Abstract The intentional targeting of components in a cloud based application, in order to artificially inflate usage bills, is an issue application owners have faced for many years. This has occurred under many guises, such as: Economic Denial of Sustainability (EDoS), Click Fraud and even secondary effects of Denial of Service (DoS) attacks. With the advent of commercial offerings of serverless computing circa 2015, a variant of the EDoS attack has emerged, termed, Denial-of-Wallet (DoW). We describe our development of a simulation tool as safe means to research these attacks as well as to generate datasets for the training of future mitigation systems to combat DoW. We believe that DoW may become increasingly prevalent as applications further utilise services based on a pay-per-invocation cost model. Given that the damage caused is purely financial, such attacks may not be disclosed as application users are not directly effected. As such, we believe that the development of an attack simulator and specific testing of security measures against this niche attack will be able to provide previously unavailable data and insights for the research community. We have developed a prototype DoW simulator that can emulate multiple months worth of API calls in a matter of hours for ease of training data generation. Our aspiration for the future of this work is to provide a system and starting point for research on this form of attack. We present our work on such a system Denial-of-Wallet Test Simulator (DoWTS) - a system that allows for safe testing of theorised DoW attacks against serverless applications via synthetic data generation. We also expand upon prior research on DoW and provide an analysis on the lack of specific safety measures for DoW.

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.

Serverless computing is the latest paradigm in cloud computing, offering a framework for the development of event driven, pay-as-you-go functions in a highly scalable environment. While these traits offer a powerful new development paradigm, they have also given rise to a new form of cyber-attack known as Denial of Wallet (forced financial exhaustion). In this work, we define and identify the threat of Denial of Wallet and its potential attack patterns. Also, we demonstrate how this new form of attack can potentially circumvent existing mitigation systems developed for a similar style of attack, Denial of Service. Our goal is twofold. Firstly, we will provide a concise and informative overview of this emerging attack paradigm. Secondly, we propose this paper as a starting point to enable researchers and service providers to create effective mitigation strategies. We include some simulated experiments to highlight the potential financial damage that such attacks can cause and the creation of an isolated test bed for continued safe research on these attacks.

Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is achieved through severe abstraction of the underlying architecture the functions run on. We examine the underlying architecture and report on the performance of serverless functions and how they are effected by certain factors such as memory allocation and interference caused by load induced by other users on the platform. Specifically, we focus on the serverless offerings of the four largest platforms; AWS Lambda, Google Cloud Functions, Microsoft Azure Functions and IBM Cloud Functions. In this paper, we observe and contrast between these platforms in their approach to the common issue of “cold starts”, we devise a means to unveil the underlying architecture serverless functions execute on and we investigate the effects of interference from load on the platform over the time span of one month.

OBJECTIVE: Current methods of assessing the affect patients' health has on their daily lives are extremely limited. The aim of this paper is to develop a sensor-based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behavior profiles, derived from the smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behavior profiles as a predictor of health status. RESULTS: Experiments were conducted on data from 171 participants, with an average of 114 h of data per participant. Regression models were trained and tested on the 10 SF-36 self-ratings. Results showed that the eight individual SF-36 scales and two component scores could be predicted with an average correlation of 0.683 and 0.698, respectively. General health was predicted with an average correlation of 0.752. CONCLUSION: Research shows that the clinically important difference for SF-36 self-ratings are approximately 10 points. Health status prediction errors in this study were 11.7 points on average. While the problem has not been fully solved, this paper presents a hugely promising direction for health status prediction. SIGNIFICANCE: Using the proposed techniques, health status could be measured using unobtrusive, inexpensive, and already available hardware. It could provide a means for clinicians to accurately and objectively assess the daily life benefits of treatments on an individual patient basis.

Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

Human behavior, and in particular location behavior, is highly routine based. Modern mobile phones, through global position system (GPS) technology and cell tower and WiFi location identification, enable us to trace human location behavior at scales that were previously unattainable. The goal of this paper is to examine human location behavior, through mobile phone data, and investigate if links can be made between location behavior patterns and particular demographic and social characteristics about an individual. We hypothesize that an individual's daily predictability can be key to linking their behavior to certain characteristics, and we propose predictability and geographic areas of interest models to analyze this hypothesis. Experiments reveal that measurements, which are based on our proposed location predictability models, can correctly infer 17 different characteristics about an individual with an average accuracy of 85.5%.

A system for automatically training and spotting signs from continuous sign language sentences is presented. We propose a novel multiple instance learning density matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilized to train our spatiotemporal gesture and hand posture classifiers. The experiments were carried out to evaluate the performance of the automatic sign extraction, hand posture classification, and spatiotemporal gesture spotting systems. We then carry out a full evaluation of our overall sign spotting system which was automatically trained on 30 different signs.

We present a multimodal system for the recognition of manual signs and non-manual signals within continuous sign language sentences. In sign language, information is mainly conveyed through hand gestures (Manual Signs). Non-manual signals, such as facial expressions, head movements, body postures and torso movements, are used to express a large part of the grammar and some aspects of the syntax of sign language. In this paper we propose a multichannel HMM based system to recognize manual signs and non-manual signals. We choose a single non-manual signal, head movement, to evaluate our framework when recognizing non-manual signals. Manual signs and non-manual signals are processed independently using continuous multidimensional HMMs and a HMM threshold model. Experiments conducted demonstrate that our system achieved a detection ratio of 0.95 and a reliability measure of 0.93.

Many patients with paralyzing injuries or medical conditions retain the use of their cranial nerves, which control the eyes, jaw, and tongue. While researchers have explored eye-tracking and speech technologies for these patients, we believe there is potential for directly sensing explicit tongue movement for controlling computers. In this paper, we describe a novel approach of using infrared optical sensors embedded within a dental retainer to sense tongue gestures. We describe an experiment showing our system effectively discriminating between four simple gestures with over 90% accuracy. In this experiment, users were also able to play the popular game Tetris with their tongues. Finally, we present lessons learned and opportunities for future work.

This paper presents a computer vision based virtual learning environment for teaching communicative hand gestures used in sign language. A virtual learning environment was developed to demonstrate signs to the user. The system then gives real time feedback to the user on their performance of the demonstrated sign. Gesture features are extracted from a standard web-cam video stream and shape and trajectory matching techniques are applied to these features to determine the feedback given to the user.