This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.

Recent years have seen a disconnect between much-needed real-world skills and knowledge imparted to cybersecurity graduates by higher education institutions. As employers are shifting their focus to skills and competencies when hiring fresh graduates, higher education institutions are facing a call to action to design curricula that impart relevant knowledge, skills, and competencies to their graduates, and to devise effective means to assess them. Some institutions have successfully engaged with industry partners in creating apprenticeship programs and work-based learning for their students. However, not all educational institutions have similar capabilities and resources. A trend in engineering, computer science, and information technology programs across the United States is to design project-based or scenario-based curricula that impart relevant knowledge, skills, and competencies. At our institution, we have taken an innovative approach in designing our cybersecurity courses using scenario-based learning and assessing knowledge, skills, and competencies using scenario-guiding questions. We have used the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework and the Office of Personnel Management (OPM) Hiring Cybersecurity Workforce report for skills, knowledge, and competency mapping. This paper highlights our approach, presenting its overall design and two example mappings.

Critical infrastructures have succumbed to the demands of greater connectivity. Although the scheme of connecting these critical equipment and devices to cyberspace has brought us tremendous convenience, it also enabled certain unimaginable risks and vulnerabilities. The importance of critical infrastructure (CI) protection has never been pronounced and we are in a juncture in history where CI security is paramount. Although research in this area of national need has grown steadily, pedagogical materials in this area is slow to keep up. This paper presents the development of course modules for critical infrastructure security curriculum. Although these course modules can be used to augment an existing course in CI, they can also be utilized as bases with which to build a complete CI course. Existing laboratory setups which can be used to supplement the course are also described. The course modules and the supplemental laboratories are envisioned to be great instruments for training future information security professionals. These pedagogical materials can also be used as supplements to other courses that pertain to information security, risk management, or emergency preparedness.