Research Area:  Machine Learning

Abstract:

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

Keywords:  
DDoS attacks
traffic classification
machine learning
intelligent agent
attack detections

Author(s) Name:  Rana Abu Bakar, Xin Huang, Muhammad Saqib Javed

Publisher name:  MDPI

DOI:  10.3390/s23063333

Volume Information:  Volume 23

Paper Link:   https://www.mdpi.com/1424-8220/23/6/3333