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.

中文翻译：

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基于精英克隆人工蜂群和反向传播神经网络的网络入侵检测

随着互联网技术的飞速发展，网络攻击变得更加频繁和复杂，入侵检测在网络安全中也发挥着越来越重要的作用。入侵检测具有实时性和主动性，是网络安全问题多元化趋势下不可或缺的技术。在网络安全方面，神经网络具有自学习、自适应、并行计算等特点，在入侵检测中非常重要。本文结合反向传播神经网络（BPNN）和精英克隆人工蜂群（ECABC）提出了一种新的ECABC-BPNN，它更新和优化了传统BPNN权重和阈值的设置。然后，将 ECABC-BPNN 应用于网络入侵检测。使用KDD CUP 99和水管的攻击数据样本进行GA-BPNN、PSO-BPNN、ECABC-BPNN的攻击分类实验。结果表明，本文提出的ECABC-BPNN在KDD 99上的准确率为98.08%，在水管数据上的准确率为99.76%。ECABC-BPNN 有效提高了网络入侵分类的准确率，减少了分类错误。另外，使用ECABC-BPNN对网络攻击进行分类的时间复杂度相对较低。因此，ECABC-BPNN 在网络入侵检测和分类方面具有优越的性能。ECABC-BPNN 有效提高了网络入侵分类的准确率，减少了分类错误。另外，使用ECABC-BPNN对网络攻击进行分类的时间复杂度相对较低。因此，ECABC-BPNN 在网络入侵检测和分类方面具有优越的性能。ECABC-BPNN 有效提高了网络入侵分类的准确率，减少了分类错误。另外，使用ECABC-BPNN对网络攻击进行分类的时间复杂度相对较低。因此，ECABC-BPNN 在网络入侵检测和分类方面具有优越的性能。