Network Intrusion Detection Using Supervised Machine Learning Technique

Abstract

In the modern computer world, use of the
internet is increasing day by day. However, the
increasing use of the internet creates some security
issues. These days, such new type of security attacks
occurs every day and it is not easy to detect and
prevent those attacks effectively. One common
method of attack involves sending large amount of
request to site or server and server will be unable to
handle such huge requests and site will be offline for
many days. This type of attack is called distributed
denial of service (DDOS) attack, which act as a
major security threat to internet services and most
critical attack for cyber security world. Detection and
prevention of Distributed Denial of Service Attack
(DDoS) becomes a crucial process for the
commercial organizations that uses the internet.
Different approaches have been adapted to process
traffic information collected by monitoring stations
(Routers and Servers) to distinguish malicious traffic
such as DDoS attack from normal traffic in Intrusion
Detection Systems (IDS). In general, Machine
learning techniques can be designed and
implemented with the intrusion systems to protect the
organizations from malicious traffic. Specifically,
supervised clustering techniques allow to effectively
distinguishing the normal traffic from malicious
traffic with good accuracy. In this paper, machine
learning algorithms are used to detect DDoS attacks
collected from “KDD cup 99 Dataset”, preprocessing
and feature selection technique is used on
the dataset to enhance the performance of the
classifiers and reduce the detection time. The
classification algorithms such as C4.5 decision tree
and Navie Bayes is applied on the training dataset
and the implementation of the algorithm is done
using spyder tool. The performance comparison of
algorithms is shown using confusion matrix and it is
found that C4.5 decision is more efficient in detection
of DDOS attack .The proposed method can be used
as DDoS defense system.