Details

Autor(en) / Beteiligte

• Koda, Satoru
• Kambara, Yusuke
• Oikawa, Takanori
• Furukawa, Kazuyoshi
• Unno, Yuki
• Murakami, Masahiko

Titel

Anomalous IP Address Detection on Traffic Logs Using Novel Word Embedding

Ist Teil von

• 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020, p.1504-1509

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This paper presents an anomalous IP address detection algorithm for network traffic logs. It is based on word embedding techniques derived from natural language processing to extract the representative features of IP addresses. However, the features extracted from vanilla word embeddings are not always compatible with machine learning-based anomaly detection algorithms. Therefore, we developed an algorithm that enables the extraction of more compatible features of IP addresses for anomaly detection than conventional methods. The proposed algorithm optimizes the objective functions of word embedding-based feature extraction and anomaly detection, simultaneously. According to the experimental results, the proposed algorithm outperformed conventional approaches; it improved the detection performance from 0.876 to 0.990 in the area under the curve criterion in a task of detecting the IP addresses of attackers from network traffic logs.