An improved long short term memory network for intrusion detection.

ABSTRACT: Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly o

SUBMITTER: Awad AA 

PROVIDER: S-EPMC10393181 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature