Visible to the public Insider Threat Detection Based on Adaptive Optimization DBN by Grid Search

TitleInsider Threat Detection Based on Adaptive Optimization DBN by Grid Search
Publication TypeConference Paper
Year of Publication2019
AuthorsZhang, Jiange, Chen, Yue, Yang, Kuiwu, Zhao, Jian, Yan, Xincheng
Conference Name2019 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date Publishedjul
KeywordsAdaptation models, adaptive optimization, adaptive optimization DBN, Adaptive systems, belief networks, Collaboration, composability, deep belief net, Deep Learning, deep learning model, grid search, Human Behavior, insider threat, insider threat detection method, learning (artificial intelligence), learning rate, Metrics, network structure, one-dimensional parameter optimization, Optimization methods, optimization parameters, policy-based governance, pubcrawl, resilience, Resiliency, search problems, security of data, threat detection rate, Training, two-dimensional grid

Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.

Citation Keyzhang_insider_2019