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Articles 481 - 510 of 4669
Full-Text Articles in Information Security
Data Recovery Beyond The Obvious Using Digital Forensic Techniques, Smit Chandrakant Nayak
Data Recovery Beyond The Obvious Using Digital Forensic Techniques, Smit Chandrakant Nayak
Theses, Dissertations and Culminating Projects
Advancement in drone technology, particularly for smaller drones, are creating new research fields and potential applications, particularly with regard to smaller drones. On the other hand, these enhancements bring forth additional hurdles in terms of adaptability, homogeneity, and safety. The purpose of this study is to investigate the science and technology behind drones, as well as their applications, the many ways in which citizens implement them, and the risks, precautions, and privacy problems that are associated with their utilization. This article discusses the existing literature, as well as the available solutions for drone cybersecurity, the security challenges related with drones …
Investigating User Awareness Of Privacy And Security Concerns In The Iot Era, Jack Ruffner
Investigating User Awareness Of Privacy And Security Concerns In The Iot Era, Jack Ruffner
ALL - Honors Theses
The Internet of Things (IoT) has had a significant impact on the way we view and interact with technology. This is especially prevalent in the areas of smart homes, smart tech, and mobile devices. However, despite the advantageous functions of IoT devices, they are accompanied by numerous security concerns that enable several severe privacy concerns. Many studies and informative articles present ideas that explain and prove the presence of the various risks associated with IoT devices and the need to address them. This thesis paper aims to explore the relationship between IoT device usage and security and privacy risks as …
Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark
Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark
Honors Theses
Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …
Cybervictimization And Depression: A Cultural Standpoint, Paige Heagy
Cybervictimization And Depression: A Cultural Standpoint, Paige Heagy
University Honors College
The author’s aim was to investigate the relationship between cybervictimization and depression, as well as using peer attachment and culture as moderators by giving questionnaires to 1347 participants (age range = 11-15 years) from India and the United States. Through four questionnaires, adolescents reported their levels of endorsement in either individualism or collectivism culture, levels of cybervictimization, levels of depression/depressive symptoms, and their levels of peer attachment. Adolescents reported that there is a significantly positive correlation between cybervictimization and depression. Differences were found according to culture and peer attachment, as well. Cybervictimization has begun to erupt worldwide as internet usage …
Guardians Of The Data: Government Use Of Ai And Iot In The Digital Age, Jannat Saeed
Guardians Of The Data: Government Use Of Ai And Iot In The Digital Age, Jannat Saeed
Honors Theses
The exponential growth of technology, epitomized by Moore's Law – “the observation that the number of transistors on an integrated circuit will double every two years”– has propelled the swift evolution of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. This phenomenon has revolutionized various facets of daily life, from smart home devices to autonomous vehicles, reshaping how individuals interact with the world around them. However, as governments worldwide increasingly harness these innovations to monitor and collect personal data, profound privacy concerns have arisen among the general populace. Despite the ubiquity of AI and IoT in modern society, formal …
Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia
Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia
Research Collection School Of Computing and Information Systems
Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …
Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen
Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen
Research Collection School Of Computing and Information Systems
With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …
Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang
Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …
Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar
Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar
Research Collection School Of Computing and Information Systems
Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of …
Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas
Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas
Graduate Theses and Dissertations (2019 - present)
This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …
Examining Outcomes Of Privacy Risk And Brand Trust On The Adoption Of Consumer Smart Devices, Marianne C. Loes
Examining Outcomes Of Privacy Risk And Brand Trust On The Adoption Of Consumer Smart Devices, Marianne C. Loes
Graduate Theses and Dissertations (2019 - present)
With more connected devices on earth than there are people, Internet of Things (IoT) is arguably just as innovative as the original introduction of the Internet. Though much of the research on technology acceptance and adoption has been conducted in organizational settings, the consumer use of IoT technologies, such as smart devices, is becoming a fertile field of research. The merger of these research streams is especially relevant from a societal perspective as smart devices become more embedded in consumer’s daily lives, particularly with the introduction of the “meta verse.” While original technology acceptance research is limited to two system-specific …
Agriculture 4.0 And Beyond: Evaluating Cyber Threat Intelligence Sources And Techniques In Smart Farming Ecosystems, Hang T. Bui, Hamed Aboutorab, Arash Mahboubi, Yansong Gao, Nazatul H. Sultan, Aufeef Chauhan, Mohammad Z. Parvez, Michael Bewong, Rafiqul Islam, Zahid Islam, Seyit A. Camtepe, Praveen Gauravaram, Dineshkumar Singh, M. A. Babar, Shihao Yan
Agriculture 4.0 And Beyond: Evaluating Cyber Threat Intelligence Sources And Techniques In Smart Farming Ecosystems, Hang T. Bui, Hamed Aboutorab, Arash Mahboubi, Yansong Gao, Nazatul H. Sultan, Aufeef Chauhan, Mohammad Z. Parvez, Michael Bewong, Rafiqul Islam, Zahid Islam, Seyit A. Camtepe, Praveen Gauravaram, Dineshkumar Singh, M. A. Babar, Shihao Yan
Research outputs 2022 to 2026
The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information …
Policy-Based Remote User Authentication From Multi-Biometrics, Yangguang Tian, Yingjiu Li, Robert H. Deng, Guomin Yang, Nan Li
Policy-Based Remote User Authentication From Multi-Biometrics, Yangguang Tian, Yingjiu Li, Robert H. Deng, Guomin Yang, Nan Li
Research Collection School Of Computing and Information Systems
In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user's authentication request if and only if the user's multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies.
A Survey On Searchable Symmetric Encryption, Feng Li, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Jianting Ning, Robert H. Deng
A Survey On Searchable Symmetric Encryption, Feng Li, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Jianting Ning, Robert H. Deng
Research Collection School Of Computing and Information Systems
Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state-of-the-art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past …
Baffle : Hiding Backdoors In Offline Reinforcement Learning Datasets, Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Kecen Li, Arunesh Sinha, Bowen Xu, Xinwen Hou, David Lo, Tianhao Wang
Baffle : Hiding Backdoors In Offline Reinforcement Learning Datasets, Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Kecen Li, Arunesh Sinha, Bowen Xu, Xinwen Hou, David Lo, Tianhao Wang
Research Collection School Of Computing and Information Systems
Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given …
A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka
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 …
Binder, Tyler A. Peaster, Lindsey M. Davenport, Madelyn Little, Alex Bales
Binder, Tyler A. Peaster, Lindsey M. Davenport, Madelyn Little, Alex Bales
ATU Scholars Symposium
Binder is a mobile application that aims to introduce readers to a book recommendation service that appeals to devoted and casual readers. The main goal of Binder is to enrich book selection and reading experience. This project was created in response to deficiencies in the mobile space for book suggestions, library management, and reading personalization. The tools we used to create the project include Visual Studio, .Net Maui Framework, C#, XAML, CSS, MongoDB, NoSQL, Git, GitHub, and Figma. The project’s selection of books were sourced from the Google Books repository. Binder aims to provide an intuitive interface that allows users …
Techniques To Detect Fake Profiles On Social Media Using The New Age Algorithms – A Survey, A K M Rubaiyat Reza Habib, Edidiong Elijah Akpan
Techniques To Detect Fake Profiles On Social Media Using The New Age Algorithms – A Survey, A K M Rubaiyat Reza Habib, Edidiong Elijah Akpan
ATU Scholars Symposium
This research explores the growing issue of fake accounts in Online Social Networks [OSNs]. While platforms like Twitter, Instagram, and Facebook foster connections, their lax authentication measures have attracted many scammers and cybercriminals. Fake profiles conduct malicious activities, such as phishing, spreading misinformation, and inciting social discord. The consequences range from cyberbullying to deceptive commercial practices. Detecting fake profiles manually is often challenging and causes considerable stress and trust issues for the users. Typically, a social media user scrutinizes various elements like the profile picture, bio, and shared posts to identify fake profiles. These evaluations sometimes lead users to conclude …
What Students Have To Say On Data Privacy For Educational Technology, Stephanie Choi
What Students Have To Say On Data Privacy For Educational Technology, Stephanie Choi
Cybersecurity Undergraduate Research Showcase
The literature on data privacy in terms of educational technology is a growing area of study. The perspective of educators has been captured extensively. However, the literature on students’ perspectives is missing, which is what we explore in this paper. We use a pragmatic qualitative approach with an experiential lens to capture students’ attitudes towards data privacy in terms of educational technology. We identified preliminary, common themes that appeared in the survey responses. The paper concludes by calling for more research on how students perceive data privacy in terms of educational technology.
A Case Study Of The Crashoverride Malware, Its Effects And Possible Countermeasures, Samuel Rector
A Case Study Of The Crashoverride Malware, Its Effects And Possible Countermeasures, Samuel Rector
Cybersecurity Undergraduate Research Showcase
CRASHOVERRIDE is a modular malware tailor-made for electric grid Industrial Control System (ICS) equipment and was deployed by a group named ELECTRUM in a Ukrainian substation. The malware would launch a protocol exploit to flip breakers and would then wipe the system of ICS files. Finally, it would execute a Denial Of Service (DOS) attack on protective relays. In effect, months of damage and thousands out of power. However, due to oversights the malware only caused a brief power outage. Though the implications of the malware are cause for researching and implementing countermeasures against others to come. The CISA recommends …
Investigating Vulnerabilities In The Bluetooth Host Layer In Linux, Jack Dibari
Investigating Vulnerabilities In The Bluetooth Host Layer In Linux, Jack Dibari
Cybersecurity Undergraduate Research Showcase
This paper investigates vulnerabilities within the Bluetooth host layer in Linux systems. It examines the Bluetooth protocol's evolution, focusing on its implementation in Linux, particularly through the BlueZ host software. Various vulnerabilities, including BleedingTooth, BLESA, and SweynTooth, are analyzed.
Data Profits Vs. Privacy Rights: Ethical Concerns In Data Commerce, Amiah Armstrong
Data Profits Vs. Privacy Rights: Ethical Concerns In Data Commerce, Amiah Armstrong
Cybersecurity Undergraduate Research Showcase
In today’s digital age, the collection and sale of customer data for advertising is gaining a growing number of ethical concerns. The act of amassing extensive datasets encompassing customer preferences, behaviors, and personal information raises questions of its true purpose. It is widely acknowledged that companies track and store their customer’s digital activities under the pretext of benefiting the customer, but at what cost? Are users aware of how much of their data is being collected? Do they understand the trade-off between personalized services and the potential invasion of their privacy? This paper aims to show the advantages and disadvantages …
Comparing Cognitive Theories Of Learning Transfer To Advance Cybersecurity Instruction, Assessment, And Testing, Daniel T. Hickey Ph.D., Ronald J. Kantor
Comparing Cognitive Theories Of Learning Transfer To Advance Cybersecurity Instruction, Assessment, And Testing, Daniel T. Hickey Ph.D., Ronald J. Kantor
Journal of Cybersecurity Education, Research and Practice
The cybersecurity threat landscape evolves quickly, continually, and consequentially. This means that the transfer of cybersecurity learning is crucial. We compared how different recognized “cognitive” transfer theories might help explain and synergize three aspects of cybersecurity education. These include teaching and training in diverse settings, assessing learning formatively & summatively, and testing & measuring achievement, proficiency, & readiness. We excluded newer sociocultural theories and their implications for inclusion as we explore those theories elsewhere. We first summarized the history of cybersecurity education and proficiency standards considering transfer theories. We then explored each theory and reviewed the most relevant cybersecurity education …
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Thesis/ Dissertation Defenses
In recent years, artificial intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …
Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Alalawi
Thesis/ Dissertation Defenses
In today's interconnected world, individuals, private corporations, public institutions, and governments face increasingly sophisticated cyber threats and attacks, highlighting the critical need for individuals and organizations to understand cybersecurity comprehensively. Cyberattacks have affected many countries and infrastructures in different sectors worldwide, including the United Arab Emirates (UAE), which has become a main target for cybercrime due to its booming economy and tourism. The UAE considers cybersecurity an increasingly critical issue in our digital world, and increasing cybersecurity awareness among residents is essential to protect themselves and their organizations from cyberattacks. The primary objectives of this study are to identify key …
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Theses
In recent years, Artificial Intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …
Improving Ethics Surrounding Collegiate-Level Hacking Education: Comprehensive Implementation Plan And Affiliation With Peer-Led Initiatives, Shannon O. Morgan
Improving Ethics Surrounding Collegiate-Level Hacking Education: Comprehensive Implementation Plan And Affiliation With Peer-Led Initiatives, Shannon O. Morgan
ALL - Honors Theses
The purpose of this research study is to analyze the ethical ramifications of hacking education on the collegiate level in relation to cybersecurity majors and minors. Educators and university officials must take action to prevent the misuse of the information, skills, and knowledge gained from being a student in a cyber-related course. As the dependence on technology rises, it is crucial that future technology professionals are effectively trained and taught the ins and outs of the field to better protect users from becoming a victim of a cyberattack. The outcome of this study is to compile a comprehensive list of …
Gradual Memory Safety, Jack Phillips
Gradual Memory Safety, Jack Phillips
All NMU Master's Theses
This paper extends the theory of Gradual Types to include memory safe Region-Types and Region-Based Memory Management. It also makes advancements in the capabilities of Region-Based systems. Lastly, it presents the Svejk language and Hasek Type System.
Network Level Detection Of Iot Attacks Via Time Series Shape Mining, Srijani Basu
Network Level Detection Of Iot Attacks Via Time Series Shape Mining, Srijani Basu
Masters Theses
This research proposes a method, for detecting intrusions in the Internet of Things (IoT) realm. This approach was specifically tested using datasets containing both benign, and attacks on smart home devices like the Belkin Wemo Power Switch, Lifx Smart Bulb, Amazon Echo, and Netatmo Welcome Camera. These attacks consist of specification-compliant volumetric DDoS attacks, which can be both direct and reflective, with very low traffic volumes implemented in an ON-OFF pattern. Here, the ON-OFF pattern can be referred to as a pulse attack strategy.
We combined non-linear statistical moving averages, and Dynamic Time Warping into a single framework that can …
Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Humaid Alalawi
Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Humaid Alalawi
Theses
In today's interconnected world, individuals, private corporations, public institutions, and governments face increasingly sophisticated cyber threats and attacks, highlighting the critical need for individuals and organizations to understand cybersecurity comprehensively. Cyberattacks have affected many countries and infrastructures in different sectors worldwide, including the United Arab Emirates (UAE), which has become a main target for cybercrime due to its booming economy and tourism. The UAE considers cybersecurity an increasingly critical issue in our digital world, and increasing cybersecurity awareness among residents is essential to protect themselves and their organizations from cyberattacks. The primary objectives of this study are to identify key …