Artificial intelligence technologies with generative capabilities have accelerated fundamental transformations in security architectures, necessitating reconceptualization of security frameworks and threat assessment protocols. This study presents a pedagogical framework for integrating generative AI (GenAI) into university-level cybersecurity curricula. The methodology establishes foundational knowledge in generative models and language processing architectures, followed by applications across defensive security measures. The framework includes automated cyber threat intelligence, malicious code and malware detection, log anomaly detection, digital image forensics, and AI-assisted penetration testing. The framework acknowledges the dual-use nature of GenAI in security domains, incorporating prompt injection attacks that manipulate model behaviors and compromise system integrity. Laboratory modules presented will provide students with hands-on experience on advanced tools including large language models, diffusion models, and cognitive architectures for automated security assessment. This study seeks to prepare cybersecurity professionals with critical competencies necessary for effective operation within an environment increasingly shaped by artificial intelligence systems.

The Dark web is considered the concealed part of the internet and harbors a huge assortment of cyber threats that compromise global security. One must understand what goes into the technical infrastructure of the Dark Web to develop an effective strategy for monitoring threats, conducting investigations, and implementing the appropriate security measures necessary to protect against illegal activities and data breaches originating from the Dark Web. In such a rapidly changing cyber threat landscape, especially threats originating from the Dark Web, it calls for a re-evaluation of the traditional information security curricula at academic institutions. This educational research work investigates the compelling need to integrate Dark Web Education into Cybersecurity programs for arming the future workforce with a comprehensive knowledge base and skillset necessary to fight modern-day cyber threats. A review of the current state of cybersecurity education reveals wide gaps in knowledge and readiness about Dark Web issues. This paper presents a structured approach for integrating Dark Web topics into the existing curricula on cybersecurity, focusing on legal, ethical, and technical dimensions. We believe in balance: on one side, theoretical knowledge; on the other, hands-on experiences that ensure the learner takes away just how complex the Dark Web is-without taking part in or condoning any illegal activities. Equally, a set of recommendations are commented on for educators and developers of curricula to integrate education about Dark Web safely and effectively into their cybersecurity programs, which will enhance the overall quality and relevance of cybersecurity education in preparing the students for the challenges of the digital age.

Censorship systems face significant challenges in detecting anonymity-preserving traffic due to advanced obfuscation techniques employed by Tor pluggable transports like Obfs4 and Snowflake. Conventional detection approaches exhibit diminished effectiveness in operational environments where obfuscated traffic constitutes a minute fraction of overall network communications. We present a Cost-Sensitive Bidirectional Generative Adversarial Network (CS-BiGAN) that addresses these challenges through enhanced feature representation learning and classification resilience under extreme class imbalance. Our methodology incorporates a custom dataset collection framework capturing representative traffic patterns from multiple obfuscation protocols, coupled with a cost-sensitive learning mechanism to mitigate class disparity effects. Comprehensive evaluation demonstrates that CS-BiGAN achieves 98.25% accuracy under balanced conditions, with protocol-specific F1-scores of 99.29% for Obfs4 and 97.16% for Snowflake. The model’s distinguishing characteristic is the sustained performance under severe base rate imbalances (1000:1:1:1) that reflect real-world network conditions, maintaining F1-scores exceeding 90.80% for minority classes on average. This performance substantially surpasses existing approaches, establishing practical applicability in operational environments. Our findings offer insights relevant to both censorship system deployment and the advancement of robust obfuscation methodologies designed to circumvent detection mechanisms.

The ongoing expansion of the digital landscape has led to a growing convergence between the fields of artificial intelligence (AI) and network security. This has necessitated the need for universities to incorporate AI into their network security curriculum. Although traditional network security courses are considered crucial, they lack the agility to address constantly evolving threats. AI offers a transformative solution to such difficulties with its predictive analytics, real-time intrusion detection, and adaptive learning capabilities. This study highlights the importance of incorporating AI into network security curricula at the undergraduate level. A modification to the curriculum is proposed, wherein AI themes are integrated into network security courses and labs. The proposed curricula include understanding theoretical AI concepts and designing AI -augmented hands-on laboratories. The pedagogy emphasizes the tools, and frame-works that facilitate the construction of AI models for intrusion detection, mal ware analysis, and network analytics. This plays a significant importance in providing a simulated environment for students to engage with AI tools and methods to address authentic cyber threats.

Fog computing is a technology that brings computing, storage, and networking services closer to devices and systems, aiming to improve speed, efficiency, and data processing capabilities for various applications. The growing importance of fog and edge computing technologies means we need new and better ways to teach students about these areas. This paper offers a detailed guide on how to use xFogSim, an extended version of the Omnet++ network simulator, for teaching fog and edge computing. We give students a clear path to follow, starting with simple network designs and moving to more complex ones, helping them understand how federated learning works. We tested xFogSim with a group of students and found that it really helps them grasp fog and edge computing ideas better than traditional teaching methods. xFogSim also gives practical information about important performance metrics, helping bridge the gap between what students learn in class and what they need to know in the real world. This paper shows that using xFogSim in classrooms gives students a strong base in distributed computing systems, getting them ready for future tech challenges.

The realm of academic security education is primarily focused on defensive strategies. However, there's a growing acceptance of offensive techniques, initially crafted by hackers. Several educators in the field of information security believe that incorporating offensive strategies into the curriculum creates more adept security professionals than focusing solely on defensive methods. Students in information security courses must engage in offensive and defensive tactics to effectively handle malicious activities and devise suitable security measures. This paper presents a case study on executing an in-depth, practical cybersecurity laboratory exercise centered on a prevalent network attack, the DNS spoofing attack, which is vital for network security training. The primary educational goal of this hands-on lab exercise is to equip students with the skills to conduct a DNS spoofing attack within a controlled, virtual network environment using Kali Linux. The introduction of this offensive cybersecurity lab exercise resulted in enhanced student performance; however, it also raised significant ethical issues. Consequently, the paper outlines several measures that academic institutions should consider to mitigate the risks associated with teaching offensive strategies in information security education programs.