Zobrazeno 1 - 10
of 805
pro vyhledávání: '"NICHOLAS, CHARLES"'
Autor:
Gupta, Siddhant, Lu, Fred, Barlow, Andrew, Raff, Edward, Ferraro, Francis, Matuszek, Cynthia, Nicholas, Charles, Holt, James
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent. In this work, we ask if there is a way for anti-virus deve
Externí odkaz:
http://arxiv.org/abs/2411.18516
It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are explained as learning a kind of compression, and that better compression l
Externí odkaz:
http://arxiv.org/abs/2410.15280
Autor:
Barron, Ryan C., Grantcharov, Ves, Wanna, Selma, Eren, Maksim E., Bhattarai, Manish, Solovyev, Nicholas, Tompkins, George, Nicholas, Charles, Rasmussen, Kim Ø., Matuszek, Cynthia, Alexandrov, Boian S.
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific
Externí odkaz:
http://arxiv.org/abs/2410.02721
Autor:
Barron, Ryan, Eren, Maksim E., Bhattarai, Manish, Wanna, Selma, Solovyev, Nicholas, Rasmussen, Kim, Alexandrov, Boian S., Nicholas, Charles, Matuszek, Cynthia
Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting action
Externí odkaz:
http://arxiv.org/abs/2403.16222
Autor:
Eren, Maksim E., Barron, Ryan, Bhattarai, Manish, Wanna, Selma, Solovyev, Nicholas, Rasmussen, Kim, Alexandrov, Boian S., Nicholas, Charles
National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods f
Externí odkaz:
http://arxiv.org/abs/2403.02546
One of the major benefits of quantum computing is the potential to resolve complex computational problems faster than can be done by classical methods. There are many prototype-based clustering methods in use today, and selection of the starting node
Externí odkaz:
http://arxiv.org/abs/2401.11258
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can cause an overwhelming number of false positives. However, academic re
Externí odkaz:
http://arxiv.org/abs/2312.15813
Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files and classifying malware by family. However, malware can be categorized according to many other types of attributes,
Externí odkaz:
http://arxiv.org/abs/2310.11706
Autor:
Eren, Maksim E., Bhattarai, Manish, Joyce, Robert J., Raff, Edward, Nicholas, Charles, Alexandrov, Boian S.
Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of r
Externí odkaz:
http://arxiv.org/abs/2309.06643