Developing Realistic Distributed Denial of Service (DDoS) Dataset for Machine Learning-based Intrusion Detection System

Abstract

During the last decade, attackers have compromised reputable systems to launch massive Distributed Denial of Services (DDoS) attacks against banking services, corporate websites, and e-commerce business. Such attacks cause enormous reputation and financial losses which ruined their services to authorized users. Conventionally, diverse solutions have been proposed to combat emerging DDoS attacks. However, there is no ideal solution available to-date. To validate majority of the existing solutions, researchers have considered simulation based experiments that become obsolete. Now a days, the trend has shifted to publicly available realistic datasets for DDoS validation purpose. Thus, in this research study, we have provided a comprehensive review of currently available datasets and proposed a novel taxonomy for classification of DDoS attacks. Further, we generated a new dataset called "CRCDDoS2022", which can overcome all existing shortcomings. Moreover, a novel generated dataset "CRCDDoS2022" can overcome drawback. Moreover, with this new dataset, a new attack (Malware) family classification and detection approach is also provided which based on the set of features in network flow. Lastly, this research provided the most significant feature sets for the detection of DDoS attacks of various types along with their corresponding weights.