Main Article Content
In the last few years, a number of attacks and malicious activities have been attributed to common channels between users. A botnet is considered as an important carrier of malicious and undesirable briskness. In this paper, we propose a support vector machine to classify botnet activities according to k-means, k-medoids, and neural network clusters. The proposed approach is based on the features of transfer control protocol packets. System performance and accuracy are evaluated using a predefined data set. Results show the ability of the proposed approach to detect botnet activities with high accuracy and performance in a short execution time. The proposed system provides 95.7% accuracy rate with a false positive rate less than or equal to 3%.
How to Cite
Obeidat, A. A. (2022). Hybrid Approach for Botnet Detection Using K-Means and K-Medoids with Hopfield Neural Network. International Journal of Communication Networks and Information Security (IJCNIS), 9(3). https://doi.org/10.17762/ijcnis.v9i3.2514 (Original work published December 11, 2017)