Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision

Temporally adaptive monitoring procedures with applications in enterprise cyber-security

Due to the perpetual threat of cyber-attacks, enterprises must employ and develop new methods of detection as attack vectors evolve and advance. Enterprise computer networks produce a large volume and variety of data including univariate data streams, time series and network graph streams. Motivated by cyber-security, this thesis develops adaptive monitoring tools for univariate and network graph data streams, however, they are not limited to this domain. In all domains, real data streams present several challenges for monitoring including trend, periodicity and change points. Streams often also have high volume and frequency. To deal with the non-stationarity in the data, the methods applied must be adaptive. Adaptability in the proposed procedures throughout the thesis is introduced using forgetting factors, weighting the data accordingly to recency. Secondly, methods applied must be computationally fast with a small or fixed computation burden and fixed storage requirements for timely processing. Throughout this thesis, sequential or sliding window approaches are employed to achieve this. The first part of the thesis is centred around univariate monitoring procedures. A sequential adaptive parameter estimator is proposed using a Bayesian framework. This procedure is then extended for multiple change point detection, where, unlike existing change point procedures, the proposed method is capable of detecting abrupt changes in the presence of trend. We additionally present a time series model which combines short-term and long-term behaviours of a series for improved anomaly detection. Unlike existing methods which primarily focus on point anomalies detection (extreme outliers), our method is capable of also detecting contextual anomalies, when the data deviates from persistent patterns of the series such as seasonality. Finally, a novel multi-type relational clustering methodology is proposed. As multiple relations exist between the different entities within a network (computers, users and ports), multiple network graphs can be generated. We propose simultaneously clustering over all graphs to produce a single clustering for each entity using Non-Negative Matrix Tri-Factorisation. Through simplifications, the proposed procedure is fast and scalable for large network graphs. Additionally, this methodology is extended for graph streams. This thesis provides an assortment of tools for enterprise network monitoring with a focus on adaptability and scalability making them suitable for intrusion detection and situational awareness.Open Acces

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio