scholarly journals A Robust and Efficient Real Time Network Intrusion Detection System Using Artificial Neural Network In Data Mining

2011 ◽  
Vol 1 (4) ◽  
pp. 10-19 ◽  
Author(s):  
Renuka Devi Thanasekaran
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Artificial Neural

Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System

2018 ◽  
Vol 27 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Rafath Samrin ◽  
Devara Vasumathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Similarity Measures ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Benchmark Database ◽  
Artificial Neural

AbstractDespite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.

Hacker Intrusion Detection System Based on Artificial Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 2924-2928
Author(s):  
Jing Huang ◽  
Hai Bin Chen ◽  
Jiang Zhang ◽  
Han Bo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
System Model ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Network Intrusion ◽  
Artificial Neural

In this paper, some scholars’ idea of applying neural network technology in the design of hacker intrusion detection system model and making a hacker intrusion detection system model based on artificial neural network is adopted. This study selects KDDCup’99 for network intrusion detection data set to learn the characteristics of the intrusion accurately; completes the normalization of all characteristics to achieve rapid convergence of the artificial neural network; analyses the advantages and disadvantages of different neural network training functions; achieves a high accuracy rate for intrusion detection successfully.

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.

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