OS and Networks Commons^™

Integrating The Bullet Physics Engine Into Minecraft, Ethan Johnson

Student Academic Conference

During the past fall semester, I started a programming project called Rayon which is designed to be a realistic physics engine implementation that runs alongside the videogame Minecraft. It is a library which Minecraft mod developers can use to implement realistic entity movement into their own mods. Rayon, being entirely written in the Java programming language, currently uses a port of the Bullet physics engine called JBullet which is very outdated and no longer being maintained. To find a more performant solution, I have set out to replace JBullet with an alternative library called LibBulletJME which is designed to interface …

Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu

Research Collection School Of Computing and Information Systems

The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, …

Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations, Lili Quan, Qianyu Guo, Hongxu Chen, Xiaofei Xie, Xiaohong Li, Yang Liu, Jing Hu

Research Collection School Of Computing and Information Systems

The security assurance of SSL/TLS critically depends on the correct validation of X.509 certificates. Therefore, it is important to check whether a certificate is correctly validated by the SSL/TLS implementations. Although differential testing has been proven to be effective in finding semantic bugs, it still suffers from the following limitations: (1) The syntax of test cases cannot be correctly guaranteed. (2) Current test cases are not diverse enough to cover more implementation behaviours. This paper tackles these problems by introducing SADT, a novel syntax-aware differential testing framework for evaluating the certificate validation process in SSL/TLS implementations. We first propose a …

Audee: Automated Testing For Deep Learning Frameworks, Qianyu Guo, Xiaofei Xie, Yi Li, Xiaoyu Zhang, Yang Liu, Xiaohong Li, Chao Shen

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial, especially for safety-critical applications. Existing work mainly focuses on the quality analysis of DL models, but lacks attention to the underlying frameworks on which all DL models depend. In this work, we propose Audee, a novel approach for testing DL frameworks and localizing bugs. Audee adopts a search-based approach and implements three different mutation strategies to generate diverse test cases by exploring combinations of model structures, parameters, weights and inputs. Audee is able to detect three types of bugs: logical bugs, crashes and Not-a-Number (NaN) …

Deepsonar: Towards Effective And Robust Detection Of Ai-Synthesized Fake Voices, Run Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo, Xiaofei Xie, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

With the recent advances in voice synthesis, AI-synthesized fake voices are indistinguishable to human ears and widely are applied to produce realistic and natural DeepFakes, exhibiting real threats to our society. However, effective and robust detectors for synthesized fake voices are still in their infancy and are not ready to fully tackle this emerging threat. In this paper, we devise a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition (SR) system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices. Layer-wise neuron behaviors provide an important insight to meticulously catch the differences among inputs, …

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective …

Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of …

Marble: Model-Based Robustness Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Yi Li, Xiaofei Xie, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safetyand security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of …

Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data …

A Performance-Sensitive Malware Detection System Using Deep Learning On Mobile Devices, Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, Yang Liu

Research Collection School Of Computing and Information Systems

Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications (apps) provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing serious security threats 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 addition, the uploading process also suffers …

Novel Deep Learning Methods Combined With Static Analysis For Source Code Processing, Duy Quoc Nghi Bui

Dissertations and Theses Collection (Open Access)

It is desirable to combine machine learning and program analysis so that one can leverage the best of both to increase the performance of software analytics. On one side, machine learning can analyze the source code of thousands of well-written software projects that can uncover patterns that partially characterize software that is reliable, easy to read, and easy to maintain. On the other side, the program analysis can be used to define rigorous and unique rules that are only available in programming languages, which enrich the representation of source code and help the machine learning to capture the patterns better. …

Spark: Spatial-Aware Online Incremental Attack Against Visual Tracking, Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu

Research Collection School Of Computing and Information Systems

Adversarial attacks of deep neural networks have been intensively studied on image, audio, and natural language classification tasks. Nevertheless, as a typical while important real-world application, the adversarial attacks of online video tracking that traces an object’s moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and …

Functional Programming For Systems Software: Implementing Baremetal Programs In Habit, Donovan Ellison

University Honors Theses

Programming in a baremetal environment, directly on top of hardware with very little to help manage memory or ensure safety, can be dangerous even for experienced programmers. Programming languages can ease the burden on developers and sometimes take care of entire sets of errors. This is not the case for a language like C that will do almost anything you want, for better or worse. To operate in a baremetal environment often requires direct control over memory, but it would be nice to have that capability without sacrificing safety guarantees. Rust is a new language that aims to fit this …

Reproducible Application Platforms For Distributed Computing Systems, John Q. Wofford Iii

Computer Science ETDs

A scientific conclusion requires falsifiable evidence. Results from distributed systems research are often difficult to reproduce because these systems consist of multiple nodes, each running independent system software and communicating across inter-node devices. This work motivates, describes, and demonstrates a reproducible application platform for distributed computing systems based on a layered, container-based software stack. This system effectively moves all application software dependencies from the host to a portable container. Each layer represents a particular functionality of the software stack. The layers are modular and extensible so that results are not only repeatable, but they can also be built on to …

Memlock: Memory Usage Guided Fuzzing, Cheng Wen, Haijun Wang, Yuekang Li, Shengchao Qin, Yang Liu, Zhiwu Xu, Hongxu Chen, Xiaofei Xie, Geguang Pu, Ting Liu

Research Collection School Of Computing and Information Systems

Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the state-of-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and …

Towards Characterizing Adversarial Defects Of Deep Learning Software From The Lens Of Uncertainty, Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun

Research Collection School Of Computing and Information Systems

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. …

Typestate-Guided Fuzzer For Discovering Use-After-Free Vulnerabilities, Haijun Wang, Xiaofei Xie, Yi Li, Cheng Wen, Yuekang Li, Yang Liu, Shengchao Qin, Hongxu Chen, Yulei Sui

Research Collection School Of Computing and Information Systems

Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some (long) sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we propose to model UaF vulnerabilities as typestate properties, and develop a typestate-guided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Graph Classification With Kernels, Embeddings And Convolutional Neural Networks, Monica Golahalli Seenappa, Katerina Potika, Petros Potikas

Faculty Publications, Computer Science

In the graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches. We modify a kernel graph convolutional neural network approach, that extracts subgraphs (patches) …

Black Box Analysis Of Android Malware Detectors, Guruswamy Nellaivadivelu, Fabio Di Troia, Mark Stamp

Faculty Publications, Computer Science

If a malware detector relies heavily on a feature that is obfuscated in a given malware sample, then the detector will likely fail to correctly classify the malware. In this research, we obfuscate selected features of known Android malware samples and determine whether these obfuscated samples can still be reliably detected. Using this approach, we discover which features are most significant for various sets of Android malware detectors, in effect, performing a black box analysis of these detectors. We find that there is a surprisingly high degree of variability among the key features used by popular malware detectors.

A Virtual Machine Introspection Based Multi-Service, Multi-Architecture, High-Interaction Honeypot For Iot Devices, Cory A. Nance

Masters Theses & Doctoral Dissertations

Internet of Things (IoT) devices are quickly growing in adoption. The use case for IoT devices runs the gamut from household applications (such as toasters, lighting, and thermostats) to medical, battlefield, or Industrial Control System (ICS) applications used in life or death situations. A disturbing trend is that for IoT devices is that they are not developed with security in mind. This lack of security has led to the creation of massive botnets that conduct nefarious acts. A clear understanding of the threat landscape IoT devices face is needed to address these security issues. One technique used to understand threats …

Network Traffic Analysis Framework For Cyber Threat Detection, Meshesha K. Cherie

Masters Theses & Doctoral Dissertations

The growing sophistication of attacks and newly emerging cyber threats requires advanced cyber threat detection systems. Although there are several cyber threat detection tools in use, cyber threats and data breaches continue to rise. This research is intended to improve the cyber threat detection approach by developing a cyber threat detection framework using two complementary technologies, search engine and machine learning, combining artificial intelligence and classical technologies.

In this design science research, several artifacts such as a custom search engine library, a machine learning-based engine and different algorithms have been developed to build a new cyber threat detection framework based …

Evolution Of Integration, Build, Test, And Release Engineering Into Devops And To Devsecops, Vishnu Pendyala

Faculty Research, Scholarly, and Creative Activity

Software engineering operations in large organizations are primarily comprised of integrating code from multiple branches, building, testing the build, and releasing it. Agile and related methodologies accelerated the software development activities. Realizing the importance of the development and operations teams working closely with each other, the set of practices that automated the engineering processes of software development evolved into DevOps, signifying the close collaboration of both development and operations teams. With the advent of cloud computing and the opening up of firewalls, the security aspects of software started moving into the applications leading to DevSecOps. This chapter traces the journey …

Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. …

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 …

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 …

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 …

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 …

Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu

Research Collection School Of Computing and Information Systems

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …