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Torsh: Obfuscating Consumer Internet-Of-Things Traffic With A Collaborative Smart-Home Router Network, Adam Vandenbussche 2022 Dartmouth College

Torsh: Obfuscating Consumer Internet-Of-Things Traffic With A Collaborative Smart-Home Router Network, Adam Vandenbussche

Dartmouth College Undergraduate Theses

When consumers install Internet-connected "smart devices" in their homes, metadata arising from the communications between these devices and their cloud-based service providers enables adversaries privy to this traffic to profile users, even when adequate encryption is used. Internet service providers (ISPs) are one potential adversary privy to users’ incom- ing and outgoing Internet traffic and either currently use this insight to assemble and sell consumer advertising profiles or may in the future do so. With existing defenses against such profiling falling short of meeting user preferences and abilities, there is a need for a novel solution that empowers consumers to …


Coded Distributed Function Computation, Pedro J. Soto 2022 The Graduate Center, City University of New York

Coded Distributed Function Computation, Pedro J. Soto

Dissertations, Theses, and Capstone Projects

A ubiquitous problem in computer science research is the optimization of computation on large data sets. Such computations are usually too large to be performed on one machine and therefore the task needs to be distributed amongst a network of machines. However, a common problem within distributed computing is the mitigation of delays caused by faulty machines. This can be performed by the use of coding theory to optimize the amount of redundancy needed to handle such faults. This problem differs from classical coding theory since it is concerned with the dynamic coded computation on data rather than just statically …


Privacy Assessment Breakthrough: A Design Science Approach To Creating A Unified Methodology, Lisa McKee 2022 Dakota State University

Privacy Assessment Breakthrough: A Design Science Approach To Creating A Unified Methodology, Lisa Mckee

Masters Theses & Doctoral Dissertations

Recent changes have increased the need for and awareness of privacy assessments. Organizations focus primarily on Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) but rarely take a comprehensive approach to assessments or integrate the results into a privacy risk program. There are numerous industry standards and regulations for privacy assessments, but the industry lacks a simple unified methodology with steps to perform privacy assessments. The objectives of this research project are to create a new privacy assessment methodology model using the design science methodology, update industry standards and present training for conducting privacy assessments that can be …


A Dark Web Pharma Framework For A More Efficient Investigation Of Dark Web Covid-19 Vaccine Products., Francisca Afua Opoku-Boateng 2022 Dakota State University

A Dark Web Pharma Framework For A More Efficient Investigation Of Dark Web Covid-19 Vaccine Products., Francisca Afua Opoku-Boateng

Masters Theses & Doctoral Dissertations

Globally, as the COVID-19 pandemic persists, it has not just imposed a significant impact on the general well-being of individuals, exposing them to unprecedented financial hardships and online information deception. However, it has also forced consumers, buyers, and suppliers to look toward a darkened economic world – the Dark Web world – a sinister complement to the internet, driven by financial gains, where illegal goods and services are advertised sold. As the Dark Web gains an increase in recognition by normal web users during this pandemic, how to perform cybercrime investigations on the Dark Web becomes challenging for manufacturers, investigators, …


Two Project On Information Systems Capabilities And Organizational Performance, Giridhar Reddy Bojja 2022 Dakota State University

Two Project On Information Systems Capabilities And Organizational Performance, Giridhar Reddy Bojja

Masters Theses & Doctoral Dissertations

Information systems (IS), as a multi-disciplinary research area, emphasizes the complementary relationship between people, organizations, and technology and has evolved dramatically over the years. IS and the underlying Information Technology (IT) application and research play a crucial role in transforming the business world and research within the management domain. Consistent with this evolution and transformation, I develop a two-project dissertation on Information systems capabilities and organizational outcomes.

Project 1 examines the role of hospital operational effectiveness on the link between information systems capabilities and hospital performance. This project examines the cross-lagged effects on a sample of 217 hospitals measured over …


A Machine Learning Approach For Reconnaissance Detection To Enhance Network Security, Rachel Bakaletz 2022 East Tennessee State University

A Machine Learning Approach For Reconnaissance Detection To Enhance Network Security, Rachel Bakaletz

Electronic Theses and Dissertations

Before cyber-crime can happen, attackers must research the targeted organization to collect vital information about the target and pave the way for the subsequent attack phases. This cyber-attack phase is called reconnaissance or enumeration. This malicious phase allows attackers to discover information about a target to be leveraged and used in an exploit. Information such as the version of the operating system and installed applications, open ports can be detected using various tools during the reconnaissance phase. By knowing such information cyber attackers can exploit vulnerabilities that are often unique to a specific version.

In this work, we develop an …


Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary ANG, Ee-peng LIM 2022 Singapore Management University

Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Most works on financial forecasting use information directly associated with individual companies (e.g., stock prices, news on the company) to predict stock returns for trading. We refer to such company-specific information as local information. Stock returns may also be influenced by global information (e.g., news on the economy in general), and inter-company relationships. Capturing such diverse information is challenging due to the low signal-to-noise ratios, different time-scales, sparsity and distributions of global and local information from different modalities. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. …


Learning Semantically Rich Network-Based Multi-Modal Mobile User Interface Embeddings, Meng Kiat Gary ANG, Ee-peng LIM 2022 Singapore Management University

Learning Semantically Rich Network-Based Multi-Modal Mobile User Interface Embeddings, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Semantically rich information from multiple modalities - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. Moreover, each UI design is composed of inter-linked UI entities which support different functions of an application, e.g., a UI screen comprising a UI taskbar, a menu and multiple button elements. Existing UI representation learning methods unfortunately are not designed to capture multi-modal and linkage structure between UI entities. To support effective search and recommendation applications over mobile UIs, we need UI representations that integrate latent semantics present in both multi-modal information and linkages between …


Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin LIU, Yuan FANG, Yong LIU, Vincent W. Zheng 2022 Singapore Management University

Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng

Research Collection School Of Computing and Information Systems

While graph neural networks (GNNs) exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, …


Topic-Guided Conversational Recommender In Multiple Domains, Lizi LIAO, Ryuichi TAKANOBU, Yunshan MA, Xun YANG, Minlie HUANG, Tat-Seng CHUA 2022 Singapore Management University

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …


Canary: An Automated Approach To Security Scanning And Remediation, David Wiles 2022 Western Kentucky University

Canary: An Automated Approach To Security Scanning And Remediation, David Wiles

Masters Theses & Specialist Projects

Modern software has a smaller attack surface today than in the past. Memory-safe languages, container runtimes, virtual machines, and a mature web stack all contribute to the relative safety of the web and software in general compared to years ago. Despite this, we still see high-profile bugs, hacks, and outages which affect major companies and widely-used technologies. The extensive work that has gone into hardening virtualization, containerization, and commonly used applications such as Nginx still depends on the end-user to configure correctly to prevent a compromised machine.

In this paper, I introduce a tool, which I call Canary, which can …


Passing Time And Syncing Secrets: Demonstrating Covert Channel Vulnerabilities In Precision Time Protocol (Ptp), Aron J. Smith-Donovan 2022 Macalester College

Passing Time And Syncing Secrets: Demonstrating Covert Channel Vulnerabilities In Precision Time Protocol (Ptp), Aron J. Smith-Donovan

Mathematics, Statistics, and Computer Science Honors Projects

Covert channels use steganographic approaches to transfer secret digital communications; when applied to network protocols, these strategies can facilitate undetectable data exfiltration and insertion attacks. Because covert channel techniques are protocol- and implementation-specific, individual case studies are necessary to assess for vulnerabilities under different conditions. While several investigations have been published evaluating covert channel potential in infrastructure- and manufacturing-based contexts, no existing research explores Precision Time Protocol (PTP), a time synchronization protocol commonly used in industrial control systems. This study aims to fill this gap by demonstrating the feasibility of a covert channel-based attack on a PTP-enabled network.


Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong FWA 2022 Singapore Management University

Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the …


Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia LI, Chunfu JIA, Pengda QIU, Qiyuan CHEN, Jiang MING, Debin GAO 2022 Singapore Management University

Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao

Research Collection School Of Computing and Information Systems

—Code virtualization is a well-known sophisticated obfuscation technique that uses custom virtual machines (VM) to emulate the semantics of original native instructions. Commercial VM-based obfuscators (e.g., Themida and VMProtect) are often abused by malware developers to conceal malicious behaviors. Since the internal mechanism of commercial obfuscators is a black box, it is a daunting challenge for the analyst to understand the behavior of virtualized programs. To figure out the code virtualization mechanism and design deobfuscation techniques, the analyst has to perform reverse-engineering on large-scale highly obfuscated programs. This knowledge learning process suffers from painful cost and imprecision. In this project, …


Resil: Revivifying Function Signature Inference Using Deep Learning With Domain-Specific Knowledge, Yan LIN, Debin GAO, David LO 2022 Singapore Management University

Resil: Revivifying Function Signature Inference Using Deep Learning With Domain-Specific Knowledge, Yan Lin, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Function signature recovery is important for binary analysis and security enhancement, such as bug finding and control-flow integrity enforcement. However, binary executables typically have crucial information vital for function signature recovery stripped off during compilation. To make things worse, recent studies show that many compiler optimization strategies further complicate the recovery of function signatures with intended violations to function calling conventions.In this paper, we first perform a systematic study to quantify the extent to which compiler optimizations (negatively) impact the accuracy of existing deep learning techniques for function signature recovery. Our experiments show that a state-of-the-art deep learning technique has …


On Size-Oriented Long-Tailed Graph Classification Of Graph Neural Networks, Zemin LIU, Qiheng MAO, Chenghao LIU, Yuan FANG, Jianling SUN 2022 Singapore Management University

On Size-Oriented Long-Tailed Graph Classification Of Graph Neural Networks, Zemin Liu, Qiheng Mao, Chenghao Liu, Yuan Fang, Jianling Sun

Research Collection School Of Computing and Information Systems

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a …


Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui LONG, Min WU, Yong LIU, Yuan FANG, Chee Kong KWOH, Jiawei LUO, Xiaoli LI 2022 Singapore Management University

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In …


Toward Safe And Verifiable Inter-Domain Routing, Xiaozhe Shao 2022 University of Massachusetts Amherst

Toward Safe And Verifiable Inter-Domain Routing, Xiaozhe Shao

Doctoral Dissertations

Interdomain routing enables each autonomous system (AS) to decide the routes toward any destination and exchange routing information with its neighboring ASs on the Internet. The Border Gateway Protocol (BGP), as the de facto routing protocol for interdomain routing, allows the administrators/operators to independently decide the routing policies for their ASs and each AS to select routes to destinations based on the locally determined routing policies.

The Internet has evolved from a hierarchical and multi-tiered interconnection network to a meshed network, where ASs are interconnected with a dense topology and more and more potential paths can be used to reach …


Intrusion Attacks On Automotive Can And Their Detection, Halley M. Paulson 2022 University of Minnesota - Morris

Intrusion Attacks On Automotive Can And Their Detection, Halley M. Paulson

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

The main highway of communication in a vehicle is the Controller Area Network, commonly known by the acronym CAN. Any vulnerability in this network could allow bad actors to block communication between vehicle subsystems, risking the safety of the vehicle’s occupants. With the ever growing list of vulnerabilities being exposed in the CAN, it is critical to address its safety. This paper looks at one of the known vulnerabilities in the data link layer of the CAN and an Intrusion Detection System that could detect attacks on this network. We detail a few processes of the CAN, arbitration and error …


Multi-Dimensional Security Integrity Analysis Of Broad Market Internet-Connected Cameras, Mark A. Stanislav 2022 Dakota State University

Multi-Dimensional Security Integrity Analysis Of Broad Market Internet-Connected Cameras, Mark A. Stanislav

Masters Theses & Doctoral Dissertations

This study used a quantitative approach with a cross-sectional, descriptive analysis survey design to examine the adherence of 40 internet-connected cameras against three IoT security frameworks to determine their overall security posture. Relevant literature was reviewed showing that prior studies in a similar regard had limitations, such as a small sample population, singular market segment focus, and/or a lack of validation against formalized frameworks. This study resulted in a uniform and multi-dimensional set of findings with supporting evidence, leading to a mapping against selected IoT security frameworks that was then quantitatively analyzed for their relative adherence as individual cameras, across …


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