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An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris 2024 University of Arkansas, Fayetteville

An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris

Computer Science and Computer Engineering Undergraduate Honors Theses

Phasor measurement unit (PMU) systems often experience unavoidable missing and erroneous measurements, which undermine power system observability and operational effectiveness. Traditional solutions for recovering missing PMU data employ a centralized approach at the control center, resulting in lengthy recovery times due to data transmission and aggregation. In this work, we leverage P4-based programmable networks to expedite missing data recovery. Our approach utilizes the data plane programmability offered by P4 to present an in-network solution for PMU data recovery. We establish a data-plane pipeline on P4 switches, featuring a customized PMU protocol parser, a missing data detection module, and an auto-regressive …


A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka 2024 William & Mary

A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka

Cybersecurity Undergraduate Research Showcase

The internet needs secure forms of identity authentication to function properly, but identity authentication is not a core part of the internet’s architecture. Instead, approaches to identity verification vary, often using centralized stores of identity information that are targets of cyber attacks. Decentralized identity is a secure way to manage identity online that puts users’ identities in their own hands and that has the potential to become a core part of cybersecurity. However, decentralized identity technology is new and continually evolving, which makes implementing this technology in an organizational setting challenging. This paper suggests that, in the future, decentralized identity …


Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian GAO, Yu QU, Sheng YU, Yue DUAN, Heng YIN 2024 Singapore Management University

Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Pseudocode diffing precisely locates similar parts and captures differences between the decompiled pseudocode of two given binaries. It is particularly useful in many security scenarios such as code plagiarism detection, lineage analysis, patch, vulnerability analysis, etc. However, existing pseudocode diffing and binary diffing tools suffer from low accuracy and poor scalability, since they either rely on manually-designed heuristics (e.g., Diaphora) or heavy computations like matrix factorization (e.g., DeepBinDiff). To address the limitations, in this paper, we propose a semantics-aware, deep neural network-based model called SIGMADIFF. SIGMADIFF first constructs IR (Intermediate Representation) level interprocedural program dependency graphs (IPDGs). Then it uses …


Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong 2024 Kyung Hee University, South Korea

Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong

School of Cybersecurity Faculty Publications

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound …


Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi NIU, Guansong PANG, Ling CHEN 2024 Singapore Management University

Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this …


Lora Gateway Coverage And Capacity Analysis For Supporting Monitoring Passive Infrastructure Fiber Optic In Urban Area, I Ketut Agung Enriko, Fikri Nizar Gustiyana, Gede Chandrayana Giri 2023 Institut Teknologi telkom Purwokerto, Indonesia

Lora Gateway Coverage And Capacity Analysis For Supporting Monitoring Passive Infrastructure Fiber Optic In Urban Area, I Ketut Agung Enriko, Fikri Nizar Gustiyana, Gede Chandrayana Giri

Elinvo (Electronics, Informatics, and Vocational Education)

In the era of digital transformation, telecommunications infrastructure has become the backbone of global connectivity. Optical Distribution Cabinet (ODC) is a crucial part of an optical network that distributes signals to various points in the network. Maintenance and monitoring of ODCs have become essential to ensure optimal availability and performance. However, conventional approaches are often expensive and difficult to implement. The objective of this study is to develop a LoRaWAN network with the purpose of determining the required number of gateways. Additionally, the research aims to devise an IoT-basedODC device monitoring system within the FTTH network, utilizing data from PT. …


Deep Learning For Photovoltaic Characterization, Adrian Manuel de Luis Garcia 2023 University of Arkansas-Fayetteville

Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia

Graduate Theses and Dissertations

This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …


Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson 2023 Christopher Newport University

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …


Robust Test Selection For Deep Neural Networks, Weifeng SUN, Meng YAN, Zhongxin LIU, David LO 2023 Chongqing University

Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance …


The Propagation And Execution Of Malware In Images, Piper Hall 2023 Christopher Newport University

The Propagation And Execution Of Malware In Images, Piper Hall

Cybersecurity Undergraduate Research Showcase

Malware has become increasingly prolific and severe in its consequences as information systems mature and users become more reliant on computing in their daily lives. As cybercrime becomes more complex in its strategies, an often-overlooked manner of propagation is through images. In recent years, several high-profile vulnerabilities in image libraries have opened the door for threat actors to steal money and information from unsuspecting users. This paper will explore the mechanisms by which these exploits function and how they can be avoided.


Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-hwee TAN, Ze JI 2023 Singapore Management University

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …


Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu SUN, Kevin ALLIX, Kisub KIM, Xin ZHOU, Dongsun KIM, David LO, Tegawendé F. BISSYANDE, Jacques KLEIN 2023 Singapore Management University

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …


Predicting Network Failures With Ai Techniques, Chandrika Saha 2023 The University of Western Ontario

Predicting Network Failures With Ai Techniques, Chandrika Saha

Electronic Thesis and Dissertation Repository

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …


Improving User Experience By Optimizing Cloud Services, Ishita Dasgupta 2023 University of Massachusetts Amherst

Improving User Experience By Optimizing Cloud Services, Ishita Dasgupta

Doctoral Dissertations

Today, cloud services offer myriads of applications, tailor made for different users in the field of weather, health, finance, entertainment, etc. These services fulfill varying genres of user demands over the Internet. For example, these services can be live (live weather radar, ESPN Live) or on-demand services (weather forecasting, Netflix). While these applications cater to different customer requirements, it is necessary for these services to be efficient with respect to latency, scalability, robustness and quality of experience. These systems need to constantly evolve to provide the best user experience and meet the most current demands of the customer. For instance, …


Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce ZHANG, Rex YING, Hady Wirawan LAUW 2023 Singapore Management University

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …


The Role Of The Family In Confronting The Excessive Use Of Modern Technology Among Children "Therapeutic Alternatives", Khaled Mikhlif Al-Jenfawi 2023 Journal of Police and Legal Sciences

The Role Of The Family In Confronting The Excessive Use Of Modern Technology Among Children "Therapeutic Alternatives", Khaled Mikhlif Al-Jenfawi

Journal of Police and Legal Sciences

This study aimed to identify the role of the family in confronting the excessive use of technology and social media programs from the view point of social workers and psychologists working for the Juvenile Welfare Department of the Ministry of Social Affairs and Labor in Kuwait, in the light of some variables (sex , and practical experience)

The studywas a descriptive analytical study. It used the social survey method. A questionnaire consisting of (39) items was built and designed, and its validity and reliability were tested. Among the most important results of the study: The level of the family's role …


Mechanisms To Reduce Cyber Threats And Risks, Saad alsuwaileh 2023 Journal of Police and Legal Sciences

Mechanisms To Reduce Cyber Threats And Risks, Saad Alsuwaileh

Journal of Police and Legal Sciences

Addressing the mechanisms of reducing cyber threats and risks Research Because cyberspace is an important arena for various international interactions, especially in recent times in light of the increase in cyber-attacks between some countries, which affects their national security. In this context, many countries are trying to make an effort to develop their capabilities to be used in any cyber-attack, or to take adequate preventive measures to protect them from any possible cyberattacks, especially in light of the impact of these attacks on vital places and institutions such as banks and ministries or on important facilities such as water and …


Future Trends And Directions For Secure Infrastructure Architecture In The Education Sector: A Systematic Review Of Recent Evidence, Isaac Atta Senior Ampofo, Isaac Atta Junior Ampofo 2023 Kwame Nkrumah University of Science and Technology

Future Trends And Directions For Secure Infrastructure Architecture In The Education Sector: A Systematic Review Of Recent Evidence, Isaac Atta Senior Ampofo, Isaac Atta Junior Ampofo

Journal of Research Initiatives

The most efficient approach to giving large numbers of students’ access to computational resources is through a data center. A contemporary method for building the data center's computer infrastructure is the software-defined model, which enables user tasks to be processed in a reasonable amount of time and at a reasonable cost. The researcher examines potential directions and trends for a secured infrastructure design in this article. Additionally, interoperable, highly reusable modules that can include the newest trends in the education industry are made possible by cloud-based educational software. The Reference Architecture for University Education System Using AWS Services is presented …


Seed Selection For Testing Deep Neural Networks, Yuhan ZHI, Xiaofei XIE, Chao SHEN, Jun SUN, Xiaoyu ZHANG, Xiaohong GUAN 2023 Singapore Management University

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …


Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi CHEN, Yixin CAO, Yan ZHANG, Zhiwei LIU 2023 Singapore Management University

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …


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