Intrusion detection systems using long short-term memory (LSTM)

Journal of Big Data

Samira Douzi¹  Badr Hssina²  FatimaEzzahra Laghrissi²  Khadija Douzi² 

[1]FMPR, University Mohammed V, Rabat, Morocco

[2]FSTM, University Hassan II, Casablanca, Morocco

关键词: Intrusion detection systems;    Deep learning;    LSTM;    PCA;    Mutual information;   

DOI  :  10.1186/s40537-021-00448-4

来源: Springer

【 摘 要 】

An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and multiclass classification.

【 授权许可】

CC BY