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Code Analysis for Intelligent Cyber Systems: A Data-Driven Approach

journal contribution
posted on 2020-07-01, 00:00 authored by Rory Coulter, Qing-Long Han, Lei PanLei Pan, Jun Zhang, Yang Xiang
Cyber code analysis is fundamental to malware detection and vulnerability discovery for defending cyber attacks. Traditional approaches resorting to manually defined rules are gradually replaced by automated approaches empowered by machine learning. This revolution is accelerated by big code from open source projects which support machine learning models with outstanding performance. In the context of a data-driven paradigm, this paper reviews recent analytic research on cyber code of malicious and common software by using a set of common concepts of similarity, correlation and collective indication. Sharing security goals in recognizing anomalous code that may be malicious or vulnerable. The ability to do so is not determined in isolation, rather drawn for code correlation and context awareness. This paper demonstrates a new research methodology of data driven cyber security (DDCS) and its application in cyber code analysis. The framework of the DDCS methodology consists of three components, i.e., cyber security data processing, cyber security feature engineering, and cyber security modeling. Some challenging issues are suggested to direct the future research.

History

Journal

Information Sciences

Volume

524

Season

July

Pagination

46 - 58

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0020-0255

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, Published by Elsevier Inc.

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