no code implementations • 14 Feb 2024 • Cheng Wang, Christopher Redino, Abdul Rahman, Ryan Clark, Daniel Radke, Tyler Cody, Dhruv Nandakumar, Edward Bowen
Results on a typical network configuration show that the RL agent can automatically discover resilient C2 attack paths utilizing both Tor-based and conventional communication channels, while also bypassing network firewalls.
1 code implementation • 28 Nov 2023 • Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed, Devin Quinn, Marc Vucovich, Dhruv Nandakumar, Kevin Choi, Abdul Rahman, Edward Bowen, Sachin Shetty
In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach.
no code implementations • 6 Jun 2023 • Dhruv Nandakumar, Sathvik Murli, Ankur Khosla, Kevin Choi, Abdul Rahman, Drew Walsh, Scott Riede, Eric Dull, Edward Bowen
The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web.
no code implementations • 1 Nov 2022 • Dhruv Nandakumar, Robert Schiller, Christopher Redino, Kevin Choi, Abdul Rahman, Edward Bowen, Marc Vucovich, Joe Nehila, Matthew Weeks, Aaron Shaha
The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale.
no code implementations • 12 Oct 2022 • Marc Vucovich, Amogh Tarcar, Penjo Rebelo, Narendra Gade, Ruchi Porwal, Abdul Rahman, Christopher Redino, Kevin Choi, Dhruv Nandakumar, Robert Schiller, Edward Bowen, Alex West, Sanmitra Bhattacharya, Balaji Veeramani
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior.
no code implementations • 29 Aug 2022 • Deepak Kushwaha, Dhruv Nandakumar, Akshay Kakkar, Sanvi Gupta, Kevin Choi, Christopher Redino, Abdul Rahman, Sabthagiri Saravanan Chandramohan, Edward Bowen, Matthew Weeks, Aaron Shaha, Joe Nehila
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack.
no code implementations • 4 May 2022 • Christopher Redino, Dhruv Nandakumar, Robert Schiller, Kevin Choi, Abdul Rahman, Edward Bowen, Matthew Weeks, Aaron Shaha, Joe Nehila
With this paper, the authors' overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.