In this report the use of Machine Learning (ML) techniques in Software Vulnerability Research (SVR) is investigated, discussing previous and current efforts to illustrate how ML is utilised by academia and industry in this area.
Our scientific and technical publications are an important vehicle for the dissemination of our work.
We have several goals for our publications:
- To communicate the results of the research program to clients and fulfil our reporting responsibilities to the Department of Defence.
- To record the results of our research program.
- To communicate the results of scientific research and technical information to Australian industry and academia.
- To increase awareness of and enhance the reputation of DST Group.
Latest scientific publications
This technical note considers processes that alternate randomly between 'working' and 'broken' over an interval of time.
This report describes the key enhacements made, on recommendation by DST Group, to the C-27J aircraft model.
This report presents the findings of an azimuth elevation survey to determine the minimum safe elevations for antenna pointing and signal transmission.
The purpose of this report is to review colour measurement data for its usefulness in establishing performance criteria for CRP food components.
This technical note is a review of current open literature regarding covert timing channels over network traffic.
This paper explores the definition of non-traditional sensing (NTS), some of the technologies that are typically associated with NTS and, ultimately, questions the need for NTS as a concept.
This report provides a review of studies that have examined how the transparency of an autonomous system affects key variables such as operator performance, response time, situation awareness, perceived usability, and subjective workload.
This report explores how data (from sensors) can be exploited into actions (for shooters) over different paths, and sensors connected to shooters just-in-time vs just-in-case.