A Lightweight and Accurate Classification Framework for Traffic Log Analysis Based on an Effective Feature Representation Method

As cyberattacks become increasingly sophisticated, organizations face an urgent need for timely and accurate incident response to reduce their impact on critical systems. Automating the analysis of network traffic logs has become essential for supporting security analysts and specialists. Although many previous studies have applied machine learning to address this task, they often encounter challenges such as dependence on large-scale analytics platforms, limited exploration of machine learning algorithms, and difficulties in deploying distributed systems due to high costs, complexity, and privacy concerns. To tackle these limitations, we propose a lightweight and accurate machine learning-based framework for the automatic analysis of network traffic logs. Our approach transforms log data into feature vectors using a document-based feature representation method. Experimental results on benchmark datasets demonstrate that our method enables efficient and effective traffic log analysis suitable for practical deployment.