An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs.

The smartphone has become an indispensable tool in our daily lives, and the Android operating system is widely installed on our smartphones. This makes Android smartphones a prime target for malware. In order to address threats posed by malware, many researchers have proposed different malware detection approaches, including using a function call graph (FCG).

CLDTLog: System Log Anomaly Detection Method Based on Contrastive Learning and Dual Objective Tasks.

System logs are a crucial component of system maintainability, as they record the status of the system and essential events for troubleshooting and maintenance when necessary. Therefore, anomaly detection of system logs is crucial. Recent research has focused on extracting semantic information from unstructured log messages for log anomaly detection tasks.

ConAnomaly: Content-Based Anomaly Detection for System Logs.

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event templates and then utilize machine learning methods to detect anomalies.

A Hybrid Analysis-Based Approach to Android Malware Family Classification.

With the popularity of Android, malware detection and family classification have also become a research focus. Many excellent methods have been proposed by previous authors, but static and dynamic analyses inevitably require complex processes. A hybrid analysis method for detecting Android malware and classifying malware families is presented in this paper, and is partially optimized for multiple-feature data.