The Design of Improved Elman Network Intrusion Detection Algorithm in Digital Campus Network

2014 ◽  
Vol 1049-1050 ◽  
pp. 2096-2099
Author(s):  
Jing Jing Wang
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Scale Up ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Campus Network ◽  
Potential Threat ◽  
Digital Campus ◽  
Elman Neural Network ◽  
Network Intrusion

With the development of digital campus network users, web users exhibit scale up, the campus network users to use different computer level each are not identical, uneven, a potential threat to network is more serious, campus network security has become an urgent need to solve the problem. In this paper, based on the neural network, the concept of Elman memory, and proposed an improved algorithm of Elman neural network, the realization of network intrusion detection. The experimental results show that, the algorithm can effectively improve the accuracy of network intrusion detection algorithm.

Research on the Network Intrusion Detection Method Introduced with the View of Quantum Optimization

2013 ◽  
Vol 380-384 ◽  
pp. 2687-2690
Author(s):  
Xiao Jin Zhao
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Principal Component ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Operating Data ◽  
Quantum Neural Network ◽  
Network Intrusion ◽  
Quantum Optimization

In the process of network intrusion detection, the network operating data need to be counted. Then, the network intrusion detection can be performed through comparing the values of the statistical results with the threshold values of network intrusion detection sequentially. However, too large network operating data will cause the overlapping of operating data during the detection, reducing the accuracy of the network intrusion detection. In order to avoid the defect mentioned above, a large data network intrusion detection algorithm introduced with quantum optimization neural network is proposed. Through the analysis of the principal component of the data, the process of the massive network operating data can be simplified. Using the quantum neural network method, the initial threshold of network intrusion feature can be achieved, so as to provide accurate data base for the network intrusion detection. Taking the advantage of small distance parade of genetic algorithms, the threshold characteristic is optimized and the mass redundancy interference characteristic is overcome, so as to fulfill the network intrusion detection. Experimental results show that the proposed algorithm used for network intrusion detection can improve the accuracy of detection effectively and achieve satisfactory results.

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.

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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