To address the challenge of effectively preventing internal attacks in big data environments,a deep learning-based user and entity behavior analysis(UEBA)solution is proposed,building on in-depth research of user and entity behavior analysis techniques.The solution involves conducting ex-perimental analysis using relevant datasets.Firstly,UEBA technology is utilized to establish a baseline of normal activities for employees and system devices,creating user behavior pattern profiles.Sec-ondly,a multi-network model architecture based on deep learning is implemented to accurately detect internal threats such as sensitive data theft,account misuse,and anomalous access requests targeting Web service API.The experimental results indicate that the multi-layer perceptron within a single net-work model achieves the highest accuracy,followed by recurrent neural networks,while the radial ba-sis function network performs relatively poorly.Furthermore,the accuracy of the multi-network model,which combines three neural network models,shows a significant improvement over single network models,with a lower false positive rate,making it practically significant for real-world applications.
internal attacksdeep learninguser and entity behavior analysisuser behavior pattern profilingmulti-network model
维普
万方数据
