A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning

摘要: Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-physical system over last decade. Defending against DDoS is not only challenging but also strategic. Tons new strategies and approaches have proposed to defend different types attacks. The ongoing battle between attackers defenders full-fledged due its newest techniques. Machine learning (ML) promising outcomes research fields including cybersecurity. In this paper, ensemble unsupervised ML approach used implement an intrusion detection which noteworthy accuracy detect goal increase while decreasing false positive rate. NSL-KDD dataset twelve feature sets from existing are for experimentation compare our results with those individual other models.