Container Anomaly Detection Based on Attention Mechanism and Multi-scale Convolutional Neural Network
Containers are widely used in cloud computing due to their lightweight,flexibility,and ease of deployment,making them an indispensable technology.However,they also face security concerns due to their shared kernel and weaker resource isolation compared to virtual machines.Based on attention mechanism and convolutional neural network,this paper proposes a method of process anomaly detection in container based on system call sequence,which uses the data generated by container process operation to analyze and judge the abnormal behavior of process.The experimental results on public datasets and simulated attack scenarios show that this method can detect anomalies in the behavior of processes within containers,and is higher in accuracy and precision than comparison methods such as random forest and LSTM.
system callcontaineranomaly detectiondeep learningattention mechanism
万方数据
维普
