Intrusion detection system has become the fundamental part for the network security and essential for network security because of the expansion of attacks which causes many issues. This is because of the broad development of internet and access to data systems around the world. For detecting the abnormalities present in the network or system, the intrusion detection system (IDS) is used. Because of the large volume of data, the network gets expanded with false alarm rate of intrusion and detection accuracy decreased. This is one of the significant issues when the network experiences unknown attacks. The principle objective was to expand the accuracy and reduce the false alarm rate (FAR). To address the above difficulties the proposed with Crow Search Optimization algorithm with Adaptive Neuro-Fuzzy Inference System (CSO-ANFIS) is used. The ANFIS is the combination of fuzzy interference system and artificial neural network, and to enhance the performance of the ANFIS model the crow search optimization algorithm is used to optimize the ANFIS. The NSL-KDD data set was used to validate the performance of intrusion detection of the proposed model and the experiment results are compared with other existing techniques for overall performance validation. The results of the intrusion detection based on the NSL-KDD dataset was better and efficient compared with those models because the detection rate was 95.80% and the FAR result was 3.45%.

入侵检测系统已经成为网络安全的基本组成部分，并且由于攻击的扩展而引起网络安全，对网络安全至关重要。这是因为互联网的广泛发展以及世界范围内对数据系统的访问。为了检测网络或系统中存在的异常，使用入侵检测系统（IDS）。由于海量数据，网络不断扩大，入侵的误报率降低，检测精度下降。当网络遭受未知攻击时，这是重要问题之一。主要目标是提高准确性并降低误报率（FAR）。为了解决上述困难，使用了带有自适应神经模糊推理系统（CSO-ANFIS）的Crow Search Optimization算法。ANFIS是模糊干扰系统和人工神经网络的组合，为了提高ANFIS模型的性能，使用了乌鸦搜索优化算法来优化ANFIS。使用NSL-KDD数据集来验证所提出模型的入侵检测性能，并将实验结果与其他现有技术进行比较以进行整体性能验证。与之相比，基于NSL-KDD数据集的入侵检测结果更好，更有效，因为其检测率为95.80％，FAR结果为3.45％。使用NSL-KDD数据集来验证所提出模型的入侵检测性能，并将实验结果与其他现有技术进行比较以进行整体性能验证。与之相比，基于NSL-KDD数据集的入侵检测结果更好，更有效，因为其检测率为95.80％，FAR结果为3.45％。使用NSL-KDD数据集来验证所提出模型的入侵检测性能，并将实验结果与其他现有技术进行比较以进行整体性能验证。与之相比，基于NSL-KDD数据集的入侵检测结果更好，更有效，因为其检测率为95.80％，FAR结果为3.45％。