Network Intrusion Detection System Report and Analysis for Security

This report provides an overview of Network Intrusion Detection Systems (IDS). It discusses the role of IDS in network security, highlighting the use of algorithms like decision trees, one-class support vector machines, and hybrid intrusion detection methods. The report explains the entropy calculation used in decision trees and the SVM algorithm. It also mentions the use of MATLAB for algorithm testing and the importance of multi-core parallel systems. The report includes a flowchart and block diagram illustrating the IDS process and references relevant research. This report is designed to help understand the principles and applications of intrusion detection techniques for maintaining network security and protecting against various cyber threats.

Running head: NETWORK INTRUSION
Network Intrusion
Name of the Student
Name of the University
Author’s Note

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Literature review
The network intrusion detection system (IDE) is a system that helps in detecting various
attacks in the network system. IDS can be placed on any network that helps in collecting data
and information for providing security to the networks. Algorithms are required in order to
perform detection of attacks in a network system. The Next Generation Intrusion Detection
Expert System (NIDES) has helped in maintaining the security of the network system by the use
of the analytical and statistical model. The use of IDS has helped in maintaining the security of
the data and information in the network server of the company [1].
This method comprises with detection rate of both known and unknown attacks is
enhanced. There are some proposed intrusion detection algorithms including decision tree, one-
class support vector machine and hybrid intrusion detection method. Decision tree method works
on divided and conquers method with a recursive procedure. The primary application of the
Decision tree algorithms are for providing data and divide into corresponding classes.
Where Gain(S, A) is gain of set S after a split over the A attribute; Entropy(S) is the
information entropy of set S.
The entropy is as follows:
A one-class support vector machine has been used by a various organization in order to
protect from network attacks. The algorithm of this system is as follows:

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Where w is vector orthogonal to hyperplane, n = [n1,.. nl] is vector of slack variables
used to penalize rejected instances, and q represent margin, i.e. distance of the hyperplane from
origin.
The detection rate of hybrid detection methods can be more than 99% without
considering performance of first-class SVM model. Next step is testing step of algorithms using
the MATLAB. The training time of proposed algorithms is measured to analyze the time
complexity. Therefore, the use of these algorithms might help in maintaining the security of the
network system. Different viruses and malware are detected with the helped of the network
intrusion detection system in the network system. The use of the multi-core parallel system has
helped in creating a favourable condition for computing systems and its networks.

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Figure 1: Flow Chart of Network intrusion detection system
(Source: Created by author)

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Figure 2: Block Diagram of network IDS
(Source: Created by Author)

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References
[1] Kim, Gisung, Seungmin Lee, and Sehun Kim. "A novel hybrid intrusion detection method
integrating anomaly detection with misuse detection." Expert Systems with Applications 41, no.
4 (2014): 1690-1700.