Security

We propose a general framework for using deep learning to detect vulnerabilities, named SySeVR. For evaluate the SySeVR, we collect the Semantics-based Vulnerability Candidate (SeVC) dataset, which contains all kinds of vulnerabilities that are available from the National Vulnerability Database (NVD) and the Software Assurance Reference Dataset (SARD).

This database consists of the data used for the 2018 IEEE Signal Processing Cup.  This iteration of the Signal Processing Cup was a forensic camera model identification challenge.  Teams of undergraduate students were tasked with building a system capable of determining type of camera (manufacturer and model) that captured a digital image without relying on metadata.

Expansion of wireless body area networks (WBANs) applications such as health-care, m-banking, and others  has lead to vulnerability of privacy and personal data. An effective and unobtrusive natural method of authentication is therefore a necessity in such applications. Accelerometer-based gait recognition has become an attractive solution, however, continuous sampling of accelerometer data reduces the battery life of wearables. This paper investigates the usage of received signal strength indicator (RSSI) as a source of gait recognition.

Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" published at IEEE TIFS https://ieeexplore.ieee.org/document/8396276.