Hierarchical Long Short-Term Memory Network for Cyberattack Detection
Haixia Hou,
Yingying Xu,
Menghan Chen,
Zhi Liu,
Wei Guo,
Mingcheng Gao,
Yang Xin,
Lizhen Cui
Affiliations
Haixia Hou
State Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, China
Joint SDU-NTU Center for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
Mingcheng Gao
State Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, China
State Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, China
With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate.
