E2IDS: An Enhanced Intelligent Intrusion Detection System Based On Decision Tree Algorithm

Abstract

Due to the increased usage of the Internet of Things and heterogeneous distributed devices, the development of effective and reliable intrusion detection systems (IDS) has become more critical. The massive volume of data with various dimensions and security features, on the other hand, can influence detection accuracy and raise the computation complexity of these systems. Fortunately, Artificial Intelligence (AI) has recently attracted a lot of attention, and it is now a principal component of these systems. This work presents an enhanced intelligent intrusion detection model (E2IDS) to detect state of the art known cyberattacks. The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. Furthermore, accuracy, recall and F-score are selected as the performance evaluation metrics. The experimental results show that our E2IDS not only overcomes the benchmark work but also reduces the complexity of the computing process.