NCSU

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Visible to the public Development of Methodology Guidelines for Security Research

Submitted by najmeri on Wed, 09/25/2019 - 3:22pm

This project seeks to aid the security research community in conducting and reporting methodologically sound science through (1) development, refinement, and use of community-based security research guidelines; and (2) characterization of the security literature based upon those guidelines.

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Visible to the public Reasoning about Accidental and Malicious Misuse via Formal Methods

Submitted by najmeri on Tue, 04/09/2019 - 4:09pm

This project seeks to aid security analysts in identifying and protecting against accidental and malicious actions by users or software through automated reasoning on unified representations of user expectations and software implementation to identify misuses sensitive to usage and machine context.

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Visible to the public Principles of Secure BootStrapping for IoT

Submitted by mpsingh on Mon, 03/19/2018 - 9:58pm

This project seeks to aid developers in designing and implementing protocols for establishing mutual trust between users, Internet of Things (IoT) devices, and their intended environment through identifying principles of secure bootstrapping, including tradeoffs among security objectives, device capabilities, and usability.

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Visible to the public Predicting the Difficulty of Compromise through How Attackers Discover Vulnerabilities

Submitted by mpsingh on Mon, 03/19/2018 - 9:57pm

The goal of this project is to aid security engineers in predicting the difficulty of system compromises through the development and evaluation of attack surface measurement techniques based upon attacker-centric vulnerability discovery processes.

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Visible to the public Coordinated Machine Learning-Based Vulnerability & Security Patching for Resilient Virtual Computing Infrastructure

Submitted by mpsingh on Mon, 03/19/2018 - 9:57pm

This research aims at aiding administrators of virtualized computing infrastructures in making services more resilient to security attacks through applying machine learning to reduce both security and functionality risks in software patching by continually monitoring patched and unpatched software to discover vulnerabilities and triggering proper security updates.

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Visible to the public NCSU SoS Lablet Summary

Submitted by Katie Dey on Thu, 07/13/2017 - 9:42am. Contributor: