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The GenCyber Teacher Academy (GTA) stands as a pioneering professional development initiative, empowering Connecticut's high school educators in diverse STEM fields to explore and integrate cybersecurity concepts into their teaching. The inaugural 2022 edition facilitated inquiry-based learning and collaborative discourse on GenCyber Cybersecurity Concepts. However, program evaluation uncovered areas for curriculum enhancement. This paper delineates the evaluation process, curriculum revisions, and their implementation outcomes. Findings demonstrate that the revised 2023 GTA fostered improved teacher engagement with modules, enhancing their ability to integrate cybersecurity principles while prioritizing online safety. Notably, the revised GTA fortified the sustainable GenCyber Teacher Academy Teaching and Learning Community, bolstering a network of educators and practitioners destined to collectively mold Connecticut's cybersecurity landscape. 

This paper presents the GenCyber Teacher Academy (GTA), a unique professional development program that provides Connecticut's high school teachers across various STEM disciplines with opportunities to explore cybersecurity concepts and incorporate them in their curriculum. Participating teachers experienced inquiry-based learning, focused classroom discourse, and collaborative learning that centered on GenCyber Cybersecurity Concepts. Results indicate GTA enabled teachers to reflect on best practices in incorporating cybersecurity concepts while promoting online safety. Moreover, GTA established a sustainable GenCyber Teacher Academy Teaching Learning Community of high school teachers supported by a community of practitioners that will collectively shape the future of cybersecurity in Connecticut. 

The ecosystem for automated offensive security tools has grown in recent years. As more tools automate offensive security techniques via Artificial Intelligence (AI) and Machine Learning (ML), it may result in vulnerabilities due to adversarial attacks. Therefore, it is imperative that research is conducted to help understand the techniques used by these security tools. Our work explores the current state of the art in offensive security tools. First, we employ an abstract model that can be used to understand what phases of an Offensive Cyber Operation (OCO) can be automated. We then adopt a generalizable taxonomy, and apply it to automation tools (such as normal automation and the use of artificial intelligence in automation). We then curated a dataset of tools and research papers and quantitatively analyzed it. Our work resulted in a public dataset that includes analysis of (n=57) papers and OCO tools that are mapped to the the MITRE ATT&CK Framework enterprise techniques, applicable phases of our OCO model, and the details of the automation technique. The results show a need for a granular expansion on the ATT&CK Exploit Public-Facing application technique. A critical finding is that most OCO tools employed Simple Rule Based automation, hinting at a lucrative research opportunity for the use of Artificial Intelligence (AI) and Machine Learning (ML) in future OCO tooling. 

Mobile device features like Apple CarPlay and Android Auto provide drivers safer hands-free navigation methods to use while driving. In crash investigations, understanding how these applications store data may be crucial in determining the what, when, where, who and why. By analyzing digital artifacts generated by Android Auto and Apple CarPlay, investigators can determine the last application displayed on the head unit, the application layout of the user’s home display screen, and other evidence which points to the utilization of the mobile device and its features while driving. Additionally, usage data can be found within other applications compatible with Android Auto and Apple CarPlay. In this paper, we explore the digital evidence produced by these applications and propose a proof of concept open source tool to assist investigators in automatically extracting relevant artifacts from Android Auto and Apple CarPlay as well as other day-to-day essential applications. 

AI Forensics is a novel research field that aims at providing techniques, mechanisms, processes, and protocols for an AI failure investigation. In this paper, we pave the way towards further exploring a sub-domain of AI forensics, namely AI model forensics, and introduce AI model ballistics as a subfield inspired by forensic ballistics. AI model forensics studies the forensic investigation process, including where available evidence can be collected, as it applies to AI models and systems. We elaborate on the background and nature of AI model development and deployment, and highlight the fact that these models can be replaced, trojanized, gradually poisoned, or fooled by adversarial input. The relationships and the dependencies of our newly proposed subdomain draws from past literature in software, cloud, and network forensics. Additionally, we share a use-case mini-study to explore the peculiarities of AI model forensics in an appropriate context. Blockchain is discussed as a possible solution for maintaining audit trails. Finally, the challenges of AI model forensics are discussed.