Welcome to the CYSE 201S: Cybersecurity and the Social Sciences page. This section brings together all course-related materials, key concepts, and reflections that demonstrate how cybersecurity connects with human behavior, society, and social science research.
Cybersecurity is more than technology—it is shaped by people, culture, psychology, communication, and the spread of information. This page highlights what I have learned about these human-centered dimensions and how they influence digital safety.
Key Course Topics Covered
1. A Reverse Digital Divide: Information Security Behaviors in Generation Y & Generation Z
This topic explored how different age groups approach online security.
- Generation Y tends to be more cautious and experienced with digital risks.
- Generation Z is highly tech-savvy but sometimes more relaxed about cybersecurity practices.
Studying these behavioral differences helped me understand how age, experience, and digital upbringing shape security habits—and how cybersecurity solutions must adapt to diverse users.
2. Social Science Research: Principles, Methods, and Practices
This section introduced the foundations of social science research, including:
- Research methods (qualitative, quantitative, mixed)
- Ethics in studying human behavior
- How data is collected and interpreted
Understanding research principles allowed me to analyze cybersecurity issues more effectively. It showed how human factors—motivation, behavior, perception, and social structure—directly influence cyber risks and responses.
3. Cybersecurity and Its Discontents: AI, IoT, and Digital Misinformation
This topic examined modern cybersecurity challenges caused by emerging technologies, such as:
- Artificial Intelligence and its dual-use capabilities
- The Internet of Things (IoT) and the risks of hyper-connected devices
- Digital misinformation, which influences public opinion and undermines trust
This material helped me recognize how technology can both improve and complicate security, especially when misinformation spreads rapidly across social platforms.
CYSE 201S Page/ Cybersecurity and the Social Science
Article Review #1: Exploring Online Deception – Fraudsters’ Social Engineering Tactics on Job Platforms
Salahadin, Abas
September 30, 2025
1. Social Science Relevance
This article discusses internet fraud, with a particular emphasis on the strategies used by scammers on job boards. It has to do with fundamental social science ideas including social interaction, selfishness, and how technology shapes behavior. The sociology of trust, power, and vulnerability is reflected in the study’s examination of how fraud operates in a digital social setting. It presents deception as a socially formed behavior that adjusts to expectations and standards in the virtual world.
2. Research Focus (RQ, Hypotheses, IV & DV)
Research Questions
How do fraudsters’ social engineering (SE) tactics vary based on users’ resume presentations, and does fraudster sophistication influence SE strategies?
Hypotheses:
While exploratory in nature, the authors hypothesize that resume features (such as personal detail) will influence the type of SE tactics used, and that more technologically advanced fraudsters will employ more complex strategies.
Independent Variables:
Resume features (detail, format, demographic hints); fraudster sophistication level.
Dependent Variables:
Type and frequency of SE tactics used; effectiveness or engagement outcome.
3. Research Methods
The study used a mixed-methods approach. It first conducted qualitative coding of fraudster messages to identify patterns in SE tactics. Then, it used quantitative analysis to test relationships between resume attributes and the types of tactics used. The design was cross-sectional, analyzing a single data collection period.
4. Data & Analysis
Types of Data:
Collected digital communication data from fraudulent accounts on employment databases, and metadata related to user resumes.
Analysis Techniques:
The authors conducted qualitative thematic coding of the text data, followed by statistical correlation and frequency analysis to assess patterns in the use of SE strategies across varying conditions.
5. Connection to Course Concepts
This article strongly connects to concepts from our course lectures and PowerPoint presentations, especially the social construction of deviance, routine activity theory, and cyber victimization. Fraudsters exploit digital trust and social norms to craft believable personas, aligning with how deviant behavior is socially contextualized. Routine activity theory is applicable, as fraudsters target users in low-guardianship environments—online job platforms lacking protective filters.
6. Marginalized Groups: Challenges, Concerns, Contributions
While the article does not center explicitly on marginalized populations, it implies that vulnerable users—such as economically disadvantaged individuals, immigrants, or the digitally less literate—may be more susceptible to fraud. These groups often face higher pressure to find employment quickly and may lack the resources or digital fluency to detect deception, exacerbating inequality in online spaces.
7. Contributions to Society
The study offers critical insights into how online fraud evolves and adapts. By identifying patterns in social engineering tactics, it enables platforms, cybersecurity experts, and policymakers to develop targeted prevention strategies. The findings support the need for user education, platform design enhancements, and fraud detection systems grounded in behavioral patterns. This contribution helps bridge the gap between academic theory and practical application.
Conclusion
This article is a valuable contribution to both the field of cyber criminology and broader social science. It blends qualitative and quantitative methods to offer a deep understanding of online fraud tactics. The study’s insights inform real-world interventions and policy. While limited by its narrow data source and lack of cultural diversity, it provides a strong foundation for further research on digital deception and social vulnerability.
Reference
Cole, T. (2023). Exploring fraudsters’ strategies to defraud users on online employment databases. International Journal of Cyber Criminology. https://cybercrimejournal.com/menuscript/index.php/cybercrimejournal/article/view/90
Article Review #2: Understanding University Student Vulnerability to Phishing Attacks
Student Name: Salahadin Abas
School of Cybersecurity, Old Dominion University
CYSE 201S: Cybersecurity and the Social Sciences
Instructor Name: Diwakar Yalpi
Date: 11/13/2025
INTRODUCTION/BLUF
This study by Broadhurst, Skinner, Sifniotis, Matamoros-Macias, and Ipsen in 2019 investigates the vulnerability of college students to phishing scams. To gauge real world vulnerability, the researchers used a quasi experimental approach to send 138 students different kinds of phishing emails, including spear-phishing, customized, and generic. The study discovered that while cybercrime awareness, gender, IT proficiency, and perceived online safety did not significantly affect susceptibility, individualized scams were more successful than generic ones. Significantly, international and first-year students were more susceptible to phishing scams, underscoring the necessity of focused cyber-education initiatives.
Relation/Connection to Social Science Principles
Important social science concepts, especially those pertaining to human behavior, social institutions, and decision-making, are closely related to the subject of phishing vulnerability. This study demonstrates how social elements including experience level, cultural background, and integration into institutional settings influence people’s behavior in digital environments in addition to technology. First-year and international students are more vulnerable, which is consistent with larger social science concerns of socialization, adaptation, and unequal access to networks of support or knowledge. The paper emphasizes how social context shapes risk perception and online behavior by looking at how various groups interpret and react to phishing attempts. Overall, the
study supports the social science theory that socialization, identity, group membership, and structural injustices all have an impact on behavior, including in cyberspace.
Research Question / Hypothesis / Independent Variable / Dependent Variable
.
The study looks into the characteristics that affect university students’ vulnerability to phishing attacks. The authors make a number of predictions, like students who receive cybercrime awareness training (the “Hunter” group) would be less likely than untrained students to fall for phishing attempts; that spear-phishing or tailored phishing emails would be more successful than generic ones; and that vulnerability would be predicted by factors like gender, IT proficiency, and perceived online safety. The type of phishing email received, the awareness condition (primed vs. unprimed), and a number of student characteristics, including year level, domestic or international status, gender, IT skill level, and perceived Internet safety, are among the study’s primary independent variables.The main dependent variable is students’ sensitivity to phishing, which is determined by whether or not they engaged with the scam content or clicked links in the phishing emails.
Types of Data Analysis Used
The authors tested students’ vulnerability to phishing using statistical techniques that were both descriptive and inferential. Initially, they summarized fundamental patterns of student reactions using descriptive statistics, such as the quantity of clicks or interactions with each form of phishing email. The characteristics that strongly predicted phishing susceptibility were then tested using a Generalized Linear Model (GLM). These factors included awareness condition, phishing email type, gender, IT competence, perceived Internet safety, year level, and international versus domestic status. The researchers were able to identify the factors that significantly affected students’ propensity to respond to the phishing emails thanks to this inferential study. Identifying group differences and assessing the strength of several predictors of phishing sensitivity were the main goals of the investigation.
Overall Contributions of the Study to Society
This study contributes important insights to society by showing that phishing susceptibility is not solely a technical issue but a social one shaped by experience, identity, and access to information. By identifying which groups are most vulnerable like the international and first-year students, the research provides evidence that can guide universities and policymakers in developing targeted cybersecurity training and support programs. The findings also encourage institutions to adopt proactive measures that strengthen digital awareness and reduce victimization across diverse populations. Really the study boosts our understanding of how social factors influence cybersecurity risks and underscores the societal importance of improving digital safety through inclusive education and awareness initiatives.
Reference
Broadhurst, R., Skinner, K., Sifniotis, N., Matamoros‑Macias, B., & Ipsen, Y. (2019). Phishing and Cybercrime Risks in a University Student Community. International Journal of Cybersecurity Intelligence & Cybercrime, 2(1), 4–23 https://vc.bridgew.edu/ijcic/vol2/iss1/2/
Professional career paper
Your Name: Salahadin Abas
Date 11/13/2025
Security Operations Center (SOC) Analyst
BLUF: By keeping an eye on systems, spotting threats, and handling incidents, SOC analysts safeguard businesses. Social science research is a major component of their work, particularly when it comes to comprehending human behavior, the reasons behind cyberattacks, and how users engage with technology.
Introduction
An organization’s networks and systems must be continuously monitored by a Security Operations Center (SOC) analyst in order to identify, evaluate, and address cybersecurity threats. SOC analysts are essential to data protection, business continuity, and upholding public confidence in today’s digital world. This essay describes how SOC analysts apply social science concepts, how the SOC environment relates to class ideas, and how the profession engages with society and marginalized groups.
Social Science Principles in the SOC Career
SOC analysts rely on research in social science to understand user decision-making, insider threat motivations, and cybercriminal conduct. Human-computer interaction HCI research helps analysts anticipate typical user mistakes, like falling for phishing efforts due to cognitive overload or social engineering approaches (Workman, 2008). If analysts are well-versed in psychological motivators such financial hardship, retaliation, or ideology, they can assess insider-threat risk levels more successfully (Furnell & Clarke, 2012). Social science concepts also serve as a guide for communication tactics. In order to successfully communicate dangers to non-technical staff, SOC analysts must have a thorough understanding of how people perceive risk and respond to alerts. These insights support more successful cybersecurity awareness initiatives and lessen dangerous organizational practices.
Application of Key Class Concepts
SOC analysts’ daily responsibilities are directly related to class concepts like risk assessment, social engineering, user behavior, and trust. By analyzing human behavior patterns, spotting weaknesses, and making sure policies adhere to moral and legal requirements, analysts routinely assess organizational risk. Understanding social factors is essential for methods like behavior-based analytics, incident response documentation, and phishing analysis. For instance, when analysts notice a suspicious login, they use a behavioral science-based approach to determine whether the behavior matches the user’s usual patterns. Additionally, analysts employ mitigation techniques that take into account how users actually use systems rather than just how they ought to.
Marginalization and Cybersecurity
Cybersecurity incidents disproportionately impact marginalized groups, including individuals with limited digital literacy or communities with reduced access to secure technologies. SOC Analysts contribute to reducing these disparities by implementing secure authentication methods, improving accessibility, and monitoring for cyber threats that target vulnerable populations (Henry, 2020).Additionally, SOC teams support efforts that promote equitable digital protection—such as creating simpler security tools and ensuring security alerts are understandable for all users, regardless of background or education level.
Career Connection to Society
The stability of society is directly impacted by the work of SOC analysts. Analysts contribute to preserving public safety and trust by stopping breaches in industries like government, healthcare, and finance. supporting law enforcement and reacting to significant cyber incidents, their work also contributes to national security. The SOC role is further shaped by public policy, including data protection laws and breach reporting requirements. To maintain compliance and safeguard organizations and the communities they serve, analysts need to stay up to date on legal requirements.
Conclusion
Protecting businesses and society from cyber threats is an important function of a Security Operations Center Analyst. Social science ideas, such as behavioral research, human-computer interaction, and communication techniques, play a major role in their work. In this line of work, class ideas like risk assessment, social engineering, and user behavior analysis are used on a daily basis. In addition to identifying and addressing threats, SOC analysts contribute to the development of safer, more welcoming online environments for all users.
References (APA Format)
Furnell, S., & Clarke, N. (2012). Power to the people? The evolving recognition of human aspects of security. Computers & Security, 31(8), 983–988.
Henry, C. (2020). Digital inequality and cybersecurity risks in marginalized communities. Journal of Cyber Policy, 5(2), 123–139.
Workman, M. (2008). Wisecrackers: A theory-grounded investigation of phishing and pretext social engineering threats. Journal of the American Society for Information Science and Technology, 59(4), 662–674 .