As the botnet makes the way for many illegal activities, it is considered as the most critical threats to cybersecurity. Although many detection models have been presented by the researchers, they couldn’t detect the botnets in an early stage. So to overcome this issue, an enhanced support vector neural network (ESVNN) is presented as the detection model in this paper. For enhancing the classification accuracy, the suitable features of traffic flows are selected from the dataset. By observing the constant response packets, the features such as response packet ratio of the bot, length of the initial packet, packet ratio and small packets are extracted. These extracted features are used as input features for the proposed ESVNN classifier or prediction model. In ESVNN, Artificial Flora (AF) algorithm is presented for enhancing the performance of SVNN. The AF is an intelligent algorithm which is inspired from the reproduction and the migration characteristics of flora. Simulation results depict thatthe novel botnet detection model achieves better accuracy and F-measure than the existing prediction models. The presented model has reached to a higher precision of 0.8709, recall of 0.8636, accuracy of 0.8684, and F-score of 0.8669.

僵尸网络为许多非法活动铺平了道路，被认为是对网络安全的最严重威胁。尽管研究人员提出了许多检测模型，但它们无法在早期阶段检测到僵尸网络。因此，为了克服这个问题，本文提出了一种增强的支持向量神经网络（ESVNN）作为检测模型。为了提高分类准确性，从数据集中选择交通流的合适特征。通过观察恒定的响应报文，可以提取出僵尸程序的响应报文比率，初始报文长度，报文比率和小报文等特征。这些提取的特征用作建议的ESVNN分类器或预测模型的输入特征。在ESVNN中，提出了人工植物区系（AF）算法来增强SVNN的性能。AF是一种智能算法，其灵感来自植物的繁殖和迁移特性。仿真结果表明，新型僵尸网络检测模型比现有的预测模型具有更高的准确性和F-measure。提出的模型已达到0.8709的更高精度，0.8636的召回率，0.8684的精度以及0.8669的F分数。