Back to Search
Start Over
Code analysis for intelligent cyber systems: A data-driven approach
- Source :
- Information Sciences. 524:46-58
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- 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.
- Subjects :
- Feature engineering
Information Systems and Management
Computer science
Static program analysis
Context (language use)
02 engineering and technology
Computer security
computer.software_genre
Theoretical Computer Science
Data-driven
Software
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Code (cryptography)
Context awareness
business.industry
05 social sciences
050301 education
Computer Science Applications
Control and Systems Engineering
Malware
020201 artificial intelligence & image processing
business
0503 education
computer
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 524
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........2e6c991fbf483ff2894448908fd3fd97