In various industries, cybersecurity regulations have been enacted in an effort to drive improvements to organizational security postures. Despite the prominent influence of these regulations, there has been limited prior investigation of how organizations engage with these regulations and the challenges that they face. Assessing these factors is vital for understanding the impact of cybersecurity regulations in practice and how to enhance them moving forward.

As one of the fastest growing populations in the K-12 public school system, multilingual learners (MLs), particularly those from Hispanic and/or Latinx backgrounds, represent the future workforce of the nation [1, 2, 10, 12]. Yet, they are drastically underrepresented in STEM, including computer science (CS) fields and little is known about effective ways to teach computational skills to MLs at the elementary school level [1, 3, 5, 9]. This three-year collaborative project, funded by the National Science Foundation, aims to develop linguistically inclusive integrated computer science (CS) curricula using educational robotics for elementary students in grades 3-5. More specifically, in this project, we integrate CS with mathematics, science, and English language arts to extend all elementary students’ exposure to meaningful and relevant CS experiences [4, 8, 11]. The integrated units incorporate a range of linguistically inclusive pedagogical strategies and language scaffolds to engage MLs in language-rich CS experiences, provide them with equitable learning opportunities, and support their development of computational thinking skills. The units are designed using Predict-Run-Investigate-Modify-Make (PRIMM) and TIPP pedagogical frameworks [6, 7] to scaffold students’ learning of CT concepts and promote CS learning. The project will utilize a design-based research framework gathering classroom-based data, assessment data, and interviews with teachers and students. The central research questions explore how participation in the project influences elementary teachers’ CS teaching efficacy beliefs and identity positionings as teachers of CS and MLs. The research questions related to students include how the participation in the integrated units impacts students’ CS skills, views of computer scientists, and computer scientist identity. We are in the process of providing professional development programs for teachers. At the beginning and end of the PD program, we will gather data from participant teachers. In the following academic semester, the participant teachers will be expected to implement the curricular materials in their own classrooms. Prior and subsequent to the class implementations, the data will be collected from students to examine the effect of curricular units.

Malware is a major threat to modern computer systems. Malicious behaviors are hidden by a variety of techniques: code obfuscation, message encoding and encryption, etc. Countermeasures have been developed to thwart these techniques in order to expose malicious behaviors. However, these countermeasures rely heavily on identifying specific API calls, which has significant limitations as these calls can be misleading or hidden from the analyst. In this paper, we show that malicious programs share a key component which we call a behavior dispatcher, a code structure which is intercepted between various condition checks and malicious actions. By identifying these behavior dispatchers, a malware analysis can be guided into behavior dispatchers and activate hidden malicious actions more easily. We propose BDHunter, a system that automatically identifies behavior dispatchers to assist triggering malicious behaviors. BDHunter takes advantage of the observation that a dispatcher compares an input with a set of expected values to determine which malicious behaviors to execute next. We evaluate BDHunter on recent malware samples to identify behavior dispatchers and show that these dispatchers can help trigger more malicious behaviors (otherwise hidden). Our experimental results show that BDHunter identifies 77.4% of dispatchers within the top 20 candidates discovered. Furthermore, BDHunter-guided concolic execution successfully triggers 13.0x and 2.6x more malicious behaviors, compared to unguided symbolic and concolic execution, respectively. These demonstrate that BDHunter effectively identifies behavior dispatchers, which are useful for exposing malicious behaviors.

Determining if a given program satisfies a given bound on the amount of resources that it may use is a fundamental problem with critical practical applications. Conventional automatic verifiers for safety properties cannot be applied to address this problem directly because such verifiers target properties expressed in decidable theories; however, many practical bounds are expressed in nonlinear theories, which are undecidable.