4 research outputs found
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Driver and Passenger Identification from Smartphone Data
The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics
and Electronics (CAPE) agreement
Applying Machine Learning to enhance payments systems security
Ph. D. Thesis.During the last two decades, the economic losses because fraudulent card payment transactions have tripled. The significant percentage of losses is because of fraud on e-commerce
transactions. Nowadays, there is a clear trend to use more and more mobile devices to make
electronic purchases, and it is estimated that this trend will continue in the coming years.
In the card payment scheme, big financial institutions process millions of transactions every
day; thus, they can model the processed transactions to predict fraud. On the other hand,
merchants process a much lower number of transactions, but they have access to valuable
information that they can collect from the devices that users utilise during the transaction.
In this thesis, we propose a series of measures to enhance the security of these two scenarios
based on past transactional data and information collected from the users’ device. Most of
the approaches proposed so far to model processed transactions were based on supervised
Machine Learning techniques. We propose a fraud detection system for card payments based
on an unsupervised machine learning technique; thus, the system may be able to recognise
new patterns of fraud.
On the other hand, we are looking far ahead, and because of the increment of use of mobile
devices to conduct payments, we propose a series of measures to enhance the security of the
mobile payment system. We have proposed a user identification and verification systems
for smartphones. We base the identification and verification systems on motion data, so the
systems will not require any explicit action from users
Driver and Passenger Identification from Smartphone Data
The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches, which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilizes the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analyzing the user behavior during entry, namely, the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique