WEDL-NIDS: Improving Network Intrusion Detection Using Word Embedding-Based Deep Learning Method

A Network Intrusion Detection System (NIDS) helps system administrators to detect security breaches in their organization. Current research focus on machine learning based network intrusion detection method. However, as numerous complicated attack types have growingly appeared and evolved in recent years, obtaining high detection rates is increasingly difficult. Also, the performance of a NIDS is highly dependent on feature design, while a feature set that can accurately characterize network traffic is still manually designed and usually costs lots of time. In this paper, we propose an improved NIDS using word embedding-based deep learning (WEDL-NIDS), which has the ability of dimension reduction and learning features from data with sophisticated structure. The experimental results show that the proposed method outperforms previous methods in terms of accuracy and false alarm rate, which successfully demonstrates its effectiveness in both dimension reduction and practical detection ability.