The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

2014 ◽  
Vol 599-601 ◽  
pp. 726-730 ◽  
Author(s):  
Gang Ke ◽  
Ying Han Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Detection System ◽  
Training Sample ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

The Application of Improved Genetic Neural Network in IDS for Material Detecting

2011 ◽  
Vol 321 ◽  
pp. 213-217
Author(s):  
Qing Yu ◽  
Jin Lin Wang ◽  
Xiao Chen Sui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Network Model ◽  
Bp Neural Network ◽  
Error Function ◽  
Detection Algorithm ◽  
Test Time ◽  
Training Time ◽  
Network Intrusion

The basic idea of twice genetic algorithm optimization of BP neural network model(TGB)is rough selection network model using genetic algorithm, then use BP neural network to determine the parameters which can make the error function obtained the minimum and determine its position in the parameter space, then the genetic algorithm again to solve the problem of possible local minima.Feature selection is a new formulation of dimension reduction methods. It can simplify the size of neural network and improve real-time and the accuracy of the system.The simulation results TGB-based network intrusion detection algorithm improve intrusion detection rate of samples in different degrees. It can reduce significantly training time and test time. It further demonstrates the effectiveness and feasibility of this method. The study is very useful to detect materials. So from the analysis, you can learn some skills for materials detecting.

A Wavelet Kernel-Based Support Vector Machine for Communication Network Intrusion Detection

2014 ◽  
Vol 989-994 ◽  
pp. 4474-4477
Author(s):  
Ying Zhan
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Better Than

This study is to propose a wavelet kernel-based support vector machine (SVM) for communication network intrusion detection. The common intrusion types of communication network mainly include DOS, R2L, U2R and Probing. SVM, BP neural network are used to compare with the proposed wavelet kernel-based SVM method to show the superiority of wavelet kernel-based SVM. The detection accuracy for communication network intrusion of wavelet kernel-based SVM is 96.67 %, the detection accuracy for communication network intrusion of SVM is 90.83%, and the detection accuracy for communication network intrusion of BP neural network is 86.67%.It can be seen that the detection accuracy for communication network intrusion of wavelet kernel-based SVM is better than that of SVM or BP neural network.

scholarly journals Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zengri Zeng ◽  
Wei Peng ◽  
Baokang Zhao
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Causal Intervention ◽  
Preliminary Classification

In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

The Application of BP Neural Network in Network Intrusion Detection

2013 ◽  
Vol 765-767 ◽  
pp. 1415-1418 ◽  
Author(s):  
Ya Fang Lou ◽  
Zhi Jun Yuan ◽  
Hao Wu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Practical Significance ◽  
Network Intrusion Detection ◽  
Critical Problem ◽  
Detection Model ◽  
Detection Systems ◽  
Network Intrusion ◽  
Detection Technology

As the network is impacting enormously to all aspects of society, the network security becomes a critical problem. The traditional intrusion detection technology exists some disadvantages: the imperfection of architecture, the slow detecting of system, the vulnerable of itself architecture, and so on. This paper presents an intrusion detection model based on BP neural network which has the incomparable advantages against traditional intrusion detection systems. Therefore, the study of this subject possesses the practical significance.

scholarly journals A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Omar Almomani
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

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