An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks

Abstract

The number of devices connected to the Internet is increasing day by day. This increase causes cyber-attacks to be larger and more complex. It is important to sdetect the anomalies rapidly when there is a cyber-attack. In detecting anomalies, high false positive rate is obtained by using feature extraction based on statistical calculations and machine learning algorithms. In proposed approach, the measured values obtained from the network are normalized between 0 and 1. These values applied to autoencoder model trained with optimum hyper parameters. This model contributes to feature learning and dimensional reduction. Support vector machines effectively differentiate between normal and DDOS attack traffic by using these features. The CICIDS dataset and virtually generated DDOS traffic are used to validate the proposed approach and measure its performance. The results show that the proposed approach speeds up training and testing times and performs better classification performance metrics than most previous approaches. The novelty of the study is that AE-SVM trained with CICIDS successfully captures virtually generated DDOS traffic data. Despite the unbalanced data set, 99.1% test success was achieved in detection of DDOS traffic which is produced with Kali Linux. This success contributed to the solution of the high false-positive problem compared to other models.