Gateway courses play a key role in student success.These courses often control access to advanced coursework and can delay graduation if students struggle to pass them.Identifying such courses is important for curriculum design and student support.However, existing methods rely on heuristics or past grades and often overlook complex patterns in student enrollment data.In this paper, we propose a tensor decomposition-based method to identify gateway courses in a systematic way.We model studentcourse-semester interactions as a three-dimensional tensor.This captures relationships between students, courses, and time.Using PARAFAC tensor decomposition, we extract latent factors that represent course importance based on their structure in the data.Our analysis shows that this method highlights courses that are central, or distinctive in the curriculum.Courses with high latent factor magnitudes are likely to represent gateway courses or critical prerequisites.As future work, we plan to combine tensor decomposition with learning techniques.This could enable predictive models and provide insights for curriculum design.Our goal is to develop a complete framework to analyze educational data, and support institutions in improving student outcomes.

Research opportunities offer students at the master's level a chance to apply their knowledge, create projects for their portfolio, and to gain an understanding of the research process in preparation for potential doctoral studies. The traditional structure of these opportunities, however, is not trivial to translate to online programs with asynchronous delivery. As online programs grow, it is important to examine ways that the traditional benefits of these opportunities can be extended to a broader range of students. In this poster, we discuss our experience building the infrastructure and running early efforts at reaching this goal. We discuss several dedicated courses and seminars developed to offer larger scale opportunities for students to pursue research. We also discuss efforts in active development to provide better infrastructure support to reduce the friction and complexity of pursuing research at scale.

The research focuses in academia are typically determined from the top down, with professors focusing on projects that align with their existing lab or can be readily supported by grants. This approach is pragmatic, but this focus may not align with the bottom-up interests of the available student body. As part of a broader effort to expand the available research opportunities in a graduate program in computer science, this work focuses on collecting data on what is motivating student interest in research and what specific fields students desire to study with the intention of using the results for decision-making about how to allocate resources to best match student interests. This work reports on the responses of 143 graduate students in computer science at a major research institution in the United States in a course-based program where access to research opportunities is not guaranteed. It examines both the fields of particular interest and the self-reported motivations leading these students to attempt to seek out research opportunities.

Bulletproof hosting Autonomous Systems (ASes)-malicious ASes fully dedicated to supporting cybercrime-provide freedom and resources for a cyber-criminal to operate.Their services include hosting a wide range of illegal content, botnet C&C servers, and other malicious resources.Thousands of new ASes are registered every year, many of which are often used exclusively to facilitate cybercrime.A natural approach to squelching bulletproof hosting ASes is to develop a reputation system that can identify them for takedown by law enforcement and as input to other attack detection systems (e.g., spam filters, botnet detection systems).Unfortunately, current AS reputation systems rely primarily on data-plane monitoring of malicious activity from IP addresses (and thus can only detect malicious ASes after attacks are underway), and are not able to distinguish between malicious and legitimate but abused ASes.As a complement to these systems, in this paper, we explore a fundamentally different approach to establishing AS reputation.We present ASwatch, a system that identifies malicious ASes using exclusively the control-plane (i.e., routing) behavior of ASes.ASwatch's design is based on the intuition that, in an attempt to evade possible detection and remediation efforts, malicious ASes exhibit "agile" control plane behavior (e.g., short-lived routes, aggressive re-wiring).We evaluate our system on known malicious ASes; our results show that ASwatch detects up to 93% of malicious ASes with a 5% false positive rate, which is reasonable to effectively complement existing defense systems. CCS Concepts• Security and privacy →

This paper studies the routing dynamics of malicious networks. We characterize the routing behavior of malicious networks on both short and long timescales. We find that malicious networks more consistently advertise prefixes with short durations and long inter- arrival times; over longer timescales, we find that malicious ASes connect with more upstream providers than legitimate ASes, and they also change upstream providers more frequently.