values participating in a constraint violation. We compare both approaches and show that the penalty-based technique has a more dramatic effect on the cost landscape, leading to a higher ability to escape local optima. We present an improved version of an existing penalty-based algorithm where penalty resets are driven by the amount of distortion to the cost landscape caused by penalties. We compare this algorithm with an algorithm based on constraint weights and justify the difference in their performance

Dynamic agent prioritisation with penalties in distributed local search.

Distributed Constraint Satisfaction Problems (DisCSPs) solving techniques solve problems which are distributed over a number of agents.The distribution of the problem is required due to privacy, security or cost issues and, therefore centralised problem solving is inappropriate. Distributed local search is a framework that solves large combinatorial and optimization problems. For large problems it is often faster than distributed systematic search methods. However, local search techniques are unable to detect unsolvability and have the propensity of getting stuck at local optima. Several strategies such as weights on constraints, penalties on values and probability have been used to escape local optima. In this paper, we present an approach for escaping local optima called Dynamic Agent Prioritisation and Penalties (DynAPP) which combines penalties on variable values and dynamic variable prioritisation for the resolution of distributed constraint satisfaction problems. Empirical evaluation with instances of random, meeting scheduling and graph colouring problems have shown that this approach solved more problems in less time at the phase transition when compared with some state of the art algorithms. Further evaluation of the DynAPP approach on iteration-bounded optimisation problems showed that DynAPP is competitive