Everyone loves the performance t-shirts we made last time (see picture below). This is why we're doing it again this year! Though our budget is very limited, we will do the best to order different styles and sizes for our group. Our plan is to have the t-shirts ready for giveaways in Spring 2024. 

Please complete this 10-second survey to help us determine the quantities we need per size/style. Thank you very much!  If you have questions, please email (suns at umbc.edu).

Link to the survey  (Access restricted to UMBC users)

If you've never taken Mental Health First Aid (or it's been 3 years or more since you've taken it), now is the time to sign up for training!

As we shared with you during our first GBM of the new year two weeks ago, we are looking for some new members for our e-board. We are in search for a treasurer and a social media manager. 

The election form is now open and we ask that you complete it before this upcoming Wednesday evening (Oct 4th).

Below is a link to a Google Form that we will be using to record your interest in running. Once you have completed the form, we will send you an email asking you to fill out a slide template to be shown during the election. 

The election will be taking place THIS FRIDAY (Oct 6th) during our GBM at 2pm in ILSB 230! We hope to see you then! :)

Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files, and classifying malware by family. Malware, however, can be categorized according to many other types of attributes, and the ability to identify these attributes in newly-emerging malware using machine learning will provide significant value to analysts. In particular, we have identified four tasks which are under-represented in prior work: classification by behaviors that malware exhibit, platforms that malware run on, vulnerabilities that malware exploit, and packers that packed the malware. To obtain labels for training and evaluating ML classifiers on these tasks, we created an antivirus (AV) tagging tool called ClarAVy. ClarAVy's sophisticated AV label parser distinguishes itself from prior AV-based taggers with the ability to parse 882 different AV label formats used by 90 different AV products accurately. We are releasing benchmark datasets for each of these four classification tasks, tagged using ClarAVy and comprising nearly 5.5 million malicious files in total. Our malware behavior dataset includes 75 distinct tags -- nearly seven times more than the only prior benchmark dataset with behavioral tags. To our knowledge, we are the first to release datasets with malware platform, exploitation, and packer tags.

RJ Joyce (joyce8@umbc.edu) is a PhD student at UMBC under the supervision of Dr. Charles Nicholas and Dr. Edward Raff. Presently, RJ works as a data scientist at Booz Allen Hamilton performing research at the intersection of malware analysis and machine learning. RJ is also a visiting lecturer at UMBC and is teaching CMSC-426 Principles of Computer Security course this semester.

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: Oct. 20 (1-2pm) Josh Benaloh (Microsoft), ElectionGuard; Nov. 3, Jason Rheinhart (Sandia), Risk analysis; Nov. 17 (1-2pm) Austin Murdoch (Sixmap); Dec. 1, Enis Golaszewski (UMBC), Automatic cryptographic bindings; Jan. 16-19, 2024, UMBC SFS/CySP Research Study.