Abstract:
Cyber security has become a great issue in this technological world. There are several
types of cyber-attacks that are present, where Distributed Denial-of-Service (DDoS) is one
ofthe most common attack type in the cyber world. Researchers are doing their best to find
a solution to get rid of DDoS attacks. With the advancement of technology day by day,
millions of people across the world are relying on the internet. People are using internet in
every field of life from the very basic home task to the academics
ofusers are increasing day by day, security issues are also increasing. DDoS has grown
significantly than normal. DDoS attacks frequency is doubled in every year but due to
COVID-19 pandemic, as everything is shifted on internet.
To identify and to take measures against DDoS attacks has become a necessary task. There
is a need to make a system intelligent enough to detect the difference between the legitimate
request and DDoS attack request. Blocking the traffic is not a solution. It is important to
develop a technique which is intelligent enough to distinguish the normal and malicious
traffic.
research. As the number
more
There are many solutions available up till now. Researchers are using different techniques to
get rid ofthis problem. In this research, three different approaches are used to check which
one is better for cyber security dataset. The dataset used is CICDDoS 2019 comprises of
different DDoS attack types. The first approach is Machine Learning approach in which
Random Forest algorithm are used. Second approach consists of ANN (Artificial Neural
Network) and CNN (Convolutional Neural Network). The performance of CNN and RF is
almost same. Accuracy obtained by using of all the three approaches are better. In some of
the attack classification, the accuracy is increased up to 99.9%. Whereas ANN algorithm has
an average performance for cybersecurity dataset. There are many anomalies occurred in the
performance ofANN.
The performance parameters include Accuracy, Training Time, Testing Time and Confusion
Matrix. CNN takes more time in training than RF but there is a very less chance of any
