Gareth W. Peters

Marta Campi, Guillaume Staerman, Gareth W. Peters et al.

Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnormality score is computed by projecting each curve observation on a drawn dictionary through a linear inner product. Such linear inner product and the dictionary are a priori choices that highly influence the algorithm's performances and might lead to unreliable results, particularly with complex datasets. This work addresses these challenges by introducing \textit{Signature Isolation Forest}, a novel AD algorithm class leveraging the rough path theory's signature transform. Our objective is to remove the constraints imposed by FIF through the proposition of two algorithms which specifically target the linearity of the FIF inner product and the choice of the dictionary. We provide several numerical experiments, including a real-world applications benchmark showing the relevance of our methods.

Dorota Toczydlowska, Gareth W. Peters, Pavel V. Shevchenko

We propose a novel generalisation to the Student-t Probabilistic Principal Component methodology which: (1) accounts for an asymmetric distribution of the observation data; (2) is a framework for grouped and generalised multiple-degree-of-freedom structures, which provides a more flexible approach to modelling groups of marginal tail dependence in the observation data; and (3) separates the tail effect of the error terms and factors. The new feature extraction methods are derived in an incomplete data setting to efficiently handle the presence of missing values in the observation vector. We discuss various special cases of the algorithm being a result of simplified assumptions on the process generating the data. The applicability of the new framework is illustrated on a data set that consists of crypto currencies with the highest market capitalisation.

Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat et al.

Classification of IoT devices into different types is of paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machine learning techniques for fingerprinting (or classifying) IoT devices, with promising results. However, existing works have assumed that the features used for building the machine learning models are readily available or can be easily extracted from the network traffic; in other words, they do not consider the costs associated with feature extraction. In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features. We also take a step forward from the current practice of considering the misclassification loss as a binary value, and make a case for different losses based on the misclassification performance. Thereby, and more importantly, we introduce the notion of risk for IoT device classification. We define and formulate the problem of cost-aware IoT device classification. This being a combinatorial optimization problem, we develop a novel algorithm to solve it in a fast and effective way using the Cross-Entropy (CE) based stochastic optimization technique. Using traffic of real devices, we demonstrate the capability of the CE based algorithm in selecting features with minimal risk of misclassification while keeping the cost for feature extraction within a specified limit.

Wolfgang Fruehwirt, Matthias Gerstgrasser, Pengfei Zhang et al.

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.

Pengfei Zhang, Ido Nevat, Gareth W. Peters et al.

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.

Gareth W. Peters, Efstathios Panayi, Ariane Chapelle

The internet era has generated a requirement for low cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money have emerged as a consequence. For the most part, e-money has fulfilled this role, but the last few years have seen two new types of money emerge. Centralised virtual currencies, usually for the purpose of transacting in social and gaming economies, and crypto-currencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer online payments. We describe the historical context which led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we then discuss them in the context of monetary theory, in order to determine how they may be have value under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world.

Shihao Yan, Robert Malaney, Ido Nevat et al.

In this work we propose and examine Location Verification Systems (LVSs) for Vehicular Ad Hoc Networks (VANETs) in the realistic setting of Rician fading channels. In our LVSs, a single authorized Base Station (BS) equipped with multiple antennas aims to detect a malicious vehicle that is spoofing its claimed location. We first determine the optimal attack strategy of the malicious vehicle, which in turn allows us to analyze the optimal LVS performance as a function of the Rician $K$-factor of the channel between the BS and a legitimate vehicle. Our analysis also allows us to formally prove that the LVS performance limit is independent of the properties of the channel between the BS and the malicious vehicle, provided the malicious vehicle's antenna number is above a specified value. We also investigate how tracking information on a vehicle quantitatively improves the detection performance of an LVS, showing how optimal performance is obtained under the assumption of the tracking length being randomly selected. The work presented here can be readily extended to multiple BS scenarios, and therefore forms the foundation for all optimal location authentication schemes within the context of Rician fading channels. Our study closes important gaps in the current understanding of LVS performance within the context of VANETs, and will be of practical value to certificate revocation schemes within IEEE 1609.2.

Shihao Yan, Robert Malaney, Ido Nevat et al.

In this work we examine the performance of a Location Spoofing Detection System (LSDS) for vehicular networks in the realistic setting of Rician fading channels. In the LSDS, an authorized Base Station (BS) equipped with multiple antennas utilizes channel observations to identify a malicious vehicle, also equipped with multiple antennas, that is spoofing its location. After deriving the optimal transmit power and the optimal directional beamformer of a potentially malicious vehicle, robust theoretical analysis and detailed simulations are conducted in order to determine the impact of key system parameters on the LSDS performance. Our analysis shows how LSDS performance increases as the Rician K-factor of the channel between the BS and legitimate vehicles increases, or as the number of antennas at the BS or legitimate vehicle increases. We also obtain the counter-intuitive result that the malicious vehicle's optimal number of antennas conditioned on its optimal directional beamformer is equal to the legitimate vehicle's number of antennas. The results we provide here are important for the verification of location information reported in IEEE 1609.2 safety messages.

Gareth W. Peters, Ariane Chapelle, Efstathios Panayi

We develop the first basic Operational Risk perspective on key risk management issues associated with the development of new forms of electronic currency in the real economy. In particular, we focus on understanding the development of new risks types and the evolution of current risk types as new components of financial institutions arise to cater for an increasing demand for electronic money, micro-payment systems, Virtual money and cryptographic (Crypto) currencies. In particular, this paper proposes a framework of risk identification and assessment applied to Virtual and Crypto currencies from a banking regulation perspective. In doing so, it addresses the topical issues of understanding important key Operational Risk vulnerabilities and exposure risk drivers under the framework of the Basel II/III banking regulation, specifically associated with Virtual and Crypto currencies. This is critical to consider should such alternative currencies continue to grow in utilisation to the point that they enter into the banking sector, through commercial banks and financial institutions who are beginning to contemplate their recognition in terms of deposits, transactions and exchangeability for fiat currencies. We highlight how some of the features of Virtual and Crypto currencies are important drivers of Operational Risk, posing both management and regulatory challenges that must start to be considered and addressed both by regulators, central banks and security exchanges. In this paper we focus purely on the Operational Risk perspective of banks operating in an environment where such electronic Virtual currencies are available. Some aspects of this discussion are directly relevant now, whilst others can be understood as discussions to raise awareness of issues in Operational Risk that will arise as Virtual currency start to interact more widely in the real economy.