AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference

Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80% of its localisation accuracy at the top rank when using only 10% of generated mutants, compared to results obtained without sampling

Inverse Simulation as a Tool for Fault Detection and Isolation in Planetary Rovers

With manned expeditions to planetary bodies beyond our own and the Moon currently intractable, the onus falls upon robotic systems to explore and analyse extraterrestrial environments such as Mars. These systems typically take the form of wheeled rovers, designed to navigate the difficult terrain of other worlds. Rovers have been used in this role since Lunokhod 1 landed on the Moon in 1970. While early rovers were remote controlled, communication latency with bodies beyond the Moon and the desire to improve mission effectiveness have resulted in increasing autonomy in planetary rovers. With an increase in autonomy, however, comes an increase in complexity. This can have a negative impact on the reliability of the rover system. With a fault-free system an unlikely prospect and human assistance millions of miles away, the rover must have a robust fault detection, isolation and recovery (FDIR) system. The need for comprehensive FDIR is demonstrated by the recent Chinese lunar rover, Yutu (or “Jade Rabbit”). Yutu was rendered immobile 42 days after landing and remained so for the duration of its operational life: 31 months. While its lifespan far exceeded its expected value, Yutu's inability to move severely impaired its ability to perform its mission. This clearly highlights the need for robust FDIR. A common approach to FDIR is through the generation and analysis of residuals. Output residuals may be obtained by comparing the outputs of the system with predictions of those outputs, obtained from a mathematical model of the system which is supplied with the system inputs. Output residuals allow simple detection and isolation of faults at the output of the system. Faults in earlier stages of the system, however, propagate through the system dynamics and can disperse amongst several of the outputs. This problem is exemplified by faults at the input, which can potentially excite every system state and thus manifest in every output residual. Methods exist for decoupling and analysing output residuals such that input faults may be isolated, however, these methods are complex and require comprehensive development and testing. A conceptually simpler approach is presented in this paper. Inverse simulation (InvSim) is a numerical method by which the inputs of a system are obtained for a desired output. It does so by using a Newton-Raphson algorithm to solve a non-linear model of the system for the input. When supplied with the outputs of a fault-afflicted system, InvSim produces the input required to drive a fault-free system to this output. The fault therefore manifests itself in this generated input signal. The InvSim-generated input may then be compared to the true system input to generate input residuals. Just as a fault at an output manifests itself in the residual for that output alone, a fault at an input similarly manifests itself only in the residual for that input. InvSim may also be used to generate residuals at other locations in the system, by considering distinct subsystems with their own inputs and outputs. This ability is tested comprehensively in this paper. Faults are applied to a simulated rover at a variety of locations within the system structure and residuals generated using both InvSim and conventional forward simulation. Residuals generated using InvSim are shown to facilitate detection and isolation of faults in several locations using simple analyses. By contrast, forward simulation requires the use of complex analytical methods such as structured residuals or adaptive thresholds

Spatio-temporal traffic anomaly detection for urban networks

Urban road networks are often affected by disruptions such as accidents and roadworks, giving rise to congestion and delays, which can, in turn, create a wide range of negative impacts to the economy, environment, safety and security. Accurate detection of the onset of traffic anomalies, specifically Recurrent Congestion (RC) and Nonrecurrent Congestion (NRC) in the traffic networks, is an important ITS function to facilitate proactive intervention measures to reduce the level of severity of congestion. A substantial body of literature is dedicated to models with varying levels of complexity that attempt to identify such anomalies. Given the complexity of the problem, however, very less effort is dedicated to the development of methods that attempt to detect traffic anomalies using spatio-temporal features. Driven both by the recent advances in deep learning techniques and the development of Traffic Incident Management Systems (TIMS), the aim of this research is to develop novel traffic anomaly detection models that can incorporate both spatial and temporal traffic information to detect traffic anomalies at a network level. This thesis first reviews the state of the art in traffic anomaly detection techniques, including the existing methods and emerging machine learning and deep learning methods, before identifying the gaps in the current understanding of traffic anomaly and its detection. One of the problems in terms of adapting the deep learning models to traffic anomaly detection is the translation of time series traffic data from multiple locations to the format necessary for the deep learning model to learn the spatial and temporal features effectively. To address this challenging problem and build a systematic traffic anomaly detection method at a network level, this thesis proposes a methodological framework consisting of (a) the translation layer (which is designed to translate the time series traffic data from multiple locations over the road network into a desired format with spatial and temporal features), (b) detection methods and (c) localisation. This methodological framework is subsequently tested for early RC detection and NRC detection. Three translation layers including connectivity matrix, geographical grid translation and spatial temporal translation are presented and evaluated for both RC and NRC detection. The early RC detection approach is a deep learning based method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The NRC detection, on the other hand, involves only the application of the CNN. The performance of the proposed approach is compared against other conventional congestion detection methods, using a comprehensive evaluation framework that includes metrics such as detection rates and false positive rates, and the sensitivity analysis of time windows as well as prediction horizons. The conventional congestion detection methods used for the comparison include Multilayer Perceptron, Random Forest and Gradient Boost Classifier, all of which are commonly used in the literature. Real-world traffic data from the City of Bath are used for the comparative analysis of RC, while traffic data in conjunction with incident data extracted from Central London are used for NRC detection. The results show that while the connectivity matrix may be capable of extracting features of a small network, the increased sparsity in the matrix in a large network reduces its effectiveness in feature learning compared to geographical grid translation. The results also indicate that the proposed deep learning method demonstrates superior detection accuracy compared to alternative methods and that it can detect recurrent congestion as early as one hour ahead with acceptable accuracy. The proposed method is capable of being implemented within a real-world ITS system making use of traffic sensor data, thereby providing a practically useful tool for road network managers to manage traffic proactively. In addition, the results demonstrate that a deep learning-based approach may improve the accuracy of incident detection and locate traffic anomalies precisely, especially in a large urban network. Finally, the framework is further tested for robustness in terms of network topology, sensor faults and missing data. The robustness analysis demonstrates that the proposed traffic anomaly detection approaches are transferable to different sizes of road networks, and that they are robust in the presence of sensor faults and missing data.Open Acces

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin

Modern solutions for precise fault localization in Low Voltage (LV) Distribution Networks (DNS) often rely on costly tools such as micro-Phasor Measurement Unit (μPMU), potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. Using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMS do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by industry partner Scottish Power Energy Networks (SPEN). Results show that the current estimation regressor significantly improves fault localization and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, enabling highly accurate fault detection using SM voltage-only data, with further refinement through estimation of CSC. The proposed DT offers automated fault detection, enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on the densely-noded distribution network

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin.

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit ( PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive PMU on a densely-noded distribution network