Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Miki, E."'
Autor:
Bridges, Robert A., Weber, Brian, Beaver, Justin M., Smith, Jared M., Verma, Miki E., Norem, Savannah, Spakes, Kevin, Watson, Cory, Nichols, Jeff A., Jewell, Brian, Iannacone, Michael. D., Stahl, Chelsey Dunivan, Huffer, Kelly M. T., Oesch, T. Sean
This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial Intell
Externí odkaz:
http://arxiv.org/abs/2308.14835
Autor:
Blevins, Deborah H., Moriano, Pablo, Bridges, Robert A., Verma, Miki E., Iannacone, Michael D., Hollifield, Samuel C
Publikováno v:
Workshop on Automotive and Autonomous Vehicle Security (AutoSec) 2021
Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface which is vulnerable to message
Externí odkaz:
http://arxiv.org/abs/2101.05781
Autor:
Verma, Miki E., Bridges, Robert A., Iannacone, Michael D., Hollifield, Samuel C., Moriano, Pablo, Hespeler, Steven C., Kay, Bill, Combs, Frank L.
Publikováno v:
PLoS one 19, no. 1 (2024): e0296879
Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions on CANs. Producing vehicul
Externí odkaz:
http://arxiv.org/abs/2012.14600
Autor:
Bridges, Robert A., Oesch, Sean, Verma, Miki E., Iannacone, Michael D., Huffer, Kelly M. T., Jewell, Brian, Nichols, Jeff A., Weber, Brian, Beaver, Justin M., Smith, Jared M., Scofield, Daniel, Miles, Craig, Plummer, Thomas, Daniell, Mark, Tall, Anne M.
Publikováno v:
Digital Threats: Research and Practice 2023
In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files? Is it wort
Externí odkaz:
http://arxiv.org/abs/2012.09214
Autor:
Miki E Verma, Robert A Bridges, Michael D Iannacone, Samuel C Hollifield, Pablo Moriano, Steven C Hespeler, Bill Kay, Frank L Combs
Publikováno v:
PLoS ONE, Vol 19, Iss 1, p e0296879 (2024)
Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Prod
Externí odkaz:
https://doaj.org/article/ddd94838251c439ca697993e9312f0e5
Autor:
Verma, Miki E., Bridges, Robert A., Sosnowski, Jordan J., Hollifield, Samuel C., Iannacone, Michael D.
CANs are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make,
Externí odkaz:
http://arxiv.org/abs/2006.05993
Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle fun
Externí odkaz:
http://arxiv.org/abs/1811.07897
Autor:
Verma, Miki E., Bridges, Robert A.
Host logs, in particular, Windows Event Logs, are a valuable source of information often collected by security operation centers (SOCs). The semi-structured nature of host logs inhibits automated analytics, and while manual analysis is common, the sh
Externí odkaz:
http://arxiv.org/abs/1811.00591
Autor:
Verma, Miki E.1 (AUTHOR), Bridges, Robert A.2 (AUTHOR), Iannacone, Michael D.2 (AUTHOR), Hollifield, Samuel C.2 (AUTHOR), Moriano, Pablo3 (AUTHOR) moriano@ornl.gov, Hespeler, Steven C.3 (AUTHOR), Kay, Bill3,4 (AUTHOR), Combs, Frank L.5 (AUTHOR)
Publikováno v:
PLoS ONE. 1/22/2024, Vol. 19 Issue 1, p1-32. 32p.
Autor:
Robert A. Bridges, Sean Oesch, Michael D. Iannacone, Kelly M. T. Huffer, Brian Jewell, Jeff A. Nichols, Brian Weber, Miki E. Verma, Daniel Scofield, Craig Miles, Thomas Plummer, Mark Daniell, Anne M. Tall, Justin M. Beaver, Jared M. Smith
Publikováno v:
Digital Threats: Research and Practice.
There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detector