ACM chapter talk Career, job search, and interviewing tips
Nikhil Kumar Mengani (UMBC MS CS ’18), Microsoft SDE
The UMBC student ACM chapter will hold a session on careers and job searches from 4:00 pm to 5:00 pm ET on Saturday, March 13. Nikhil Mengani, a UMBC graduate and current Microsoft Software Development Engineer, will talk about interview tips, using LinkedIn, and overall job search best practices.
Join the online meeting for some great insights and a Q&A session with Nikhil. Join via webex. For more information, contact Samit Shivadekar at
talk: Moving Target Mobile IPv6 Defense, 12-1 Fri 2/26
The UMBC Cyber Defense Lab presents
Moving Target Mobile IPv6 Defense
Prof.Vahid Heydari Computer Science, Rowan University
Remote cyberattacks can be started from an unlimited distance through the Internet. These attacks include particular actions that allow attackers to compromise systems remotely. Address-based Distributed Denial-of-Service (DDoS) attacks and remote exploits are two main categories of these attacks. A remote exploit takes advantage of a bug or vulnerability to view or steal data or gain unauthorized access to a vulnerable system. Current security solutions in IPv6 such as IPsec, firewall, and Intrusion Detection and Prevention System (IDPS) can prevent remote attacks against known vulnerability exploits. However, zero-day exploits can defeat the best firewalls and IDPSs due to using undisclosed and uncorrected computer application vulnerability. Therefore, a new solution is needed to prevent these attacks. This talk discusses a Moving Target Mobile IPv6 Defense (MTM6D) that randomly and dynamically changes the IP addresses to prevent remote attacks in the reconnaissance step. The talk briefly covers the wide range of applications of MTM6D including critical infrastructure networks, virtual private networks, web servers, Internet-controlled robots, and anti-censorship.
Vahid Heydari received the M.S. degree in Cybersecurity and the Ph.D. degree in Electrical and Computer Engineering from the University of Alabama in Huntsville. He is currently an Associate Professor of Computer Science and the Director of the Center for Cybersecurity Education and Research at Rowan University, Glassboro, NJ. He is also a co-founder of a cybersecurity startup ObtegoCyber. His research interests include moving target defenses, mobile ad-hoc, sensor, and vehicular network security. He is a member of ACM, IEEE Computer Society and Communications Society.
Host: Alan T. Sherman, , 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. All meetings are open to the public. Upcoming CDL Meetings:
Mar 12, Chao Liu (UMBC), Efficient asynchronous BFT with adaptive security Mar 26, Jeremy Clark (Concordia) April 9, (UMBC), MeetingMayhem: A network adversarial thinking game April 23, Peter Peterson (University of Minnesota Duluth), Adversarial thinking May 7, Farid Javani (UMBC), Anonymization by oblivious transfer
talk: Ed Raff on Machine Learning for Malware: Challenges and Progress, 12-1pm ET Wed 2/17
UMBC Information Systems Department
Machine Learning for Malware: Challenges and Progress
Dr. Edward Raff Booz Allen Hamilton Visiting Prof. UMBC Computer Science & Electrical Engineering
Malware is an ever-growing problem, single malware families have caused billions in damages, and the first direct death attributed to malware taking down a hospital has occurred. To detect new malware, machine learning is a naturally attractive approach. However, malware poses a number of unique challenges that have slowed the progress of ML-based solutions. In this talk, we will look at the task of malware detection from byte-based analysis, why it poses many challenging machine learning research problems, and progress we have made on these tasks by taking some non-standard approaches to machine learning: building shallow and wide networks instead of deep, handicapping the features of our model to make it robust, and using literal compression algorithms (LZMA) to find similar content.
Edward Raff leads Booz Allen’s machine learning research group and supports clients in developing new ML solutions. His research includes cybersecurity, adversarial machine learning, fairness and ethics, fingerprint biometrics, and high-performance computing. In his spare time, he is the author of the JSAT machine learning library. He received his BS and MS in Computer Science from Purdue University and his Ph.D. in CS from UMBC. Dr. Raff is a Nvidia Deep Learning certified instructor, and Visiting Professor at UMBC.
talk: Modeling and Simulation for Reducing Risks Associated with Extreme Weather, 11-12 2/10
CARTA Distinguished Lecture
Modeling and Simulation for Reducing the Risks Associated with Extreme Weather
Dr. Robert Atlas
Research Professor & Global Coordinator for CARTA Director Emeritus/ NOAA Atlantic Oceanographic and Meteorological Laboratory
The reduction of losses related to hurricanes and other extreme weather phenomena involves many complex aspects ranging from purely theoretical, observational, computational, and numerical, to operational and decisional. A correct warning can lead to proper evacuation and damage mitigation, and produce immense benefits. However, over-warning can lead to substantial unnecessary costs, a reduction of confidence in warnings, and a lack of appropriate response. In this chain of information, the role played by scientific research is crucial.
The National Oceanic and Atmospheric Administration (NOAA), in combination with the National Aeronautics and Space Administration (NASA), other agencies, and universities is contributing to these efforts through observational and theoretical research to better understand the processes associated with extreme weather. This includes model and data assimilation development, Observing System Experiments (OSE), and Observing System Simulation Experiments (OSSE) designed to ascertain the value of existing observing systems and the potential of new observing systems to improve weather prediction and theoretical understanding. This high-level talk, which was first given as the Keynote address at the 2019 Winter Simulation Conference, will describe innovative research for developing advanced next-generation global and regional models to improve weather prediction, and the application of OSSEs to optimize the observing system.
Dr. Robert Atlas is the former Chief Meteorologist at NASA’s Goddard Laboratory for Atmospheres and is Director Emeritus of the National Oceanic and Atmospheric Administration’s (NOAA) Atlantic Oceanographic and Meteorological Laboratory in Miami, Fla. Some of the areas he focused his research on included the prediction, movement, and strengthening of hurricanes. He has worked with both satellite data and computer models as a means to study these hurricane behaviors.
Dr. Atlas received his Ph.D. in Meteorology and Oceanography in 1976 from New York University. Prior to receiving the doctorate, he was a weather forecaster in the U.S. Air Force where he maintained greater than 95 percent forecast accuracy. From 1976 to 1978, Dr. Atlas was a National Research Council Research Associate at NASA’s Goddard Institute for Space Studies, New York, an Assistant Professor of Atmospheric and Oceanic Science for SUNY, and Chief Consulting Meteorologist for the ABC television network.
In 1978, Dr. Atlas joined NASA as a research scientist. He served as head of the NASA Data Assimilation Office from 1998-2003, and as Chief meteorologist at NASA GSFC from 2003-2005. Dr. Atlas has performed research to assess and improve the impact of satellite data on numerical weather prediction since 1973. He was a key member of the team that first demonstrated the significant impact of quantitative satellite data on numerical weather prediction and is the world’s leading expert on Observing System Simulation Experiments, a technology that enables scientists to determine the quantitative value of new observing systems before funds are allocated for their development.
He served as a member of the Satellite Surface Stress Working Group, the NASA Scatterometer (NSCAT) Science Team, the ERS Science Team, the SeaWinds Satellite Team, the Working Group for Space-based Laser Winds, the Scientific Steering Group for GEWEX, the Council of the American Meteorological Society, and as Chairman of the U.S. World Ocean Circulation Experiment (WOCE) Advisory Group for model-based air-sea fluxes. He is currently a member of the Science Teams for two NASA space missions.
From 1974-1976, he developed a global upper-ocean model and studied oceanic response to atmospheric wind forcing as well as large-scale atmospheric response to sea surface temperature (SST) anomalies (unusual events). In more recent years, his research concentrated on the role of how the air and sea interact in the development of cyclones, the role of soil moisture and unusual SST events in the initiation, maintenance, and decay of prolonged heatwaves and drought, and most recently on the modeling and prediction of hurricane formation, movement, and intensification.
He is a recipient of the NASA Medal for Exceptional Scientific Achievement and the American Meteorological Society’s Banner I. Miller Award. In 2019, just prior to his retirement from NOAA, he was honored by the National Hurricane Center for Enduring Contributions to the nation’s hurricane forecast and warning program, and by the U.S. House of Representatives for his service to the nation.
talk: Theoryful Machine Learning in the Chemical Sciences, 1-2 Fri 2/5
Theoryful Machine Learning in the Chemical Sciences
Modern machine learning (ML) algorithms have achieved remarkable success in “theoryless” problems of image recognition and natural language processing. When these algorithms find applications in “theoryful” domains like physical sciences, they frequently benefit from the incorporation of domain knowledge into the ML architecture, whether enforcing constraints or symmetries or interpreting neural networks as physical systems.
The chemical sciences have many “theoryful” ML problems. In this talk, I will discuss three projects in which we leverage background theory when designing and adopting ML algorithms. In the first project, we use classical thermodynamics to derive a method to characterize mixture properties in molecular simulations and show that multiple linear regression (with no bias) is the formally correct and thermodynamically consistent model for fitting and predicting these properties. We recently developed an alternative proof from statistical thermodynamics that gives the same result, and we provide evidence that nonlinear methods provide no improvement in performance. In the second project, we perform high-throughput molecular simulations of adsorption (when molecules from a gas or liquid stick on the surface or in the pores of a material), which we analyze using neural networks. We derive a correspondence between theories of multicomponent adsorption and the self-attention mechanism in the transformer architecture and show how the theory-inspired architecture has improved generalization over the multilayer perceptron.
In the final project, I will share work on symbolic regression, in collaboration with the Mathematics of AI department at IBM. In symbolic regression, given a data set, a search through some “space of possible equations” identifies accurately-fitting and parsimonious equations that can be easily inspected by humans. We formulate the symbolic regression problem as a mixed-integer nonlinear programming (MINLP) problem and use MINLP solvers to systematically solve multiple functional forms at once, instead of via the traditional approaches that use genetic algorithms. Future approaches to integrate symbolic regression with chemical theory will be discussed.
Tyler R. Josephson is an Assistant Professor in the Chemical, Biochemical, and Environmental Engineering department at the University of Maryland, Baltimore County. He received his B.S. in Chemical Engineering from the University of Minnesota in 2011, and his Ph.D. in Chemical Engineering from the University of Delaware in 2017, after which he was a postdoctoral associate in the University of Minnesota Chemistry Department. Prof. Josephson uses multi-scale modeling and machine learning to study catalysis, solvation, adsorption, and phase equilibria. During his downtime, he loves learning new things, thinking about deep topics (like science and philosophy), and playing the piano.
talk: 2021 SFS Research Study: Vulnerabilities in UMBC’s Incident Management System, 12-1 Jan. 29
The 2021 SFS Research Study: Vulnerabilities in UMBC’s Incident Management System
Cyrus Bonyadi and Enis Golaszewski CSEE Department, UMBC
January 11–15, 2020, UMBC scholars in the CyberCorps: Scholarship for Service (SFS) and the DoD Cybersecurity Scholarship (CySP) programs collaboratively analyzed the security of UMBC’s Incident Management System (IMS). Students found numerous serious issues, including race conditions, code-injection, and cross-site scripting attacks, improper API implementation, and denial-of-service attacks. We present findings, recommendations, and details of these vulnerabilities.
UMBC’s Incident Management System (IMS) is a web application under development by UMBC’s DoIT to supplement their RequestTracker (RT). IMS allows DoIT security staff to supplement the information in RT by linking IMS incidents to RT tickets. IMS incidents store additional information and files regarding existing and potential security campaigns. Using the information in IMS and RT, DoIT generates executive reports, which can influence decisions related to budget, training, and other security concerns. Our study is helping to improve the architecture and implementation of IMS.
Participants comprised BS, MS, MPS, and Ph.D. students studying computer science, computer engineering, information systems, and cybersecurity, including SFS scholars who transferred from Montgomery College (MC) and Prince George’s Community College (PGCC) to complete their four-year degrees at UMBC.
About the Speakers. Cyrus Jian Bonyadi is a Ph.D. Student at UMBC working on distributed computing consensus theory. He is an alumnus of the varsity CyberDawgs team. email: Enis Golaszewski is a Ph.D. Student at UMBC working on protocol analysis. He is a leading member of the Protocol Analysis Lab under Dr. Sherman. email: ,
Host: Alan T. Sherman, . 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 12-1 pm. All meetings are open to the public. Upcoming CDL Meetings:
Feb 12, Richard Carback (xxnetwork), Startup lessons learned
Feb 26, Vahid Heydari (Rowan University)
Mar 12, Chao Liu (UMBC), Efficient asynchronous BFT with adaptive security
Mar 26, Jeremy Clark (Concordia)
April 9, (UMBC), MeetingMayhem: A network adversarial thinking game
April 23, Peter Peterson (University of Minnesota Duluth), Adversarial thinking
May 7, Farid Javani (UMBC), Anonymization by oblivious transfer
talk: Intelligence Community Election Security 2020, 12-1 Fri Dec 11
talk: Tim Brennan on “Economics of Law” – Insights into Cybersecurity Policy, 12pm Tue 12/8
The UMBC Center for Cybersecurity (UCYBR) Presents
“Economics of Law” – Insights into Cybersecurity Policy
Cybersecurity raises questions about who owns data and how best to discourage security breaches. This talk will offer some unexpected and perhaps controversial perspectives from economics on relevant questions, including: Who presumptively should own data? What is the purpose of liability law? Should those who violate data security always be liable, or only if they fail to take appropriate measures to prevent leaks? Could “the market” solve the problem, e.g., by people choosing where to shop on the basis of data security? Would regulation be a better means than liability to promote cybersecurity? Don’t expect answers to these questions; my hope is to stimulate and hopefully inform the discussion. If time allows, I’ll review some major actions by the Federal Trade Commission, who is the lead national agency policing privacy-related conduct.
Dr. Tim Brennan is professor emeritus of public policy and economics at UMBC, retiring in July 2020 after thirty years on the UMBC faculty. He has also been FCC Chief Economist, held the T.D. MacDonald Chair in the Canadian government’s Competition Bureau, and served on the staff of the White House Council of Economic Advisers. Before UMBC, he was an associate professor of telecommunications and public policy at George Washington University and a staff economist at the US Department of Justice Antitrust Division. He has over 130 articles and book chapters and books on competition policy, economic regulation, telecommunications and energy policy, intellectual property, and economic methods. His MA in math and Ph..D. in economics are from the University of Wisconsin.
talk: Medical Informatics – Promise and Barriers Towards Precise Medicine, 10am ET Mon 11/23, Webex
The challenging time facing the pandemic forced us to relate to the human being’s broadband picture and his surrounding as one functioning system across countries and continents. The need is to relate both to the Micro (including in-body, physical, and mental conditions) and the Macro (such as environmental, cultural, and economic factors) providing a comprehensive understanding of the human body functioning in the surrounding, towards a precise, personalized “disease signature,” definition, especially these days. A systematic literature review on the “disease signature” term revealed no clear definition. In many articles, the “disease signature” phrase appears as a single biomarker (often genetic), mainly related to neurology or oncology. (Stemmer, A. at All, 2019. Journal of Molecular Neuroscience, 67(4)). The major goal is the unity of nature, science, and technology, from the nanoscale towards converging knowledge and tools, at a confluence of disciplines, as was envisioned by the NSF in 2001 (NBIC) and further at the joint EU-US WTEC effort “Converging of Knowledge, Technology, Society,” Roco et al., Springer 2013.
The COVID-19 global health emergency increased the need for early precise diagnosis and treatment while facing major physical and mental threat and stress, such as Post Traumatic Stress Disorder (PTSD). These understandings reemphasized the need to join all forces, converge, verify and embed all knowledge, expertise, and new advanced technologies in the various disciplines. Furthermore, it enforced to verify the data originated by various sources while bridging all cultural, conceptual, curation and technology barriers, preserving privacy and ethics regulations and ensuring reliable advanced analysis tools. All of the above provide profound insight into the human body and brain functioning in the surrounding and reliable “Disease Signature,” followed by suitable therapeutic treatment.
The question to be asked: Are we able to collect Big enough data, distributed and representative enough, while bridging all barriers and accurate analysis tools to ensure reliable, replicable, reproducible outcome towards precise, personalized medicine? The Brain Medical Informatics Platform (MIP), developed by the EU Human Brain Flagship Project, as part of the EBRAINS platform, is a key feasibility study along these lines. It involves broad clinical data collections from 30 hospitals, converging knowledge and data, embedding new technologies for data privacy, preservation, and curation, as well as sophisticated analysis tools. The MIP and EBRAINS framework goal is to identify “BRAIN Disease Signatures” towards reliable medical treatment. A 3C (Categorize, Classify, Cluster) Methodology, developed in our lab, is one of the tools available on the MIP. It incorporates expert medical knowledge and experience into the analysis process of disease manifestation and potential biomarkers towards reliable insights. The 3C approach was applied to the ADNI (Alzheimer’s disease Neuro Imaging) cohort, discovering association with new subtypes, which were later verified using the Rome Gemelli hospital labs clinical data. Other case studies were Parkinson’s Disease, genetic and biomarker research: (Tal Kozlovski, et al., 2019, Frontiers in Neurology, Movement Disorders), as well as PTSD research (Ben-Zion et al., 2020, Translational Psychiatry), both in collaboration with the Tel Aviv Medical Center. The COVID-19 global health emergency increased the need for early precise diagnosis and treatment while facing major physical and mental threat and stress, such as Post Traumatic Stress Disorder (PTSD). These understandings reemphasized the need to join all forces, converge, verify and embed all knowledge, expertise, and new advanced technologies in the various disciplines. Furthermore, it enforced to verify the data originated by various sources while bridging all cultural, conceptual, curation and technology barriers, preserving privacy and ethics regulations and ensuring reliable advanced analysis tools. All of the above to provide profound insight into the human body and brain functioning in the surrounding as well as reliable “Disease Signature”, followed by suitable therapeutic treatment.
Providing “Healthy Aging” to the elderly is a perfect example conceiving all, these days, as the elderly became one of the vulnerable groups at risk. The loneliness and isolation forced by the current pandemic results in severe conditions, including stress disorders and PTSD. Thus, an International “Healthy Aging” initiative was established at TAU, promoting broad interdisciplinary research, combining knowledge and data analysis as well as advanced technologies, from most areas of science: including economics, art, social sciences, mental and physical health, lifestyle, engineering, etc. All that to ensure the best fitted reliable treatment and a balanced quality of life to the elderly in general, and in these days, in particular.
Dr. Mira Marcus-Kalish is the Director of International Research Collaborations at Tel Aviv University. Her main areas of research are mathematical modeling, converging technologies, and data mining. Dr. Kalish holds a Ph.D. in Operations Research from the Technion, Israel Institute of Technology, where she developed one of the first computerized systems for electrocardiogram (ECG) diagnosis. Her postdoctoral training was at Harvard University, the MBCRR (Molecular Biology Computer Research and Resource) laboratory, and at the Dana Farber Cancer Institute. She was awarded her B.Sc. in Statistics and Biology from the Hebrew University of Jerusalem
talk: Elisa Bertino on Security and Privacy in the IoT, 1-2 Fri 11/20
The Internet of Things (IoT) paradigm refers to the network of physical objects or”things” embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. IoT will usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g., Smart Grid), to healthcare management and urban life (e.g. Smart City). However, because of its fine-grained, continuous, and pervasive data acquisition and control capabilities, IoT raises concerns about security and privacy. Deploying existing security solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in IoT security and privacy, we present initial approaches to securing IoT data and then focus on our recent work on security analysis for cellular network protocols and edge-based anomaly detection.
Elisa Bertino is a professor of Computer Science at Purdue University. Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, and at Telcordia Technologies. Her main research interests include security, privacy, database systems, distributed systems, and sensor networks. Her research focuses on digital identity management, biometrics, IoT security, security of 4G and 5G cellular network protocols, and policy infrastructures for managing distributed systems. Prof. Bertino has published more than 700 papers in all major refereed journals, and in proceedings of international conferences and symposia. She has given keynotes, tutorials, and invited presentations at conferences and other events. She is a Fellow member of ACM, IEEE, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award “For outstanding contributions to database systems and database security and advanced data management systems”, the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”, and the ACM 2019-2020 Athena Lecturer Award.