I. Introduction
The birth of computing gave rise to the problem of threats, malware and security issues. In the past few years, growth on the internet has created more awareness towards security. A major concern is an attack on our systems by hackers. One such important attack is DDoS, which is most severe due to its impact [1]. The impact of this attack on IoT devices cannot be ignored with the rise in innovations (Cloud Computing, Fog Computing). In cybersecurity, DDoS attacks are the most prominent that stop internet traffic towards the server. In this paper, the developments and breaches in groundworks for the progress of security-efficient algorithms have been discussed [2] [3]. Also, DDoS attacks in countless network backgrounds have been examined. Many Intrusion detection tools have come into existence for the detection of these types of attacks [4] [5]. The first IDS (Intrusion Detection System) came in the year 1980, and many other systems were further introduced [6] [7]. Their main aim is to generate alerts for threatening situations and consequently generating false alarm rates. A lot of research has been done in reducing false alarms and generating high detection rates which led to the initiation of new fields. These fields have become subjects of extensive research and study thereby aiming to propose new systems for tackling zero-day attacks [4] [5]. The primary goal of this paper is to assess DDoS datasets and analyze their performance based upon varying network traffic. The important contributions of our paper are as follows: (1) Analyze the current state of existing intrusion detection datasets, including characteristics and shortcomings. (2) Collect and process open DDoS datasets from reliable sources and review them. (3) Select the most suitable machine learning algorithms to assess the dataset and build appropriate training models by labeling training instances according to the type of network traffic, malicious or benign. (4) Train, validate and test each dataset against the machine learning algorithms and generate results for each. (5) Evaluate the results of the supervised learning models using a set of performance metrics. (6) Analyze the intrusion detection performance of machine learning classifiers based on the achieved results. The remainder paper is structured as follows. Section 2 presents the literature survey. The dataset to be used and its preprocessing have been discussed in Section 3. Results have been discussed in Section 4. Section 5 represents the conclusion and future scope.