Information Security Commons^™

Cyber Science Education Meets Healthcare Technology, Angela Spencer

Journal of Cybersecurity Education, Research and Practice

The research investigates how cyber science education combines with healthcare technology during the digital age to resolve a fundamental research gap in these two advancing areas. A combined approach utilizing extensive surveys and detailed interviews evaluates the functionality of learning platforms as well as cybersecurity measures and potential uses of emerging virtual reality (VR) and augmented reality (AR) tools to improve both educational and clinical environments. The research document describes its methodologies thoroughly while. The research documents multiple quantitative and qualitative results before performing its analysis, which leads to strategy development for digit. The researchers worked to find ways that …

Efficient Privacy-Preserving Conjunctive Searchable Encryption For Cloud-Iot Healthcare Systems, Jiadi Ma, Tianqi Peng, Gong Bei, Muhammad Waqas, Hisham Alasmary, Sheng Chen

Research outputs 2022 to 2026

In cloud-Internet of Things (IoT) healthcare systems, private medical data leakage is a serious concern as the cloud server is not fully trusted. Dynamic searchable symmetric encryption (DSSE), with necessary forward and backward privacy security properties, enables doctors to retrieve ciphertexts while guaranteeing data privacy. However, existing forward and backward private DSSE schemes are not well-suited for cloud-IoT healthcare systems with attribute-value type databases. To this end, we propose an efficient privacy-preserving conjunctive searchable encryption scheme for cloud-IoT healthcare systems, called PC-SE. It is the first conjunctive DSSE scheme designed for attribute-value type databases. Specifically, we design flexible search capabilities …

The Gains Do Not Make Up For The Losses: A Comprehensive Evaluation For Safety Alignment Of Large Language Models Via Machine Unlearning, Weixiang Zhao, Yulin Hu, Xingyu Sui, Zhuojun Li, Yang Deng, Yanyan Zhao, Bing Qin, Wanxiang Che

Research Collection School Of Computing and Information Systems

Machine Unlearning (MU) has emerged as a promising technique for aligning large language models (LLMs) with safety requirements to steer them forgetting specific harmful contents. Despite the significant progress in previous studies, we argue that the current evaluation criteria, which solely focus on safety evaluation, are actually impractical and biased, leading to concerns about the true effectiveness of MU techniques. To address this, we propose to comprehensively evaluate LLMs after MU from three aspects: safety, over-safety, and general utility. Specifically, a novel benchmark MuBench with 18 related datasets is first constructed, where the safety is measured with both vanilla harmful …

Mitigating Malware Prevalence In Networks With Arbitrary Topologies: A Flip-It Cyber Game Approach Integrated With Epidemic Modeling, Mousa Tayseer Jafar, Lu Xing Yang, Gang Li, Robin Doss, Kon Mouzakis, Rajesh Vasa, Helge Janicke, Ahmed Ibrahim, Ahmed Mohsin, Iqbal H. Sarker, Kristen Moore, Seyit Camtepe, Diksha Goel

Research outputs 2022 to 2026

Cyber threats have evolved in complexity, aiming at a wide range of sectors using advanced methods and tools. This evolving threat landscape challenges existing cybersecurity frameworks, many of which lack the adaptability to counteract the complex tactics of sophisticated adversaries. Developing robust cyber defense strategies requires simulating dynamic interactions between attackers and defenders across high, moderate, and low-impact scenarios. The Flip-It cyber game serves as an intelligent framework for simulating these interactions, enabling the analysis of adaptive strategies in cybersecurity. This paper aims to address the problem of mitigating malware prevalence with full consideration of attack/defense capabilities in arbitrary network …

Integrating Adversarial Scenarios Into Llm Security Labs: An Experience Report On A Hands-On Approach, Dominic A. Wilson

Journal of Cybersecurity Education, Research and Practice

This paper presents an exploratory case study detailed as a pedagogical experience report on integrating adversarial Large Language Model (LLM) scenarios into a graduate cybersecurity curriculum. In addition to prompt injection, sophisticated techniques such as jailbreaking and model inversion pose emerging threats that traditional computer security curricula often lack. We present the design and implementation of a structured, hands-on module addressing this gap, utilizing a custom Retrieval-Augmented Generation (RAG) platform with local open-source LLMs. A cohort of 16 graduate students participated in this two-week pilot module, engaging in "red team" activities to actively exploit model alignment and privacy vulnerabilities. The …

A Review Of Routing Attacks In Routing Protocol Over Low-Power And Lossy-Based Iot Networks, Lanka Chris Sejaphala Mr., Vusimuzi Malele Prof, Francis Lugayizi Prof.

Journal of Cybersecurity Education, Research and Practice

Low-power and Lossy IoT Networks (LLNs) comprise physical sensors, processing capability, power, and other technologies to exchange information between systems and devices over the internet. However, these networks are susceptible to routing attacks affecting resources, traffic flow, and topology formation. In the related work, it has been discovered that many previous studies do not consider algorithms and implementation approaches for routing attacks. This research study provides a comprehensive in-depth synthesis insight into the description, effects, and algorithms & implementation of four routing attacks in LLNs i.e., rank, sinkhole, DIS-flooding, and worst parent attacks. The findings of this research study highlight …

Bridging The Gap: A Systematic Review Of Cyber Conflict Forecasting Models And The Case For Ai-Driven Dynamic Frameworks, Salim Arfaoui, Youssef Harrath, Omar El-Gayar

Research & Publications

Cyber conflict forecasting remains constrained by static models that overlook the integration of geopolitical context with technical indicators. This systematic literature review examines 58 studies (2010–2025) using PRISMA guidelines and an InputProcess-Output framework to classify approaches and identify key gaps. Quantitative methods dominate (67%), yet only 14% incorporate geopolitical variables, despite the political nature of cyber conflict. Major limitations include adversarial adaptation blindness (85% assume static behavior), coarse temporal granularity (72% use daily+ intervals), lack of uncertainty quantification (75%), and minimal modeling of cross-domain escalation (92% cyber-only focus). Strategic forecasting is rare, with just 14% providing long-term insights and 16% …

The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches To Teach Foundational Cybersecurity Failures, Chinedum Amaechi, Doris Asogwa, Samuel Alade

Journal of Cybersecurity Education, Research and Practice

SourceURL:file:///home/amaechi/Documents/ *Journal of Cybersecurity Education, Research and Practice (JCERP)*. **Title:** The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches to Teach Foundational Cybersecurity Failures.docx

Background: Between March and April 2026, a single threat actor allegedly compromised four Nigerian institutions across banking, payment infrastructure, government registry, and power distribution sectors. The breaches exposed millions of records and disrupted critical services, yet all four exploited elementary vulnerabilities taught in introductory cybersecurity courses. Objective: This pedagogical case study analyzes the four breaches as a unified phenomenon of "normalized negligence" and provides ready-to-use teaching materials for cybersecurity educators. Methods: Using open-source intelligence analysis of …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Systematic Approaches To Characterizing Vulnerabilities And Enhancing Robustness Of Text And Vision-Language Models, Poojitha Thota

Computer Science and Engineering Dissertations

The proliferation of artificial intelligence (AI) across critical domains, including news summarization, privacy-policy analysis, and medical decision support, has raised growing concerns about the security and robustness of these systems against adversarial manipulation. This dissertation investigates adversarial robustness in generative AI by addressing three key research goals: (1) characterizing adversarial vulnerabilities across generative models, (2) developing systematic defenses to improve the robustness of generative models, and (3) designing deployment-time safeguards for securing LLM interactions.

Towards the first goal, we characterize adversarial vulnerabilities across text-based and multimodal systems. In abstractive text summarization, we show that inference-time perturbations can exploit lead bias …

Operationalizing Cyber-Routine Activities Theory For Senior Cybercrime Prevention: An Evaluation Of The Shield Training-The-Trainer Program, Kyung-Shick Choi, Insun Park, Mijin Kim, Ji Yae Bong

International Journal of Cybersecurity Intelligence & Cybercrime

Older adults face growing risks of cyber-enabled fraud, yet scalable, evidence-based prevention programs remain limited. Guided by Cyber-Routine Activities Theory (Cyber-RAT), this study evaluates the pilot implementation of the Seniors Harnessing Internet Education for Lasting Defense (SHIELD) Training-the-Trainer program, designed to prepare law enforcement officers and community leaders to deliver cybercrime prevention education to older adults. A key innovation of SHIELD is its integration of interactive game-based simulations, which allow participants to practice verification, refusal, and reporting behaviors in realistic cybercrime scenarios. Following the December 2025 pilot session in Boston, semi-structured interviews were conducted with 12 participants from law enforcement, …

From Authorization To Loss: A Blockchain Forensic Analysis Of Transaction-Level Mechanisms In Cryptocurrency Airdrop Scams, Chanwoo Shin, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

Cryptocurrency airdrop scams have emerged as a rapidly growing form of cyber-enabled financial crime, yet remain underexplored in empirical research. This study examines how transaction-level mechanisms and offender strategies influence variation in monetary loss in airdrop scam incidents. Grounded in Cyber-Routine Activities Theory (Cyber-RAT), the study conceptualizes financial harm as occurring within decentralized environments where users’ online behaviors, particularly transaction authorization, intersect with limited digital capable guardianship. Data were drawn from 112 validated airdrop scam cases reported on Chainabuse.com between January and December 2025. Blockchain forensic analysis using Breadcrumbs was conducted to reconstruct transaction pathways, identify exchange interactions, and detect …

Ordered Mini-Batch Training For Differentially Private And Encrypted Logistic Regression, Ryan Leone

Theses, Dissertations and Culminating Projects

Logistic regression has found extensive use as a supervised machine learning algorithm due to its simplicity and efficiency in binary and multivariate classification tasks. As data sharing grows across connected devices, safeguarding sensitive personal and industrial information is of increased importance. Privacy-preserving machine learning techniques such as differential privacy and homomorphic encryption offer mathematically rigorous security guarantees, but introduce difficult accuracy, privacy loss, and computational overhead issues. This thesis investigates PPML for logistic regression through a collaborative mini-batch training framework. I propose and implement an ordered mini-batch strategy, compare it to standard shuffled methods, then integrate differential privacy noise injection …

Benchmarking Gaslighting Negation Attacks Against Reasoning Models, Bin Zhu, Hailong Yin, Jingjing Chen, Yu Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand gaslighting negation attacks—adversarial prompts that confidently deny correct answers—remains underexplored. In this paper, we conduct a systematic evaluation of three state-of-the-art reasoning models, i.e., OpenAI’s o4-mini, Claude-3.7-Sonnet and Gemini-2.5-Flash, across three multimodal benchmarks: MMMU, MathVista, and CharXiv. Our evaluation reveals significant accuracy drops (25–29% on average) following gaslighting negation attacks, indicating that even top-tier reasoning models struggle to preserve correct answers under manipulative user feedback. Built upon the insights of the evaluation and to further probe this vulnerability, …

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this work, we present the first comprehensive empirical evaluation of LLMs for AVL. Our study examines 19 leading LLMs suitable for code analysis, including ChatGPT and multiple open-source models, spanning encoder-only, encoder-decoder, and decoder-only architectures, with model sizes from 60M to 70B parameters. We evaluate three paradigms including few-shot prompting, discriminative fine-tuning, and generative fine-tuning with and …

Tackling The Societal And Regulatory Challenges Of Emerging Technologies: A Case Study Of Deepfake, Jingyao Li

2026

Governing emerging technologies such as Artificial Intelligence (AI) poses enduring challenges for policymakers, industries, and societies. Early-stage governance is often hindered by limited understanding of technological implications, rapid innovation cycles, and resistance from powerful industry actors who favor minimal oversight. Yet, timely and effective governance is essential, as new technologies are most malleable in their formative stages. This dissertation examines how emerging technologies can be governed effectively by using deepfakes technology as a focal case. This dissertation comprises three interrelated studies.

The first paper reviews the literature on deepfakes and emerging technology governance, identifying the distinct characteristics of deepfake technology …

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

Understanding Phishing Susceptibility Through Expert Consensus Using Digital Marketing Parallels And A Machine Learning-Based Implementation, Mansoor Ahmad

All Graduate Theses, Dissertations, and Other Capstone Projects

Phishing remains one of the most effective attack vectors for gaining unauthorized access to organizational systems, yet defenders often lack systematic methods to assess their exposure before an attack. This study develops a framework that uses machine learning to encode the collective expertise of cybersecurity practitioners into a portable phishing susceptibility assessment tool, with the goal to help security teams proactively identify patterns, prioritize awareness training, and strengthen detection controls. The study surveyed 27 practitioners with extensive experience in social engineering, red teaming, penetration testing, and threat analysis to identify which factors most influence phishing susceptibility. Practitioners provided quantitative ratings …

Rockyou2024: What’S Your Password?, Yixuan Zhang

Honors Theses

Passwords remain a critical part of almost every account security system. As a result, password guessing attacks remain one of the most widespread yet profitable attacks possible. Setting a password resistant to attacks is thus an important task for account holders. In this paper, we use the RockYou2024 database, a collection of approximately 10 billion real-world passwords collected from data breaches, to analyze the characteristics of passwords found in real life. We start with basic statistical property analysis, such as length, distribution of digits and symbols, and proceed onto more complicated properties such as frequencies of combinations of characters, entropy …