Modeling an intrusion detection using recurrent neural networks

被引：4

作者：

Ibrahim, Mariam ^{[1
]}

Elhafiz, Ruba ^{[1
]}

机构：

[1] German Jordanian Univ, Dep Mechatron Engn, Amman 11180, Jordan

来源：

JOURNAL OF ENGINEERING RESEARCH | 2023年 / 11卷 / 01期

关键词：

Recurrent Neural Networks; Intrusion Detection; Deep Learning; Cybersecurity;

D O I：

10.1016/j.jer.2023.100013

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Cybercrime is one of the most difficult features of contemporary digital technology. Hence, it is crucial to restrict and perhaps even prevent its impacts. Systems are protected against numerous harmful attacks by Intrusion Detection (ID) systems. One method for figuring out a system's typical behavior is to look at the call sequences the system processes make. In this paper, an ID system was devised based on the Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) architecture that is more accurate than conventional RNNs. The performance of the proposed ID model produced a high accuracy, detection rate, and low false alarm rate. Thus, supporting the model's reputability.

引用

页数：6

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