A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Abstract

Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

Investigating optimal features in log files for anomaly detection using optimization approach

“…As a result, system logs have become a key data resource for performance analysis and anomaly identification, as well as a prominent topic of researches in the area. Identifying anomalies is a significant problem that has been explored for decades [2]. To identify abnormalities in various applications such as intrusion detection, system log analysis, Realtime processing of bigdata, fraud detection, medical monitoring application, outlier detection, aviation safety study and several diverse approaches have been developed and employed.…”

Design and Implementation of a Rural Social Security System Based on Deep Learning

“…In 2006, Zhong et al published an article on deep neural networks in Science and proposed the idea of deep learning algorithms, which promoted the development of artificial neural networks and the application of deep learning methods in the field of image recognition. However, their research focuses on image recognition rather than optimization algorithms, and the proposed algorithm is still not easy to understand and not easy to operate [3].…”