Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical dat...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book Chapter |
| Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
|
| Subjects: | |
| Online Access: | Get Fullteks DOAB: description of the publication |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis. |
|---|---|
| Physical Description: | 1 electronic resource (202 p.) |
| ISBN: | books978-3-03936-643-9 9783039366422 9783039366439 |
| Access: | Open Access |