An efficient botnet detection with the enhanced support vector neural network

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.