Research Summaries

Back Machine Learning of Formal Specifications using Genetic Programming

Fiscal Year 2016
Division Graduate School of Operational & Information Sciences
Department Computer Science
Investigator(s) Drusinsky, Doron
Sponsor Office of Naval Research (Navy)
Summary NPS proposes machine learning of formal specification pertaining to the execution of a program written in any language. The technique works of a collection of sequences of samples such as <t=10, x=35> (meaning at time t variable x has values 35). These sequences can be easily generated using source code or lower level instrumentation of existing programs; the collection of such sequences is the learning data, or evidence. The proposed technique uses genetic programming to generate a collection First Order Logic (FOL) formal specifications that explain the above-mentioned evidence.
The technique is applicable to machine learning of cyber attacks, using sequences of identifiers (e.g., source IP address, contents type, etc.) as the above-mentioned evidence.
Keywords first order logic genetic programming
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal