OS and Networks Commons^™

Asem 4.0/5.0 - Evolving The Engineering Management Profession Through Industry 4.0/5.0 Collaborative Networks, T. Steven Cotter, Faisal Mahmud, Ziniya Zahedi

Engineering Management & Systems Engineering Faculty Publications

The American Society for Engineering Management was created and matured under Industry 3.0 automation. The emergence of Industry 4.0 and 5.0 are forcing all organizational sectors to rethink their long-term strategy with respect to emerging horizontal/vertical cyber-physical systems integration. This leaves open the question of the directions in which ASEM should evolve into the 21st century. This paper reports an initial mapping of Industry 4.0 and 5.0 technologies and initiatives as goal-oriented, long-term strategic collaborative networks. The research method began with the Boston Consulting Group nine technologies of Industry 4.0 (2015) and the Industry 5.0 technologies within its human-centric, sustainability, …

Privacy And Security Of The Windows Registry, Edward L. Amoruso

Graduate Thesis and Dissertation 2023-2024

The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …

Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures, Aymar Le Père Tchimwa Bouom, Jean-Pierre Lienou, Wilson Ejuh Geh, Frederica Nelson, Sachin Shetty, Charles Kamhoua

VMASC Publications

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node …

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 …

Optimal Network Analysis Through Vertex Order Coloring Of Intuitionistic Fuzzy Graph Operations, A. Meenakshi, S. Dhanushiya, Hong Qin, Maniyandy Elangovan

Data Science Faculty Publications

Intuitionistic fuzzy graphs IFGs are a powerful tool for modeling uncertainty and complex relationships. They offer versatile frameworks for addressing real-world challenges. In this research, we have introduced intuitionistic fuzzy vertex order coloring IFVOC and analyzed the alpha-strong (alpha str), beta-strong (beta str), and gamma-strong (gamma str) vertices through their degree. We explored important theorems based on the types of strong vertices, broadening the scope of our study. We analyzed multiple IFG products to determine the most optimal network based on some important metrics, including the weight and total number of alpha str vertices, the chromatic number, and the weight …

A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi

School of Cybersecurity Faculty Publications

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …

Enhancing The Efficiency And Scalability Of Cloud Networking Systems, Jiaxin Lei

Computer Science and Engineering Dissertations - Archive

Overlay networks are the de facto network virtualization technique for providing flexible and customized connectivity among distributed containers in the cloud. Despite their widespread adoption, overlay networks incur significant overhead due to their complexity, resulting in notable performance degradation compared to physical networks.

In this dissertation, I present our three-stage solutions aimed at addressing the challenges of efficiency and scalability in cloud-based container overlay networks: Firstly, we conduct a comprehensive empirical performance study of container overlay networks, identifying crucial parallelization bottlenecks within the kernel network stack. Our observations and root cause analysis uncover that these inefficiencies primarily arise from the …

A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …

Mitigating Cyber Espionage: A Network Security Strategy Using Notifications, Claire Headland

Williams Honors College, Honors Research Projects

Network security and its mitigation of cyber espionage is paramount to the confidentiality, integrity, and availability of data within the intelligence field. With the advancing efficacy of social engineering to execute cyber espionage attacks, further measures and fail-safe mechanisms have become necessary. If a malicious actor successfully penetrates the network, suspending confidential data transmissions over the compromised network becomes crucial. However, connected users need a platform to receive security notifications and, therefore, need to know that their continued network use compromises more data. This project eliminates this by achieving two primary objectives: designing a multi- layered, hardened, and segmented network …

Dynamic Memory Management For Key-Value Store, Yuchen Wang

Dissertations, Master's Theses and Master's Reports

To minimize the latency of accessing back-end servers, modern web services often use in-memory key-value (k-v) stores at the front end to cache frequently accessed objects. Due to the limited memory capacity, these stores must be configured with a fixed amount of memory. Consequently, cache replacement is required when the footprint of the accessed objects exceeds the cache size.

This thesis presents a comprehensive exploration of advanced dynamic memory management techniques for k-v stores. The first study conducts a detailed analysis of K-LRU, a random sampling-based replacement policy, proposing a dynamic K configuration scheme to exploit the potential miss ratio …

Introducing Flexible Assessment Into A Computer Networks Course: A Case Study, Joe Meehean

Journal of Mathematics and Science: Collaborative Explorations

With overall positive results and limited drawbacks, I have adapted modern pedagogical techniques to address a common difficulty encountered when teaching a computer networks course. Due to the tiered nature of the skills taught in the course, students often fail unnecessarily. Using mastery learning, competency-based education, and specifications grading as a foundation, I have developed a course that allows students with varied skills and abilities to pass. The heart of this approach is the flexible assessment of programming assignments which eliminates due dates and allows students to have their work graded and regraded without penalty. Flexible assessment also defines an …

Addressing Spectral Bias Of Deep Neural Networks By Multi-Grade Deep Learning, Ronglong Fang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Deep neural networks (DNNs) have showcased their remarkable precision in approximating smooth functions. However, they suffer from the spectral bias, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency components of a function, struggling to effectively capture its high-frequency features. This paper is to address this issue. Notice that a function having only low frequency components may be well-represented by a shallow neural network (SNN), a network having only a few layers. By observing that composition of low frequency functions can effectively approximate a high-frequency function, we propose to learn a function containing high-frequency components by composing …

A Signal Injection Attack Against Zero Involvement Pairing And Authentication For The Internet Of Things, Isaac Ahlgren, Jack West, Kyuin Lee, George K. Thiruvathukal, Neil Klingensmith

Computer Science: Faculty Publications and Other Works

Zero Involvement Pairing and Authentication (ZIPA) is a promising technique for auto-provisioning large networks of Internet-of-Things (IoT) devices. In this work, we present the first successful signal injection attack on a ZIPA system. Most existing ZIPA systems assume there is a negligible amount of influence from the unsecured outside space on the secured inside space. In reality, environmental signals do leak from adjacent unsecured spaces and influence the environment of the secured space. Our attack takes advantage of this fact to perform a signal injection attack on the popular Schurmann & Sigg algorithm. The keys generated by the adversary with …

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 …

Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …

Evaluating Social Media Reach Via Mainstream Media Discourse, Himarsha R. Jayanetti

Computer Science Faculty Publications

This study examines the intersection between social media and mainstream television (TV) news with an aim to understand how social media content amplifies its impact through TV broadcasts. While many studies emphasize social media as a primary platform for information dissemination, they often underestimate its total influence by focusing solely on interactions within the platform. This research examines instances where social media posts gain prominence on TV broadcasts, reaching new audiences and prompting public discourse. By using TV news closed captions, on-screen text recognition, and social media logo detection, we analyze how social media is referenced in TV news. Our …

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. …

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). …

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. …

Scalelong: Towards More Stable Training Of Diffusion Model Via Scaling Network Long Skip Connection, Zhongzhan Huang, Pan Zhou, Shuicheng Yan, Liang Lin

Research Collection School Of Computing and Information Systems

In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and alleviate vanishing gradient. Unfortunately, UNet often suffers from unstable training in diffusion models which can be alleviated by scaling its LSC coefficients smaller. However, theoretical understandings of the instability of UNet in diffusion models and also the performance improvement of LSC scaling remain absent yet. To solve this issue, we theoretically show that the coefficients of LSCs in UNet have big effects on the stableness of the forward and backward propagation and robustness of …

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

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.

Demo Abstract: Vgglass - Demonstrating Visual Grounding And Localization Synergy With A Lidar-Enabled Smart-Glass, Darshana Rathnayake, Dulanga Weerakoon, Meeralakshmi Radhakrishnan, Vigneshwaran Subbaraju, Inseok Hwang, Archan Misra

Research Collection School Of Computing and Information Systems

This work demonstrates the VGGlass system, which simultaneously interprets human instructions for a target acquisition task and determines the precise 3D positions of both user and the target object. This is achieved by utilizing LiDARs mounted in the infrastructure and a smart glass device worn by the user. Key to our system is the union of LiDAR-based localization termed LiLOC and a multi-modal visual grounding approach termed RealG(2)In-Lite. To demonstrate the system, we use Intel RealSense L515 cameras and a Microsoft HoloLens 2, as the user devices. VGGlass is able to: a) track the user in real-time in a global …

A Systematic Review Of The Governance Of Internet Information's Content Ecosystem In China, Weichao Li, Yi Zhou

Journal of Scientific Information Research

[Purpose/significance]This study aims to provide valuable references for future research and governance practices by reviewing the research on the governance of Internet information's content ecosystem in China.[Method/process]This paper collects literature data from the core journal library of CNKI, reveals the macro situation of this topic research by using the bibliometric method, selects the literature that meets the quality standards by using the systematic review method, summarizes relevant studies from four dimensions: governance bodies, governance objects, governance tools and governance mechanisms.[Result/conclusion]The co-governance of multi-subjects  has become the basic consensus of the academic circle. Solving the problem of subjects failure and promoting …

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 …

Achieving Efficient And Privacy-Preserving Neural Network Training And Prediction In Cloud Environments, Chuan Zhang, Chenfei Hu, Tong Wu, Liehuang Zhu, Ximeng Liu

Research Collection School Of Computing and Information Systems

The neural network has been widely used to train predictive models for applications such as image processing, disease prediction, and face recognition. To produce more accurate models, powerful third parties (e.g., clouds) are usually employed to collect data from a large number of users, which however may raise concerns about user privacy. In this paper, we propose an Efficient and Privacy-preserving Neural Network scheme, named EPNN, to deal with the privacy issues in cloud-based neural networks. EPNN is designed based on a two-cloud model and techniques of data perturbation and additively homomorphic cryptosystem. This scheme enables two clouds to cooperatively …

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 …

Lightweight Blockchains And Their Network Impact On Vehicular Ad-Hoc Network-Based Blockchain Applications, Edgar Bowlin

Electronic Theses and Dissertations

Vehicular Ad-hoc Networks (VANETs) provide networks for smart vehicles and will enable future systems to provide services that enhance the overall transportation experience. However, these applications require consideration to possible damage to both property and human life. Communication between vehicles requires data immutability and user privacies to provide safe operation of the system. Blockchains can provide these properties and more to create a more secure and decentralized system. However, a chain’s security comes from the chain length. VANETs’ ephemeral connections provide harm limits how much data can be exchanged during vehicle rendezvous. This thesis investigates lightweight blockchains that operate with …

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 …

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 …