Intrusion detection methods based on integrated deep learning model
Intrusion detection system can effectively identify abnormal data in complex network
environments, which is an effective method to ensure computer network security. Recently …
environments, which is an effective method to ensure computer network security. Recently …
A new hybrid approach for intrusion detection using machine learning methods
Ü Çavuşoğlu - Applied Intelligence, 2019 - Springer
In this study, a hybrid and layered Intrusion Detection System (IDS) is proposed that uses a
combination of different machine learning and feature selection techniques to provide high …
combination of different machine learning and feature selection techniques to provide high …
An intrusion detection system based on a simplified residual network
Y Xiao, X Xiao - Information, 2019 - mdpi.com
Residual networks (ResNets) are prone to over-fitting for low-dimensional and small-scale
datasets. And the existing intrusion detection systems (IDSs) fail to provide better …
datasets. And the existing intrusion detection systems (IDSs) fail to provide better …
An efficient mixed attribute outlier detection method for identifying network intrusions
JR Beulah, DS Punithavathani - International Journal of Information …, 2020 - igi-global.com
Intrusion detection systems (IDS) play a vital role in protecting information systems from
intruders. Anomaly-based IDS has established its effectiveness in identifying new and …
intruders. Anomaly-based IDS has established its effectiveness in identifying new and …
Novel framework for an intrusion detection system using multiple feature selection methods based on deep learning
Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They
classify a network's incoming traffic as benign or anomalous (attack). An efficient and robust …
classify a network's incoming traffic as benign or anomalous (attack). An efficient and robust …
Deep logarithmic neural network for Internet intrusion detection
Intrusion detection is an important means to deal with network attacks. The traditional neural
network needs to preprocess the sample data in intrusion detection, which may cause the …
network needs to preprocess the sample data in intrusion detection, which may cause the …
Enhancing detection of R2L attacks by multistage clustering based outlier detection
The modern society is greatly benefited by the advancement of Internet. The contemporary
humanity is significantly profited by the Internet. The ease of access to the Internet have …
humanity is significantly profited by the Internet. The ease of access to the Internet have …
A feature reduced intrusion detection system with optimized SVM using big bang big crunch optimization
The swift proliferation in traffic across computer networks has led to certain types of attacks
and intrusions, raising a serious global concern of information security. Attack detection is …
and intrusions, raising a serious global concern of information security. Attack detection is …
An intrusion detection system based on PSO-GWO hybrid optimized support vector machine
K Li, Y Zhang, S Wang - 2021 International Joint Conference …, 2021 - ieeexplore.ieee.org
Intrusion Detection System (IDS) is an important tool to ensure network security, which can
detect and prevent malicious behavior in time. However, the noise and redundancy of data …
detect and prevent malicious behavior in time. However, the noise and redundancy of data …
A survey on intrusion detection system in ad hoc networks based on machine learning
This advanced research survey aims to perform intrusion detection and routing in ad hoc
networks in wireless MANET networks using machine learning techniques. The MANETs are …
networks in wireless MANET networks using machine learning techniques. The MANETs are …