Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Redino, Christopher"'
Autor:
Wang, Cheng, Redino, Christopher, Clark, Ryan, Rahman, Abdul, Aguinaga, Sal, Murli, Sathvik, Nandakumar, Dhruv, Rao, Roland, Huang, Lanxiao, Radke, Daniel, Bowen, Edward
Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks, organizations commonl
Externí odkaz:
http://arxiv.org/abs/2406.17576
Autor:
Wang, Cheng, Redino, Christopher, Rahman, Abdul, Clark, Ryan, Radke, Daniel, Cody, Tyler, Nandakumar, Dhruv, Bowen, Edward
Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execute harmful actions, such as propagating malicious code across networks,
Externí odkaz:
http://arxiv.org/abs/2402.09200
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing
Externí odkaz:
http://arxiv.org/abs/2312.04587
Autor:
Rishu, Riddam, Kakkar, Akshay, Wang, Cheng, Rahman, Abdul, Redino, Christopher, Nandakumar, Dhruv, Cody, Tyler, Clark, Ryan, Radke, Daniel, Bowen, Edward
Building on previous work using reinforcement learning (RL) focused on identification of exfiltration paths, this work expands the methodology to include protocol and payload considerations. The former approach to exfiltration path discovery, where r
Externí odkaz:
http://arxiv.org/abs/2310.03667
Autor:
Murli, Sathvik, Nandakumar, Dhruv, Kushwaha, Prabhat Kumar, Wang, Cheng, Redino, Christopher, Rahman, Abdul, Israni, Shalini, Singh, Tarun, Bowen, Edward
We present a novel approach to identify ransomware campaigns derived from attack timelines representations within victim networks. Malicious activity profiles developed from multiple alert sources support the construction of alert graphs. This approa
Externí odkaz:
http://arxiv.org/abs/2309.00700
Autor:
Nandakumar, Dhruv, Quinn, Devin, Soba, Elijah, Kim, Eunyoung, Redino, Christopher, Chan, Chris, Choi, Kevin, Rahman, Abdul, Bowen, Edward
In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As the complexity of malicious tactics, techniques, and procedures (TTPs) c
Externí odkaz:
http://arxiv.org/abs/2305.15488
Autor:
Huang, Lanxiao, Cody, Tyler, Redino, Christopher, Rahman, Abdul, Kakkar, Akshay, Kushwaha, Deepak, Wang, Cheng, Clark, Ryan, Radke, Daniel, Beling, Peter, Bowen, Edward
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL
Externí odkaz:
http://arxiv.org/abs/2211.03027
Autor:
Nandakumar, Dhruv, Schiller, Robert, Redino, Christopher, Choi, Kevin, Rahman, Abdul, Bowen, Edward, Vucovich, Marc, Nehila, Joe, Weeks, Matthew, Shaha, Aaron
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. The diverse nature of normal behavior along with the huge landscape of a
Externí odkaz:
http://arxiv.org/abs/2211.00441
Autor:
Vucovich, Marc, Tarcar, Amogh, Rebelo, Penjo, Gade, Narendra, Porwal, Ruchi, Rahman, Abdul, Redino, Christopher, Choi, Kevin, Nandakumar, Dhruv, Schiller, Robert, Bowen, Edward, West, Alex, Bhattacharya, Sanmitra, Veeramani, Balaji
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, federated learning has provided a way for a global model to be trained w
Externí odkaz:
http://arxiv.org/abs/2210.06614
Autor:
Kushwaha, Deepak, Nandakumar, Dhruv, Kakkar, Akshay, Gupta, Sanvi, Choi, Kevin, Redino, Christopher, Rahman, Abdul, Chandramohan, Sabthagiri Saravanan, Bowen, Edward, Weeks, Matthew, Shaha, Aaron, Nehila, Joe
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. Lateral Movement intrus
Externí odkaz:
http://arxiv.org/abs/2208.13524