Network intrusion detection by artificial bee colony algorithm-based SVM

2014 ◽  
Author(s):  
Xi Zhen-yuan ◽  
Chen He ◽  
Gao Xiao-yan ◽  
Chen Lu-yi
Keyword(s):  
Intrusion Detection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Network Intrusion Detection ◽  
Bee Colony ◽  
Network Intrusion

scholarly journals Intrusion Detection for Network Based on Elite Clone Artificial Bee Colony and Back Propagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guohong Qi ◽  
Jie Zhou ◽  
Wenxian Jia ◽  
Menghan Liu ◽  
Shengnan Zhang ◽  
...  
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Artificial Bee Colony ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Intrusion Detection ◽  
Water Pipe ◽  
Network Attacks ◽  
Bee Colony ◽  
Network Intrusion

With the rapid development of Internet technology, network attacks have become more frequent and complex, and intrusion detection has also played an increasingly important role in network security. Intrusion detection is real-time and proactive, and it is an indispensable technology under the diversified trend of network security issues. In terms of network security, neural networks have the characteristics of self-learning, self-adaptation, and parallel computing, which are very important in intrusion detection. This paper combines back propagation neural network (BPNN) and elite clone artificial bee colony (ECABC) to propose a new ECABC-BPNN, which updates and optimizes the settings of traditional BPNN weights and thresholds. Then, apply ECABC-BPNN to network intrusion detection. Use the attack data samples of KDD CUP 99 and water pipe for attack classification experiments using GA-BPNN, PSO-BPNN, and ECABC-BPNN. The results show that the ECABC-BPNN proposed in this paper has an accuracy rate of 98.08% on KDD 99 and 99.76% on water pipe data. ECABC-BPNN effectively improves the accuracy of network intrusion classification and reduces classification errors. In addition, the time complexity of using ECABC-BPNN to classify network attacks is relatively low. Therefore, ECABC-BPNN has superior performance in network intrusion detection and classification.

scholarly journals Design of intrusion detection system based on improved ABC_elite and BP neural networks

2019 ◽  
Vol 16 (3) ◽  
pp. 773-795
Author(s):  
Letian Duan ◽  
Dezhi Han ◽  
Qiuting Tian
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Detection System ◽  
Local Optimum ◽  
Bp Neural Networks ◽  
Guided Search ◽  
Bee Colony

Intrusion detection is a hot topic in network security. This paper proposes an intrusion detection method based on improved artificial bee colony algorithm with elite-guided search equations (IABC elite) and Backprogation (BP) neural net works. The IABC elite algorithm is based on the depth first search framework and the elite-guided search equations, which enhance the exploitation ability of artificial bee colony algorithm and accelerate the convergence. The IABC elite algorithm is used to optimize the initial weight and threshold value of the BP neural networks, avoiding the BP neural networks falling into a local optimum during the training process and improving the training speed. In this paper, the BP neural networks optimized by IABC elite algorithm is applied to intrusion detection. The simulation on the NSL-KDD dataset shows that the intrusion detection system based on the IABC elite algorithm and the BP neural networks has good classification and high intrusion detection ability.

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