Visible to the public Biblio

Filters: Keyword is random sampling  [Clear All Filters]
2022-08-12
Kozhemyak, Olesya A., Stukach, Oleg V..  2021.  Reducing the Root-Mean-Square Error at Signal Restoration using Discrete and Random Changes in the Sampling Rate for the Compressed Sensing Problem. 2021 International Siberian Conference on Control and Communications (SIBCON). :1—3.
The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.
2017-09-06
C. Theisen, K. Herzig, B. Murphy, L. Williams.  2017.  Risk-based attack surface approximation: how much data is enough? 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP). :273-282.

Proactive security reviews and test efforts are a necessary component of the software development lifecycle. Resource limitations often preclude reviewing the entire code base. Making informed decisions on what code to review can improve a team's ability to find and remove vulnerabilities. Risk-based attack surface approximation (RASA) is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. The goal of this research is to help software development teams prioritize security efforts by the efficient development of a risk-based attack surface approximation. We explore the use of RASA using Mozilla Firefox and Microsoft Windows stack traces from crash dumps. We create RASA at the file level for Firefox, in which the 15.8% of the files that were part of the approximation contained 73.6% of the vulnerabilities seen for the product. We also explore the effect of random sampling of crashes on the approximation, as it may be impractical for organizations to store and process every crash received. We find that 10-fold random sampling of crashes at a rate of 10% resulted in 3% less vulnerabilities identified than using the entire set of stack traces for Mozilla Firefox. Sampling crashes in Windows 8.1 at a rate of 40% resulted in insignificant differences in vulnerability and file coverage as compared to a rate of 100%.