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Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan JIA, Jingyi WANG, Christopher M. POSKITT, Sudipta CHATTOPADHYAY, Jun SUN, Yuqi CHEN 2021 Singapore Management University

Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we …


Interdomain Route Leak Mitigation: A Pragmatic Approach, Benjamin Tyler McDaniel 2021 University of Tennessee, Knoxville

Interdomain Route Leak Mitigation: A Pragmatic Approach, Benjamin Tyler Mcdaniel

Doctoral Dissertations

The Internet has grown to support many vital functions, but it is not administered by any central authority. Rather, the many smaller networks that make up the Internet - called Autonomous Systems (ASes) - independently manage their own distinct host address space and routing policy. Routers at the borders between ASes exchange information about how to reach remote IP prefixes with neighboring networks over the control plane with the Border Gateway Protocol (BGP). This inter-AS communication connects hosts across AS boundaries to build the illusion of one large, unified global network - the Internet. Unfortunately, BGP is a dated protocol …


Neural Architecture Search Of Spd Manifold Networks, R.S. SUKTHANKER, Zhiwu HUANG, S. KUMAR, E. G. ENDSJO, Y. WU, Gool L. VAN 2021 Singapore Management University

Neural Architecture Search Of Spd Manifold Networks, R.S. Sukthanker, Zhiwu Huang, S. Kumar, E. G. Endsjo, Y. Wu, Gool L. Van

Research Collection School Of Computing and Information Systems

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem with a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition …


Explainable Deep Few-Shot Anomaly Detection With Deviation Networks, Guansong PANG, Choubo DING, Chunhua SHEN, Anton Van Den HENGEL 2021 Singapore Management University

Explainable Deep Few-Shot Anomaly Detection With Deviation Networks, Guansong Pang, Choubo Ding, Chunhua Shen, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples due to the lack of the knowledge about the anomalies. Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models. To address this problem, we introduce a novel weakly-supervised anomaly detection framework to train detection models without assuming the examples illustrating all possible classes of anomaly.Specifically, the …


The 4th Workshop On Heterogeneous Information Network Analysis And Applications (Hena 2021), Chuan SHI, Yuan FANG, Yanfang YE, Jiawei ZHANG 2021 Singapore Management University

The 4th Workshop On Heterogeneous Information Network Analysis And Applications (Hena 2021), Chuan Shi, Yuan Fang, Yanfang Ye, Jiawei Zhang

Research Collection School Of Computing and Information Systems

The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021) is co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The goal of this workshop is to bring together researchers and practitioners in the field and provide a forum for sharing new techniques and applications in heterogeneous information network analysis. This workshop has an exciting program that spans a number of subtopics, such as heterogeneous network embedding and graph neural networks, data mining techniques on heterogeneous information networks, and applications of heterogeneous information network analysis. The workshop program includes several invited speakers, lively discussion …


Deeprepair: Style-Guided Repairing For Deep Neural Networks In The Real-World Operational Environment, Bing YU, Hua QI, Guo QING, Felix JUEFEI-XU, Xiaofei XIE, Lei MA, Jianjun ZHAO 2021 Singapore Management University

Deeprepair: Style-Guided Repairing For Deep Neural Networks In The Real-World Operational Environment, Bing Yu, Hua Qi, Guo Qing, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise, etc. Hence, it poses a rather important problem for the DNNs' real-world applications: how to repair the deployed DNNs for correcting the failure samples under the deployed operational environment while not harming their capability of handling normal or clean data with limited …


Toward Deep Supervised Anomaly Detection: Reinforcement Learning From Partially Labeled Anomaly Data, Guansong PANG, Anton Van Den HENGEL, Chunhua SHEN, Longbing CAO 2021 Singapore Management University

Toward Deep Supervised Anomaly Detection: Reinforcement Learning From Partially Labeled Anomaly Data, Guansong Pang, Anton Van Den Hengel, Chunhua Shen, Longbing Cao

Research Collection School Of Computing and Information Systems

We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies. This approach learns the known abnormality by automatically interacting with an anomalybiased simulation environment, while continuously extending the …


Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu TIAN, Felix JUEFEI-XU, Qing GUO, Xiaofei XIE, Xiaohong LI, Yang LIU 2021 Singapore Management University

Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Research Collection School Of Computing and Information Systems

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can …


Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi CHEN, Christopher M. POSKITT, Jun SUN 2021 Singapore Management University

Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important …


Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei ZHANG, Shan JIN, Wanli XUE, Xiaofei XIE, Shengyong CHEN, Rong CHEN 2021 Singapore Management University

Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen

Research Collection School Of Computing and Information Systems

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We …


An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming YE, Ke CHEN, Xiaofei XIE, Lei MA, Ruochen HUANG, Yingfeng CHEN, Yinxing XUE, Jianjun ZHAO 2021 Singapore Management University

An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao

Research Collection School Of Computing and Information Systems

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges …


Estimating Homophily In Social Networks Using Dyadic Predictions, George BERRY, Antonio SIRIANNI, Ingmar WEBER, Jisun AN, Michael MACY 2021 Cornell University

Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy

Research Collection School Of Computing and Information Systems

Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors …


Forecasting Interaction Order On Temporal Graphs, Wenwen XIA, Yuchen LI, Jianwei TIAN, Shenghong LI 2021 Singapore Management University

Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis and the topic has been studied extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a binary classification problem about two nodes. However, for temporal graphs, links (or interactions) among node sets appear in sequential orders. And the orders may lead to interesting applications. While a binary link prediction formulation fails to handle such an order-sensitive case. In this paper, we focus on such an interaction order prediction (IOP) problem among a given node set on temporal graphs. For the technical aspect, we develop a graph neural …


Claim: Curriculum Learning Policy For Influence Maximization In Unknown Social Networks, Dexun LI, MEGHNA LOWALEKAR, Pradeep VARAKANTHAM 2021 Singapore Management University

Claim: Curriculum Learning Policy For Influence Maximization In Unknown Social Networks, Dexun Li, Meghna Lowalekar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information. Recently, it has also found application in HIV prevention, substance abuse prevention, micro-finance adoption, etc., where the goal is to identify the set of peer leaders in a real-world physical social network who can disseminate information to a large group of people. Unlike online social networks, real-world networks are not completely known, and collecting information about the network is costly as it involves surveying multiple people. In this paper, we focus on this problem of network discovery for …


Bias Field Poses A Threat To Dnn-Based X-Ray Recognition, Bingyu TIAN, Qing GUO, Felix JUEFEI-XU, Wen Le CHAN, Yupeng CHENG, Xiaohong LI, Xiaofei XIE, Shengchao QIN 2021 Singapore Management University

Bias Field Poses A Threat To Dnn-Based X-Ray Recognition, Bingyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin

Research Collection School Of Computing and Information Systems

Chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. Many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, posing a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the adversarial attack and propose a brand new attack, i.e., adversarial bias field attack where …


Optimization Planning For 3d Convnets, Zhaofan QIU, Ting YAO, Chong-wah NGO, Tao MEI 2021 Singapore Management University

Optimization Planning For 3d Convnets, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

It is not trivial to optimally learn a 3D Convolutional Neural Networks (3D ConvNets) due to high complexity and various options of the training scheme. The most common hand-tuning process starts from learning 3D ConvNets using short video clips and then is followed by learning long-term temporal dependency using lengthy clips, while gradually decaying the learning rate from high to low as training progresses. The fact that such process comes along with several heuristic settings motivates the study to seek an optimal "path" to automate the entire training. In this paper, we decompose the path into a series of training …


Traffic Engineering In Planet-Scale Cloud Networks, Rachee Singh 2021 University of Massachusetts Amherst

Traffic Engineering In Planet-Scale Cloud Networks, Rachee Singh

Doctoral Dissertations

Cloud wide-area networks (WANs) play a key role in enabling high performance applications on the Internet. Cloud providers like Amazon, Google and Microsoft, spend over hundred million dollars annually to design, provision and operate their WANs to fulfill the low-latency, high-bandwidth communication demands of their clients. In the last decade, cloud providers have rapidly expanded their datacenter deployments, network equipment and backbone capacity, preparing their infrastructure to meet the growing client demands. This dissertation re-examines the design and operation choices made by cloud providers in this phase of exponential growth along the axes of network performance, reliability and operational expenditure. …


Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin 2021 Independent Researcher

Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin

Journal of Digital Forensics, Security and Law

The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically allocated data in the Windows OS kernel and demonstrate the capacity of MemoryRanger to prevent these attacks. This paper discusses three new hijacking attacks on kernel data, which are based on bypassing OS security mechanisms. The first two hijacking attacks result in illegal access to files open in exclusive access. The third attack escalates process privileges, without applying …


Counting And Sampling Small Structures In Graph And Hypergraph Data Streams, Themistoklis Haris 2021 Dartmouth College

Counting And Sampling Small Structures In Graph And Hypergraph Data Streams, Themistoklis Haris

Dartmouth College Undergraduate Theses

In this thesis, we explore the problem of approximating the number of elementary substructures called simplices in large k-uniform hypergraphs. The hypergraphs are assumed to be too large to be stored in memory, so we adopt a data stream model, where the hypergraph is defined by a sequence of hyperedges.

First we propose an algorithm that (ε, δ)-estimates the number of simplices using O(m1+1/k / T) bits of space. In addition, we prove that no constant-pass streaming algorithm can (ε, δ)- approximate the number of simplices using less than O( m 1+1/k / T ) bits of space. Thus …


Bountychain: Toward Decentralizing A Bug Bounty Program With Blockchain And Ipfs, Alex Hoffman, Phillipe Austria, Chol Hyun Park, Yoohwan Kim 2021 University of Nevada, Las Vegas

Bountychain: Toward Decentralizing A Bug Bounty Program With Blockchain And Ipfs, Alex Hoffman, Phillipe Austria, Chol Hyun Park, Yoohwan Kim

Computer Science Faculty Publications

Bug Bounty Programs (BBPs) play an important role in providing and maintaining security in software applications. These programs allow testers to discover and resolve bugs before the general public is aware of them, preventing incidents of widespread abuse. However, they have shown problems such as organizations providing accountability of reporting bugs and nonrecognition of testers. In this paper, we discuss Bountychain, a decentralized application using Ethereum-based Smart Contracts (SCs) and the Interplanetary File System (IPFS), a distributed file storage system. Blockchain and SCs provide a safe, secure and transparent platform for a BBP. Testers can submit bug reports and organizations …


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