Early Malware Detection through Temporal Analysis of System Behaviors

Abstract

Early detection of malware is crucial for minimizing potential damage to computer systems and sensitive data. This paper investigates the application of temporal analysis techniques for identifying malicious software during early stages of infection. We focus on analyzing time-series patterns in system behaviors, including process activities, file operations, and network connections. The study examines how temporal features can reveal malicious intent before significant harm occurs. We employ sliding window analysis and sequence pattern mining to extract relevant temporal characteristics from system event logs. The research compares the effectiveness of different time window sizes and evaluates both rule-based and machine learning approaches for temporal anomaly detection. We also investigate behavioral differences across various malware lifecycle stages, from initial execution through propagation. Our experimental analysis demonstrates that temporal features can provide valuable signals for early detection. This work offers security practitioners a complementary detection method that focuses on behavioral sequences rather than static signatures, potentially improving detection rates for previously unknown malware variants while maintaining acceptable performance overhead in production environments.