The rise in cyberattacks on Industrial Control Systems (ICS) shows the need for enhanced security measures. Integrating diverse cybersecurity datasets is essential to provide a comprehensive view of the threat landscape. This paper presents an approach to automatically connect Common Weakness Enumeration (CWEs) and ICS-specific MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) techniques. We present studies on how embedding-based approaches such as SBERT and BERT and frequency-based approaches such as TF-IDF perform in detecting connections between ICS ATT&CK Techniques and CWEs. Furthermore, we employ advanced analytical methods to identify common attack patterns. We present three case studies to demonstrate the potential of embedding-based approaches for mapping multiple datasets. Our approach holds promise for detecting ICS weaknesses, showing how integrating expert knowledge and domain-specific data can be used for advanced threat analytics.

There is a national need to increase the number of minority students entering STEM fields with essential computing skills. To increase minority students’ interest and engagement in computing, a researcher-practitioner partnership between the University of Texas at El Paso and the El Paso Independent School District, developed and implemented a culturally and linguistically responsive curriculum and pedagogy to introduce computational thinking (CT) in two middle schools across different subject areas in a borderland region. The curriculum leveraged the Sol y Agua game – a bilingual, culturally-responsive game designed to engage students of this region in CT. This paper describes the process and initial findings of this project. The quantitative data from in-game analyses show that students utilized the language change feature to switch from English to Spanish more frequently than the other way – highlighting the need for educational platforms relatable to students through language, environment, and cultural context. Analyses of the qualitative data indicate that while teachers/team members understood CT and translanguaging concepts and taught lesson units that provided opportunities to practice both, CT and translanguaging were largely implicit in the curriculum. In collaborative analyses of these patterns, teachers described additional supports that would help them to make CT instruction and translanguaging strategies more explicit in the content and pedagogy, highlighting the need for systematic, targeted integration of these concepts.

The prevalence of malware on the Internet makes malware detection vital as an early warning system for organizations’ security. This paper presents a novel approach to linking knowledge from heterogeneous and specialized datasets using a sentence embedding approach. This paper also proposes a novel approach to detect malware activity using standardized and specialized datasets and people’s search interest data. We demonstrated the detection capabilities of our approach, assessing our models using four real attack study cases. We found an increase in Google searches and probabilities of our models seven days before and after an attack occurred. In addition, the web search volume and model probabilities time series are characterized by an increase in outliers around 14 days before and after the discovery of the attack. This work should pave the path for integrating domain-specific datasets and user-generated dynamic data for detecting malware activity.

Timely detection of an outbreak of any infectious disease is essential to implement timely mitigation strategies. Several approaches have been proposed to model and detect the occurrence of an outbreak. However, many of these approaches rely on health surveillance methods and informal and formal reports of illnesses. This paper proposes a single framework for detecting multiple infectious disease outbreaks by integrating disease-specific domain knowledge and public search trend data. We tested our framework with eleven infectious diseases and compared the results with the Centers for Disease Control and Prevention (CDC) outbreak data. Results show that our framework reaches accuracies greater than 95% in most cases. To our knowledge, this is the first study that uses standardized disease symptoms as main indicators, in combination with web search data, to detect infectious disease outbreaks. This work should pave the path for integrating domain-specific static information with user-generated dynamic data for detecting outbreaks of infectious diseases.