Study of long short-term memory in flow-based network intrusion detection system

2018 ◽  
Vol 35 (6) ◽  
pp. 5947-5957 ◽  
Author(s):  
Lee Nicholas ◽  
Shih Yin Ooi ◽  
Ying Han Pang ◽  
Seong Oun Hwang ◽  
Syh-Yuan Tan
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Short Term ◽  
Term Memory ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Long Short Term Memory

scholarly journals Detection of Cyberattacks Traces in IoT Data

2020 ◽  
Vol 26 (11) ◽  
pp. 1422-1434
Author(s):  
Vibekananda Dutta ◽  
Michał Choraś ◽  
Marek Pawlicki ◽  
Rafał Kozik
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Short Term Memory ◽  
Research Network ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Short Term ◽  
Term Memory ◽  
Network Intrusion ◽  
Long Short Term Memory

Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

scholarly journals Deep Learning Algorithmic Approach for Operational Anomaly Based Intrusion Detection System in Wireless Sensor Networks

2021 ◽  
Author(s):  
Ashwini Bhaskar Abhale ◽  
S S Manivannan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Short Term ◽  
Term Memory ◽  
Deep Convolution Neural Network ◽  
Long Short Term Memory

Abstract Because of the ever increasing number of Internet users, Internet security is becoming more essential. To identify and detect attackers, many researchers utilized data mining methods. Existing data mining techniques are unable to provide a sufficient degree of detection precision. An intrusion detection system for wireless networks is being developed to ensure data transmission security. The Network Intrusion Detection Algorithm (NIDS) uses a deep classification system to classify network connections as good or harmful. Deep Convolution Neural Network (DCNN), Deep Recurrent Neural Network (DRNN), Deep Long Short-Term Memory (DLSTM), Deep Convolution Neural Network Long Short-Term Memory (DCNN LSTM), and Deep Gated Recurrent Unit (DGRU) methods that use NSLKDD data records to train models are proposed. The experiments were carried out for a total of 1000 epochs. During the experiment, we achieved a model accuracy of more than 98 percent. We also discovered that as the number of layers in a model grows, so does the accuracy.

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
FatimaEzzahra Laghrissi ◽  
Samira Douzi ◽  
Khadija Douzi ◽  
Badr Hssina
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Principal Component ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

AbstractAn 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.

Sign in / Sign up

Export Citation Format

Share Document