Network Intrusion Detection System: Malware Detection and Analysis

This report examines network intrusion detection systems, focusing on the process of malware detection. It utilizes a data mining approach, explaining how historical and current data are collected, cleaned, and preprocessed. The report outlines the application of misuse and anomaly detection rules, detailing the decision-making process within the system. If a misuse is detected, a response is generated; otherwise, the process moves to anomaly detection. If both detection methods fail, the data is categorized as normal. The report also includes a bibliography of relevant sources.

Running head: Network Intrusion Detection System
Network Intrusion Detection System
Name of the Student
Name of the University
Author Note

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NETWORK INTRUSION DETECTION SYSTEM
Figure 1: Intrusion Detection System Using Data Mining Process
(Source: Created By Author)

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Explain the process of malware detection.
According to the above flowchart, the process of data mining is being used for the
process of malware detection. The historical data and the current data is collected from
different sources and is stored in a database. The data is cleaned and preprocessed for the
initiation of the data mining process. The rules to be used for the data mining process is then
applied to the cleaned dataset. The rules are of two types: misuse of rule and anomaly rule. In
the misuse rule, process the misuse is detected and then matched with the data set. If the
misuse satisfies then the response is received. If no match is found the data goes to the
anomaly detection process. If the data is matched then a response is generated and the cycle
ends. If no match is found in the anomaly detection means that both the process has failed
then the pointer is transferred to the audit record dataset. The pointer then goes to the data
mining rule of the flowchart and is termed as normal data.

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Bibliography
[1] R. Mitchell and I.-R. Chen, “A survey of intrusion detection in wireless network
applications,” Comput. Commun., vol. 42, pp. 1–23, 2014.
[2] O. Al-Jarrah and A. Arafat, “Network intrusion detection system using attack behavior
classification,” in 2014 5th International Conference on Information and Communication
Systems, ICICS 2014, 2014.