We present the results of a survey of 85 computer security experts identifying commonsense misconceptions in computer security. Misconceptions can be hard to unlearn because they are often based on incorrect inferences about personal experience. Misconceptions can lead to mistakes when people reason correctly based on these false beliefs. Therefore, it is important for instructors to address misconceptions in their teaching and help students dispel them (e.g., through in-class and hands-on education) to keep graduates from repeating classic and pernicious mistakes. After developing a codebook of approximately 100 security misconceptions with a group of eight external security experts, two researchers coded all survey responses to identify the most frequent misconceptions seen in the data. Selecting the misconceptions appearing ten times or more in the data, we identified a list of seven classes of frequent and significant misconceptions held by novices in computer security. We describe our methodology and those misconceptions in depth along with their background.  These misconceptions are serving as the target for an under-development concept inventory on computer security, along with a set of educational resources to remediate them.

Dr. Peterson earned a Bachelors of Music Education (BME) from North Park University in Chicago in 1999, and an MS (2009) and PhD (2013) in Computer Science from UCLA. His research and teaching interests center on operating systems and computer security, particularly in computer security education. He has an NSF grant to identify and remediate commonsense misconceptions about computer security, is part of an NSF grant team studying the effect of active learning on security education, and an NSF CAREER project to identify and assess the critical cybersecurity ability known as "adversarial thinking." He also leads a team of students restoring and demonstrating a 50-year old PDP-12 minicomputer, one of only a handful still operating in the world. He is a member of the ACM, IEEE, USENIX, and Sigma Xi.

Host: Alan T. Sherman, sherman@umbc.edu. Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681. The UMBC Cyber Defense Lab meets biweekly Fridays 12-1pm.  All meetings are open to the public. Upcoming CDL meetings: Nov 4, Russ Fink (APL), ARMR: Autonomous resilience / machine recovery, Nov 18, Josiah Dykstra (DoD), Myths in cybersecurity, Dec 2, Peter Peterson (UMN Duluth), Adversarial Thinking, SFS/CySP Research Study: January 2-6, 2023 (tentative)

The U.S. Government Accountability Office (GAO) is hiring information technology (IT) interns for spring 2023.

Often called the "congressional watchdog," GAO is an independent, nonpartisan agency that advises Congress about ways to improve government.  IT interns will have the opportunity to be a part of GAO's Information Systems and Technology Services team (ISTS) that manages GAO information systems, IT services, and supports business improvement initiatives.

The internship is paid and has the potential to be converted to a permanent position at GAO. In addition, the internship can be completed on a full-time or part-time basis. Applications for the internship closes next Wednesday, October 19. See GAO's application tip sheet here.

Here are links to both the undergraduate and graduate applications.

The application for Google's 2023A CS Research Mentorship Program (CSRMP) is open for students to apply. CSRMP aims to support undergraduate and graduate students from historically marginalized groups to progress in computing research pathways. You can see previous CSRMP recipients and  read about CSRMP graduate Leslie Coney's experience in the program.

About the program: Students are matched into a pod with a Google mentor and small group of peers who are at a similar academic stage, and interested in the same research area. Pods structure discussion and identify opportunities around a topic shared by students and supported by the mentor through group and one-on-one meetings.

In addition, students join a broader peer community, and are invited to virtual networking events, career panels, tech talks, and information sessions about computing research opportunities. CSRMP does not assign research projects or internships.

The mentorship cycle runs for twelve weeks during the academic year with a minimum total time commitment of ten hours per student.

Eligibility: CSRMP aims to support students from historically marginalized groups to progress in computing research pathways. The definition of who is historically marginalized is responsive to a specific region, context, and its nuances. This can include students with marginalized gender and racial/ethnic identities, students with disabilities, students from low socioeconomic status, first-generation students, and intersections of these groups and others.

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This information session is for UMBC students interested in participating in the CIRCUIT program, which provides year-long, cohort-based research experiences for undergraduates. Students receive training, full-time summer salaries, and academic credit for part-time work during the school year.  CIRCUIT is led and funded by the Johns Hopkins University Applied Physics Lab (JHU/APL) with UMBC as a participating institution.

It's time to celebrate ALL the bwtech@UMBC nominees in a variety of categories, from who gained the most traction with revenue, workforce development, who hired the most interns and collaborated with others and more.

Link: https://www.google.com/url?q=https://bwtech.umbc.edu/event/cyber_tech_awards/&source=gmail-imap&ust=1665605427000000&usg=AOvVaw3zHt6x3DdBe5wD6a28pRKM

With the deluge of data in the modern world, machine learning has become a mainstay of industry and science. Machine learning algorithms aim to find underlying relationships hidden in a task that agree with prior knowledge and best generalize to unseen data making as few assumptions about the nature of the data as possible. In cases where several tasks are expected to share underlying structure, multitask learning aims to identify and exploit this shared structure to benefit all tasks. In functional magnetic resonance imaging (fMRI), for example, datasets collected from a group of subjects can be separated into statistically independent sources by joint processing to yield improved performance over what can be achieved considering each dataset alone. In other applications, such as recommender systems, tasks and the corresponding datasets may arrive sequentially over time. This introduces further challenges associated with online processing and modeling of the joint structure in an efficient manner -- key issues toward lifelong learning. Furthermore, where linear models are insufficient to adequately describe underlying task relations, it is desirable to capitalize on rich nonlinear function spaces to develop machine learning algorithms.

In this work, multitask learning is developed and applied to various machine learning problems. In unsupervised statistical learning, an independent vector analysis algorithm is developed based on a flexible, yet simple, family of distributions termed the complex-valued multivariate generalized Gaussian distribution. In lifelong supervised and reinforcement learning, kernel dictionary learning is used to capture the joint structure of streaming tasks in rich reproducing kernel Hilbert spaces. A sparsification technique is utilized to mitigate the effects of growing computational and storage complexity typical of kernel methods without sacrificing convergence guarantees. This approach is further generalized to tasks where the data emanate from different sources, or views, yielding a kernel lifelong multitask multiview learning algorithm. To affirm the effectiveness of the algorithms developed, experiments based on synthetic as well as real-world datasets are performed. The convergence of the developed supervised lifelong learning algorithms is also rigorously established.

Committee: Drs. Seung-Jun Kim (Chair), Tulay Adali, Tim Oates, Mohamed Younis, Ramana Vinjamuri

Applications are open now to join the 2023 Cybersecurity Talent Initiative, a public-private partnership to recruit the next generation of cyber defenders to safeguard our nation's infrastructure from global threats. Click here for more information.  U.S. citizenship is required.

Undergraduate or graduate students in a cybersecurity-related field can learn more about the Cybersecurity Talent Initiative and how to apply for this incredible opportunity.

Participants selected for the program will be guaranteed a two-year placement at a federal agency with cybersecurity needs. Before the end of their federal service, participants will be invited to apply for full-time positions with the program's private sector partners. Participants hired by these companies will also receive student loan assistance. Program benefits also include:

Visit its website to learn more about the Cybersecurity Talent Initiative, including applicant information and program requirements. Apply by November 11, 2022. For more information, contact: internships@ourpublicservice.org.