Computer Sciences Commons^™

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 …

Multi-Aspect Rule-Based Ai: Methods, Taxonomy, Challenges And Directions Towards Automation, Intelligence And Transparent Cybersecurity Modeling For Critical Infrastructures, Iqbal H. Sarker, Helge Janicke, Mohamed A. Ferrag, Alsharif Abuadbba

Research outputs 2022 to 2026

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today's cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human …

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

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 …

An Analysis And Ontology Of Teaching Methods In Cybersecurity Education, Sarah Buckley

LSU Master's Theses

The growing cybersecurity workforce gap underscores the urgent need to address deficiencies in cybersecurity education: the current education system is not producing competent cybersecurity professionals, and current efforts are not informing the non-technical general public of basic cybersecurity practices. We argue that this gap is compounded by a fundamental disconnect between cybersecurity education literature and established education theory. Our research addresses this issue by examining the alignment of cybersecurity education literature concerning educational methods and tools with education literature.

In our research, we endeavor to bridge this gap by critically analyzing the alignment of cybersecurity education literature with education theory. …

Signal-To-Image Method For Counterfeit Detection In Layered Security Paradigm, Jordan Williamson

Theses and Dissertations

National level attention, resources, and priority regarding critical infrastructure have increased in recent years. This has led to adversaries and defenders exchanging positions between fortification and exploitation. One area that continues to be vulnerable is supply chain attacks like counterfeit insertion. This work investigates the application of converting collected signals into images from devices that may be considered for these critical networks. There are several aspects regarding the conversion of signals into images - specifically Red, Green, Blue (RGB) images. The methodology proposed here is potentially ideal fit for an initial security layer by achieving comparable classification results as more …

Enhancing The Resilience Of Space Systems Against Ransomware Attacks, Petersen F. Hansen

Theses and Dissertations

As their relevance has increased in recent years, space systems have become nearly essential in modern life. They are integral in the operation of navigational systems, military operations, and have ushered in a new domain of scientific inquiry. Technological advances have enabled the miniaturization of components and increased the accessibility of satellites as they find new applications in the form of Cube Satellites. However, even as these advancements have brought satellites to new heights, their interconnectedness leaves them open to new cyber threats. Ransomware attacks are one of the most prominent and disruptive cyber threats to terrestrial systems, and while …

Developing Singapore As A Smart Nation, Josephine Teo

Asian Management Insights

Mrs Josephine Teo, Singapore’s Minister for Communications and Information, and Minister-in-charge of Smart Nation and Cybersecurity, speaks about leading the country’s Smart Nation drive.

The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein

Doctoral Dissertations and Projects

As internet technology proliferate in volume and complexity, the ever-evolving landscape of malicious cyberattacks presents unprecedented security risks in cyberspace. Cybersecurity challenges have been further exacerbated by the continuous growth in the prevalence and sophistication of cyber-attacks. These threats have the capacity to disrupt business operations, erase critical data, and inflict reputational damage, constituting an existential threat to businesses, critical services, and infrastructure. The escalating threat is further compounded by the malicious use of artificial intelligence (AI) and machine learning (ML), which have increasingly become tools in the cybercriminal arsenal. In this dynamic landscape, the emergence of offensive AI introduces …

Blockchain Applications In Higher Education Based On The Nist Cybersecurity Framework, Brady Lund Ph.D.

Journal of Cybersecurity Education, Research and Practice

This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within …

Improving Belonging And Connectedness In The Cybersecurity Workforce: From College To The Profession, Mary Beth Klinger

Journal of Cybersecurity Education, Research and Practice

This article explores the results of a project aimed at supporting community college students in their academic pursuit of an Associate of Applied Science (AAS) degree in Cybersecurity through mentorship, collaboration, skill preparation, and other activities and touch points to increase students’ sense of belonging and connectedness in the cybersecurity profession. The goal of the project was focused on developing diverse, educated, and skilled cybersecurity personnel for employment within local industry and government to help curtail the current regional cybersecurity workforce gap that is emblematic of the lack of qualified cybersecurity personnel that presently exists nationwide. Emphasis throughout the project …

Cloud Ecosystem In A Box Security Services, Chirone Gamble Jr

Theses and Dissertations

The recent years have witnessed a remarkable surge in the use of large-scale data analytics, significantly advancing research in data-driven fields [1, 2]. While these innovative technologies offer the promise of accelerated research, they concurrently introduce new security and privacy challenges, complicating collaborative efforts among researchers. These challenges are ubiquitous across various research domains, particularly in the realms of data collection, analysis, and collaboration [3, 4]. Predominantly, existing data collection and analytics platforms prioritize performance over security. This prioritization compels users to rely heavily on their collaborators for adhering to all relevant security and privacy protocols. Often, researchers find themselves …

Longitudinal Attacks Against Iterative Data Collection With Local Differential Privacy, Mehmet Emre Gürsoy

Turkish Journal of Electrical Engineering and Computer Sciences

Local differential privacy (LDP) has recently emerged as an accepted standard for privacy-preserving collection of users’ data from smartphones and IoT devices. In many practical scenarios, users’ data needs to be collected repeatedly across multiple iterations. In such cases, although each collection satisfies LDP individually by itself, a longitudinal collection of multiple responses from the same user degrades that user’s privacy. To demonstrate this claim, in this paper, we propose longitudinal attacks against iterative data collection with LDP. We formulate a general Bayesian adversary model, and then individually show the application of this adversary model on six popular LDP protocols: …

Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno

All Works

This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for …

Data Driven Trade-Off Analysis For Cybersecurity, Goskel Kucukkaya, Murat Ozer, Murat Balci, Emrah Ugurlu

Engineering Management & Systems Engineering Faculty Publications

Trade-off analysis, a specialization of systems engineering, addresses design criteria like security, cost, performance, and compliance. Monte Carlo simulations are commonly employed to generate impact scenarios for trade-off analysis combined with solution alternatives that accommodate industry-specific considerations and uncertainties. In the cyber domain, this paper proposes a methodology for data-driven trade-off analysis in cybersecurity, leveraging industry reports as primary data sources using confidentiality, integrity, and availability as trade-off analysis objectives. Distribution functions are derived to manage and model uncertainties for various industries. The approach given in this study aims to facilitate informed choices and to enhance cybersecurity decision making and …

A Survey: Emerging Cybersecurity Threats In Driverless Cars, Vaibhavi Tiwari, Dharshana Rajasekar, Jiayin Wang

School of Computing Faculty Scholarship and Creative Works

In light of the increasing adoption of autonomous vehicles in our transportation networks, it is crucial to emphasize the significance of implementing strong cybersecurity protocols. These self-driving cars, which heavily depend on state-of-the-art sensors, artificial intelligence (AI), and complex communication networks, represent a remarkable advancement in technology. However, they also bring about new vulnerabilities. While it is true that autonomous vehicles have been shown to be statistically safer than traditional cars, it is important to acknowledge the growing concern surrounding cyber threats and their potential impact on the safety and reliability of these vehicles. This paper provides a thorough examination …

The Impact Of Domain Name Server (Dns) Over Hypertext Transfer Protocol Secure (Https) On Cyber Security: Limitations, Challenges, And Detection Techniques, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Muhammad Haris, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic …

Assessing Organizational Investments In Cybersecurity And Financial Performance Before And After Data Breach Incidents Of Cloud Saas Platforms, Munther B. Ghazawneh

CCAC Theses and Dissertations

Prior research indicated that providing inappropriate investment in organizations for Information Technology (IT) security makes these organizations suffer from IT security issues that may cause data breach incidents. Data breaches in cloud Software as a Service (SaaS) platforms lead to the disclosure of sensitive information, which causes disruption of services, damage to the organizational image, or financial losses. Massive data breaches still exist in cloud SaaS platforms which result in data leaks and data theft of customers in organizations.

IT security risks and vulnerabilities cost organizations millions of dollars a year as organizations may face an increase in cybersecurity challenges. …

A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos

Mesopotamian Journal of Computer Science

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review …

Reverse-Engineering Of Disinformation Campaigns During The War In Ukraine, Lora Pitman, Ava Baratz, Kelly Morgan, Marcy Alvarado

School of Cybersecurity Faculty Publications

Information operations have long been a part of warfare. Disinformation campaigns, in particular, are usually launched by states in order to mislead and confuse populations in adversarial countries, but also to obtain support for their actions from domestic audiences. These campaigns threaten human security, at the individual level, but also state- and even international security. The invasion of Ukraine by Russia came with a new wave of disinformation not only in Ukraine itself, but also in countries from various other continents. This paper studies the characteristics of the spread of disinformation from the first day of the war in February …

Concept Of Multifactor Method And Non-Functional Requirements Solution To Increase Resilience Through Functional Safety With Cybersecurity Analysis, Emilian Piesik, Marcin Sliwinski, Nary Subramanian, Janusz Zalewski

Computer Science Faculty Publications and Presentations

In the process of designing safety systems, an integrated approach in safety and cybersecurity analysis is necessary. The paper describes a new technique of increasing resilience through integrated analysis of functional safety and cybersecurity. It is a modeling methodology based on the combination of the multifactor method utilizing modified risk graphs, used previously for Safety Integrity Level (SIL) assessment, and the Non-Functional Requirements (NFR) approach. The NFR approach, based on the analysis of graphical representation of conceptual and physical components of the system, contributes a technique to include cybersecurity through the Softgoal Interdependency Graph. The assessment methodology is outlined in …

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 …

On Confidence And Sense Of Belonging In Cybersecurity Students: Analysis & Prediction, Sadaf Amna Sarwari

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent years, there has been a rapid expansion of cybersecurity programs across higher education institutions in response to the widening skills gap in the cybersecurity job market. This study adopts quantitative and qualitative approaches to identify factors influencing West Virginia University (WVU)’s LANE Department of Computer Science and Electrical Engineering (LCSEE) students’ confidence and sense of belonging in the cybersecurity field. The results are based on data collected from surveys administered to LCSEE students in April 2022 and April 2023. The responses were analyzed using descriptive & inferential statistics and logistic regression techniques. Additionally, the 2023 data was utilized …

A Systematic Review Of K-12 Cybersecurity Education Around The World, Ahmed Ibrahim, Marnie Mckee, Leslie F. Sikos, Nicola F. Johnson

Research outputs 2022 to 2026

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013-2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in …

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

In this collaborative research endeavor at the intersection of biological safety and cybersecurity for BiosView labs, the authors highlight their engagement with a diverse student cohort. The chapter delves into the motivation behind collaborations extending beyond traditional academic research environments, emphasizing inclusivity. The meticulous examination of student demographics, including gender, self-reported ethnicity, and national origin, is detailed in the methodology. A student-centric approach is central to the exploration, focusing on aligning teaching and management styles with unique student needs. The chapter elaborates on effective teaching methodologies and management practices tailored for BiosView labs. A dedicated section emphasizes the purpose of …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and on-line only instruction methods as well as outlining …

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …