An Enhanced Deep Autoencoder-based Approach for DDoS Attack Detection
Samar Sindian, Samer Sindian
Abstract
Intrusion detection systems play a crucial role in preventing security threats and defending networks from attacks. Among the attacks, distributed Denial-of-Service (DDoS) attacks literally get into the network and, in addition, they are terribly troublesome to avoid. With the advent of unknown threats, traditional machine learning approaches are impacted by lower detection rates and higher false-positive rates. As a result, the DDoS detection system requires an over-performing machine learning classifier with minimal false-positive and high detection accuracy. In this context, we propose an Improved Deep Sparse Autoencoder-based Framework (EDSA) for DDoS Attack Detection with a cost minimization strategy. The sparse autoencoder is used for dataset extraction functionality, while the softmax layer is used for traffic classification as malicious or bengin. However, intrusion detection includes the risk elements of inaccurate prediction; hence, we have used research metrics such as accuracy, precision, detection rate and specificity for our model analysis. The proposed solution uses the CICDDoS 2019 datasets and demonstrates high detection accuracy with a much less false positives percentage.