DDoS Attack Detection using Swarm Optimized Random Forest Classification
Keywords:
DDoS, Grey Wolf Optimization (GWO), Intruder Detection System (IDS), Radom Forest (RF), Stochastic Gradient Descent (SGD)Abstract
With the increasing spread of the Internet, the need for security also increases - both in the private and in the business sector. Corporate networks in particular are often exposed to attempted attacks. In order to avert or limit the damage, these attacks must be recognized and appropriate countermeasures initiated. This task is achieved by an Intruder Detection System (IDS). This paper presents a DDoS attack detection model using swarm optimization-based feature selection and Radom Forest (RF) classifier. A modified Grey Wolf Optimization (GWO) algorithm is used to select the features which produce the best accuracy. The fitness function of the conventional GWO algorithm is replaced with Stochastic Gradient Descent (SGD) in order to perform feature selection. The RF classifier is then trained using the chosen subset of features to identify attacks. The proposed model is tested on CICIDS2017 dataset and has been compared with existing machine learning techniques to evaluate the efficiency of the proposed model. GWO earned the highest Accuracy of 99.8. This was accomplished with only 40 out of 75 features. When GWO provided the least number of features, 38, resulting in accuracy of 99.7. Over several experiments, modified GWO with DT had an average classification accuracy of 99.5 percent.
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