Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks
This work provides a method for generating robust training data for deep learning models, which is crucial for organizations and researchers aiming to improve network attack detection.
This paper addresses the scarcity and imbalance of public network attack datasets by introducing protocol fuzzing to automatically generate high-quality, comprehensive network data. Deep learning models trained on this fuzzed data successfully detect real-world network attacks.
Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new approach, protocol fuzzing, to automatically generate high-quality network data, on which deep learning models can be trained. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.