SVM Based DDoS Attack Detection in IoT Using Iot-23 Botnet Dataset

Abstract

Network security is one of the most important challenges in Smart cities networks. A distributed denial of service (DDoS) attack is a cyberattack that attempts to distribute the normal traffic of a targeted server, service or network sending flood of Internet traffic. In this paper, Machine learning algorithms such as Support Vector Machine, Decision Tree and Random Forest are trained and tested to classify the DDoS attacked packets. Principal Component Analysis (PCA) is a dimensionality reduction technique that helps to improve the performance of the algorithms. So, in this paper, the efficiency of the PCA is compared with the without PCA results. Initially, the dataset is passed to PCA (Principal Component Analysis) for feature selection and then implemented in different Machine Learning algorithms. The results of all the algorithms are evaluated using the parameters accuracy, precision, F1 score, and Recall. Again, the same dataset is tested and trained in all same ML algorithms using without PCA dataset. Then the results of PCA and without PCA are compared. This experimental analysis shows that by using PCA, the execution time of the algorithm reduces significantly with a smaller number of features and yields same result as that of without PCA. More over the Decision tree and Random Forest algorithms classifies the DDoS packets very accurately compared to SVM. The results are presented as graph using Matplotlib. The dataset taken for our experimental analysis is IoT-23 dataset.