Secure Virtual Machine Placement In Cloud Data Centers,
2019
Singapore Management University
Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Due to an increasing number of avenues for conducting cross-VM side-channel attacks, the security of multi-tenant public IaaS cloud environments is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. In this paper, we focus on secure VM placement algorithms which a cloud provider can use for the automatic enforcement of security against such co-location based attacks. To do so, we first establish a metric for evaluating and quantifying co-location security of multi-tenant public IaaS clouds, and then propose a novel VM placement …
Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences,
2019
Singapore Management University
Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang
Research Collection School Of Computing and Information Systems
The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …
Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing,
2019
Singapore Management University
Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee
Research Collection School Of Computing and Information Systems
We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of …
Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions,
2019
Singapore Management University
Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Stress and depression are a common affliction in all walks of life. When left unmanaged, stress can inhibit productivity or cause depression. Depression can occur independently of stress. There has been a sharp rise in mobile health initiatives to monitor stress and depression. However, these initiatives usually require users to install dedicated apps or multiple sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook social interactions, which plays a significant role in influencing stress and depression while being a part of a social system. We present StressMon, a stress and depression detection system that …
Vitamon: Measuring Heart Rate Variability Using Smartphone Front Camera,
2019
Singapore Management University
Vitamon: Measuring Heart Rate Variability Using Smartphone Front Camera, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko, Youngki Lee
Research Collection School Of Computing and Information Systems
We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network …
Automatic Generation Of Pull Request Descriptions,
2019
Zhejiang University
Automatic Generation Of Pull Request Descriptions, Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. …
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning,
2019
Singapore Management University
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning, Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, Changjie Fan
Research Collection School Of Computing and Information Systems
—Game testing has been long recognized as a notoriously challenging task, which mainly relies on manual playing and scripting based testing in game industry. Even until recently, automated game testing still remains to be largely untouched niche. A key challenge is that game testing often requires to play the game as a sequential decision process. A bug may only be triggered until completing certain difficult intermediate tasks, which requires a certain level of intelligence. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game testing, without human competitive intelligent support. However, the existing DRLs mostly focus …
Coverage-Guided Fuzzing For Feedforward Neural Networks,
2019
Singapore Management University
Coverage-Guided Fuzzing For Feedforward Neural Networks, Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support …
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms,
2019
Singapore Management University
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms, Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice.To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks …
A Quantitative Analysis Framework For Recurrent Neural Network,
2019
Singapore Management University
A Quantitative Analysis Framework For Recurrent Neural Network, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework — DeepStellar— to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We …
Deepmutation++: A Mutation Testing Framework For Deep Learning Systems,
2019
Singapore Management University
Deepmutation++: A Mutation Testing Framework For Deep Learning Systems, Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables static analysis of the robustness of a DNN model against the input as a whole, but also allows the identification of the vulnerable segments of a …
Mobidroid: A Performance-Sensitive Malware Detection System On Mobile Platform,
2019
Singapore Management University
Mobidroid: A Performance-Sensitive Malware Detection System On Mobile Platform, Ruitao Feng, Sen Chen, Xiaofei Xie, Lei Ma, Guozhu Meng, Yang Liu, Shang-Wei Lin
Research Collection School Of Computing and Information Systems
Currently, Android malware detection is mostly performed on the server side against the increasing number of Android malware. Powerful computing resource gives more exhaustive protection for Android markets than maintaining detection by a single user in many cases. However, apart from the Android apps provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing a serious security threat to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection because the network transmission has a lot of overhead. In …
Safe Inputs Approximation For Black-Box Systems,
2019
Singapore Management University
Safe Inputs Approximation For Black-Box Systems, Bai Xue, Yang Liu, Lei Ma, Xiyue Zhang, Meng Sun, Xiaofei Xie
Research Collection School Of Computing and Information Systems
Given a family of independent and identically distributed samples extracted from the input region and their corresponding outputs, in this paper we propose a method to under-approximate the set of safe inputs that lead the blackbox system to respect a given safety specification. Our method falls within the framework of probably approximately correct (PAC) learning. The computed under-approximation comes with statistical soundness provided by the underlying PAC learning process. Such a set, which we call a PAC under-approximation, is obtained by computing a PAC model of the black-box system with respect to the specified safety specification. In our method, the …
Ad-Link: An Adaptive Approach For User Identity Linkage,
2019
Singapore Management University
Ad-Link: An Adaptive Approach For User Identity Linkage, Xin Mu, Wei Xie, Ka Wei, Roy Lee, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account’s features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a …
Statistical Log Differencing,
2019
Zhejiang University
Statistical Log Differencing, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the …
Assessing The Generalizability Of Code2vec Token Embeddings,
2019
Singapore Management University
Assessing The Generalizability Of Code2vec Token Embeddings, Hong Jin Kang, Tegawende F. Bissyande, David Lo
Research Collection School Of Computing and Information Systems
Many Natural Language Processing (NLP) tasks, such as sentiment analysis or syntactic parsing, have benefited from the development of word embedding models. In particular, regardless of the training algorithms, the learned embeddings have often been shown to be generalizable to different NLP tasks. In contrast, despite recent momentum on word embeddings for source code, the literature lacks evidence of their generalizability beyond the example task they have been trained for. In this experience paper, we identify 3 potential downstream tasks, namely code comments generation, code authorship identification, and code clones detection, that source code token embedding models can be applied …
Autofocus: Interpreting Attention-Based Neural Networks By Code Perturbation,
2019
Singapore Management University
Autofocus: Interpreting Attention-Based Neural Networks By Code Perturbation, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Despite being adopted in software engineering tasks, deep neural networks are treated mostly as a black box due to the difficulty in interpreting how the networks infer the outputs from the inputs. To address this problem, we propose AutoFocus, an automated approach for rating and visualizing the importance of input elements based on their effects on the outputs of the networks. The approach is built on our hypotheses that (1) attention mechanisms incorporated into neural networks can generate discriminative scores for various input elements and (2) the discriminative scores reflect the effects of input elements on the outputs of the …
Saffron: Adaptive Grammar-Based Fuzzing For Worst-Case Analysis,
2019
Singapore Management University
Saffron: Adaptive Grammar-Based Fuzzing For Worst-Case Analysis, Xuan Bach D. Le, Corina Pasareanu, Rohan Padhye, David Lo, Willem Visser, Koushik Sen
Research Collection School Of Computing and Information Systems
Fuzz testing has been gaining ground recently with substantial efforts devoted to the area. Typically, fuzzers take a set of seed inputs and leverage random mutations to continually improve the inputs with respect to a cost, e.g. program code coverage, to discover vulnerabilities or bugs. Following this methodology, fuzzers are very good at generating unstructured inputs that achieve high coverage. However fuzzers are less effective when the inputs are structured, say they conform to an input grammar. Due to the nature of random mutations, the overwhelming abundance of inputs generated by this common fuzzing practice often adversely hinders the effectiveness …
Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data,
2019
Western University
Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data, Jose Miguel Alves, Leonardo Honorio, Miriam A M Capretz
Electrical and Computer Engineering Publications
Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may …
On The Yellow Brick Road, A Path To Enterprise Architecture Maturity,
2019
University of the Witwatersrand
On The Yellow Brick Road, A Path To Enterprise Architecture Maturity, Avsharn Bachoo
The African Journal of Information Systems
This study concentrated on the relationship between the Enterprise Architecture (EA) maturity of an organization and the business value associated with it in the South African financial services environment. It was developed within the critical realism philosophy, which states that mechanisms generate events by accentuating the underlying EA mechanisms that lead to business value, as well as provide insights into the opportunities and challenges organizations experienced as they progressed to higher levels of maturity. Constructed using the resource-based view of the firm as the underlying theoretical framework, this research examined EA as an intangible resource and maturity as a source …
