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pro vyhledávání: '"Filar, Bobby"'
Autor:
Raff, Edward, Fleshman, William, Zak, Richard, Anderson, Hyrum S., Filar, Bobby, McLean, Mark
Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may ex
Externí odkaz:
http://arxiv.org/abs/2012.09390
False positives (FPs) have been an issue of extreme importance for anti-virus (AV) systems for decades. As more security vendors turn to machine learning, alert deluge has hit critical mass with over 20% of all alerts resulting in FPs and, in some or
Externí odkaz:
http://arxiv.org/abs/2010.12080
Autor:
Raff, Edward, Zak, Richard, Munoz, Gary Lopez, Fleming, William, Anderson, Hyrum S., Filar, Bobby, Nicholas, Charles, Holt, James
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little
Externí odkaz:
http://arxiv.org/abs/2009.03779
Autor:
Filar, Bobby, French, David
It is becoming more common that adversary attacks consist of more than a standalone executable or script. Often, evidence of an attack includes conspicuous process heritage that may be ignored by traditional static machine learning models. Advanced a
Externí odkaz:
http://arxiv.org/abs/2008.04676
Autor:
Brundage, Miles, Avin, Shahar, Clark, Jack, Toner, Helen, Eckersley, Peter, Garfinkel, Ben, Dafoe, Allan, Scharre, Paul, Zeitzoff, Thomas, Filar, Bobby, Anderson, Hyrum, Roff, Heather, Allen, Gregory C., Steinhardt, Jacob, Flynn, Carrick, hÉigeartaigh, Seán Ó, Beard, Simon, Belfield, Haydn, Farquhar, Sebastian, Lyle, Clare, Crootof, Rebecca, Evans, Owain, Page, Michael, Bryson, Joanna, Yampolskiy, Roman, Amodei, Dario
This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digit
Externí odkaz:
http://arxiv.org/abs/1802.07228
Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or for supp
Externí odkaz:
http://arxiv.org/abs/1801.08917
Security products often create more problems than they solve, drowning users in alerts without providing the context required to remediate threats. This challenge is compounded by a lack of experienced personnel and security tools with complex interf
Externí odkaz:
http://arxiv.org/abs/1707.05768
Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a p
Externí odkaz:
http://arxiv.org/abs/1610.01969
Publikováno v:
International Journal of Information Security; Feb2024, Vol. 23 Issue 1, p533-556, 24p
Akademický článek
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