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The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

Miriam E. Armstrong - One of the best experts on this subject based on the ideXlab platform.

Omprakash Gnawali - One of the best experts on this subject based on the ideXlab platform.

  • making whitelisting based Defense Work against badusb
    Proceedings of the 2nd International Conference on Smart Digital Environment, 2018
    Co-Authors: Hessam Mohammadmoradi, Omprakash Gnawali
    Abstract:

    Universal serial bus (USB) devices have widespread use in different computing platforms, including IoT gadgets, but this popularity makes them attractive targets for exploits and being used as an attack vector by malicious software. During recent years, several reports [17] ranked USB-based malware among top 10 popular malware. This security flaw can slow down the increasing penetration rate of IoT devices since most of those devices have USB ports. The research community and industry has tried to address USB security problem by implementing authentication protocols to protect users' private information and also scanning USB's storage space for any malicious software using their own repository of malware signatures, or simply disallowing use of USB devices on desktops. The new generation of USB malware does not hide in storage space, which means they are not detectable by conventional anti-malware. BadUSB is a malware recently introduced by security researchers. BadUSB modifies USB firmware and can attack all the systems which the infected USB is plugged in. The only applicable solution against this new generation of malware is whitelisting. However, generating a unique fingerprint for USB devices is challenging. In this paper, we propose an accurate USB feature based fingerprinting approach which helps us to create a list of trusted USBs as device whitelist. Our solution prevents and detects BadUSB and similar attacks by generating fingerprint from trusted USB devices' features and their primary usage. We verified the uniqueness of our generated fingerprints by analyzing real data which is collected from USB drives used by students in academic computer labs over one year. Our results indicate that our feature based whitelisting approach with an accuracy of 98.5% can identify USB whitelist members.

  • ICSDE - Making Whitelisting-Based Defense Work Against BadUSB
    2018
    Co-Authors: Hessam Mohammadmoradi, Omprakash Gnawali
    Abstract:

    Universal serial bus (USB) devices have widespread use in different computing platforms, including IoT gadgets, but this popularity makes them attractive targets for exploits and being used as an attack vector by malicious software. During recent years, several reports [17] ranked USB-based malware among top 10 popular malware. This security flaw can slow down the increasing penetration rate of IoT devices since most of those devices have USB ports. The research community and industry has tried to address USB security problem by implementing authentication protocols to protect users' private information and also scanning USB's storage space for any malicious software using their own repository of malware signatures, or simply disallowing use of USB devices on desktops. The new generation of USB malware does not hide in storage space, which means they are not detectable by conventional anti-malware. BadUSB is a malware recently introduced by security researchers. BadUSB modifies USB firmware and can attack all the systems which the infected USB is plugged in. The only applicable solution against this new generation of malware is whitelisting. However, generating a unique fingerprint for USB devices is challenging. In this paper, we propose an accurate USB feature based fingerprinting approach which helps us to create a list of trusted USBs as device whitelist. Our solution prevents and detects BadUSB and similar attacks by generating fingerprint from trusted USB devices' features and their primary usage. We verified the uniqueness of our generated fingerprints by analyzing real data which is collected from USB drives used by students in academic computer labs over one year. Our results indicate that our feature based whitelisting approach with an accuracy of 98.5% can identify USB whitelist members.

Alissa Pollitz Worden - One of the best experts on this subject based on the ideXlab platform.

  • Representing the Accused: Professional Values and Professional Choices of Small-Town Lawyers
    Criminal Justice Review, 1998
    Co-Authors: Alissa Pollitz Worden
    Abstract:

    This study explores the professional values and practices of small-town lawyers, focusing on the associations between social and professional background, attitudes toward criminal defendants and criminal justice policy, and lawyers' involvement in criminal Defense Work. Most studies of lawyers' professional choices, particularly those of criminal Defense specialists, have been conducted in urban bars, where specialization is more typical than general practice. The present study refines hypotheses from previous studies and applies them in small-town settings. The results suggest, first, that lawyers' attitudes toward criminal Defense Work are multidimensional, second, that these attitudes are only weakly related to lawyers' social and professional backgrounds, third, that concentration in criminal Defense Work is associated with practice settings, not with lawyers' personal characteristics, and, fourth, that attitudes about criminal defendants and criminal justice policy are significantly associated with t...

Akbar Siami Namin - One of the best experts on this subject based on the ideXlab platform.

Albert N. Shiryaev - One of the best experts on this subject based on the ideXlab platform.