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1. Knowledge, Skills, and Abilities for Specialized Curricula in Cyber Defense: Results from Interviews with Cyber Professionals

   https://doi.org/10.1145/3421254  

   Armstrong, Miriam E.; Jones, Keith S.; Namin, Akbar Siami; Newton, David C. (November 2020, ACM Transactions on Computing Education)

   null (Ed.)

   More specialized cybersecurity education programs are needed to address workforce needs, but it is unclear which knowledge, skills, and abilities (KSAs) fulfil industry needs. We interviewed 48 professionals within four cyber defense specialty areas: (1) Cyber Network Defense Analysis, (2) Cyber Network Defense Infrastructure Support, (3) Incident Response, and (4) Vulnerability Assessment and Management. The professionals rated a number of specialized KSAs along two dimensions: how important the KSA was to their job and how difficult the KSA was to learn. Overall, communication and other non-technical skills were rated as being very important for all cyber defense jobs. Findings indicated that, for some specialty areas, technical knowledge and skills vary considerably between jobs and so the ability to teach oneself is more valuable than proficiency in any one KSA. Findings may be used to inform the development of general cybersecurity curricula, as well as curricula that focus on Cyber Network Defense Analysis, Cyber Network Defense Infrastructure Support, or Vulnerability Assessment and Management. 

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2. Developing Micro-credentials to Infuse Cybersecurity into Technician Education

   Brown, E; Hubbard, Z (June 2024, American Society for Engineering Education)

   ASEE (Ed.)

   With manufacturing facilities across the country becoming more connected, it is critical that safeguards are in place to protect against threats to facilities’ cyber-physical systems. Technicians with training in robotics/automation/mechatronics are well positioned to help provide a first line of defense to such threats. This work, funded through a National Science Foundation (NSF) Advanced Technological Education (ATE) grant, seeks to enhance technician cybersecurity awareness through education and training. The paper provides details on the process the project team utilized to develop an initial micro-credential in the area of cybersecurity for robotics/automation/mechatronics. The paper focuses on the badge creation process and outlines how the badge modules developed can be shared to help raise cyber awareness in other fields, such as semiconductors, solar power, and electric vehicles. The badge leverages the work of other NSF ATE projects, providing a no-cost avenue for automation technicians to expand their background by earning a digital badge that enhances their resume. 

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3. Generating Fake Cyber Threat Intelligence Using Transformer-Based Models

   Ranade, P.; Piplai, A.; Mittal, S.; Joshi, A.; Finin, T (July 2021, International Joint Conference on Neural Networks 2021 (IJCNN 2021))

   Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing the model to learn incorrect inputs to serve their malicious needs. In this paper, we automatically generate fake CTI text descriptions using transformers. We show that given an initial prompt sentence, a public language model like GPT-2 with fine-tuning, can generate plausible CTI text with the ability of corrupting cyber-defense systems. We utilize the generated fake CTI text to perform a data poisoning attack on a Cybersecurity Knowledge Graph (CKG) and a cybersecurity corpus. The poisoning attack introduced adverse impacts such as returning incorrect reasoning outputs, representation poisoning, and corruption of other dependent AI-based cyber defense systems. We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters. Based on the study, professional threat hunters were equally likely to consider our fake generated CTI as true. 

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4. Hidden Markov Model Enabled Prediction and Visualization of Cyber Agility in IoT era

   https://doi.org/10.1109/JIOT.2021.3056118  

   Muhati, Eric; Rawat, Danda B. (February 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cyber-threats are continually evolving and growing in numbers and extreme complexities with the increasing connectivity of the Internet of Things (IoT). Existing cyber-defense tools seem not to deter the number of successful cyber-attacks reported worldwide. If defense tools are not seldom, why does the cyber-chase trend favor bad actors? Although cyber-defense tools monitor and try to diffuse intrusion attempts, research shows the required agility speed against evolving threats is way too slow. One of the reasons is that many intrusion detection tools focus on anomaly alerts’ accuracy, assuming that pre-observed attacks and subsequent security patches are adequate. Well, that is not the case. In fact, there is a need for techniques that go beyond intrusion accuracy against specific vulnerabilities to the prediction of cyber-defense performance for improved proactivity. This paper proposes a combination of cyber-attack projection and cyber-defense agility estimation to dynamically but reliably augur intrusion detection performance. Since cyber-security is buffeted with many unknown parameters and rapidly changing trends, we apply a machine learning (ML) based hidden markov model (HMM) to predict intrusion detection agility. HMM is best known for robust prediction of temporal relationships mid noise and training brevity corroborating our high prediction accuracy on three major open-source network intrusion detection systems, namely Zeek, OSSEC, and Suricata. Specifically, we present a novel approach for combined projection, prediction, and cyber-visualization to enable precise agility analysis of cyber defense. We also evaluate the performance of the developed approach using numerical results. 

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5. Centering Hispanic-Serving Institutions' strategies to advance computer science attainment

   https://doi.org/doi/full/10.1080/25729861.2020.1842582  

   Núñez, A.-M. (January 2021, Tapuya)

   Rodriguez Medina, L. (Ed.)

   Hispanics have become the largest ethnic minority in the US Better serving Hispanics to succeed in tertiary education and scientific fields like computing is critical to build equitable life opportunities and strengthen the US workforce. Typically, the most selective postsecondary institutions are emphasized as exemplary models for developing human capital in the US. Yet, due to the nation’s tertiary education institutional stratification, relatively low numbers of Hispanics are enrolled in these institutions. We examine how Hispanic-Serving Institutions (HSIs), federally designated institutions in the US that enroll at least 25% Hispanics, develop strategies to raise Hispanic attainment in computing fields. Specifically, we explore the activities of HSIs in the Computing Alliance for Hispanic-Serving Institutions (CAHSI), a network of over 60 HSIs and other stakeholders that are committed to raising Hispanic attainment in postsecondary computing. We address the question: How do HSIs in CAHSI employ strategies to develop talent in computing among Hispanics? Specifically, we examine how CAHSI institutions apply values that are grounded in Hispanic communities, including emphases on confianza, respeto, and familia, to support Hispanic students in computer science. Our findings indicate the importance of centering Hispanic cultural assets to improve Hispanic success in computing. 

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