An Immunity-based Security Threat Detection Model for Digital Virtual Assets

The present digital virtual assets (DVA) security systems mainly adopt general network threat detection methods (snort-based e.g.), do not deal with the specifics of DVA, thus, they are not suitable for DVA security threat detection. This paper proposes an immune-based security threat detection mode...

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Bibliographic Details
Published inProceedings of the International Conference on Security and Management (SAM) pp. 274 - 280
Main Authors Lin, Ping, Li, Tao, Liu, Xiaojie, Zhao, Hui, Zhou, Xiaojun, Zhu, Fangdong
Format Conference Proceeding
LanguageEnglish
Published Athens The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) 01.01.2018
Subjects
Algorithms
Antigens
Bioinformatics
Blockchain
Digital currencies
Forensic sciences
Games
Hypothesis testing
Immune system
Immunity
Methods
Peer to peer computing
Privacy
Protection systems
Robustness (mathematics)
Security systems
Software
Virtual reality
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Summary:The present digital virtual assets (DVA) security systems mainly adopt general network threat detection methods (snort-based e.g.), do not deal with the specifics of DVA, thus, they are not suitable for DVA security threat detection. This paper proposes an immune-based security threat detection model for DVA. The model abstracts DVA data, operation behavior and IP package into antigens, and turns security threat discovery into to a process of classifying a set of input antigens into autologous or non-autonomous through immune detectors. Experiment proves that the model has the ability of threat-recognition and self-learning. Compared with current DVA protection systems, it supports adaptability, self-organization, robustness and self-learning, and provides a good solution to detect the security threat to DVA.