Hierarchical Long Short-Term Memory Network for Cyberattack Detection

Abstract

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.