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A Hybrid Real-Time Intrusion Detection System for an Internet of Things Environment with Signature and Anomaly Based Intrusion detection

Hasan, Maaz (2019) A Hybrid Real-Time Intrusion Detection System for an Internet of Things Environment with Signature and Anomaly Based Intrusion detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

Intrusion detection systems play important role in real world applications. Every organization or government that uses any sort of networking and information systems need protection from various kinds of intrusions. Many existing intrusion detection systems provide very highly verbose output and it is not easier for administrators to identify the issues immediately. With the Artificial Intelligence (AI) techniques with underlying Machine Learning (ML) algorithms, there is scope of developing IDS based on AI. In this project, a hybrid IDS is developed using machine learning approaches. It combines Random Forest classification and K-Means clustering. This will use both misuse detection and anomaly detection for improving performance of the IDS. These algorithms are evaluated for the four categories of attacks based on precision, recall, F1-score, false-alarm-rate, and detection-rate. The proposed IDS is evaluated with NSL-KDD dataset which is highly optimized for intrusion detection research. The results of experiments showed that the hybrid IDS perform well in terms of detection rate and other metrics.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 02 Apr 2020 13:46
Last Modified: 02 Apr 2020 13:46
URI: http://norma.ncirl.ie/id/eprint/4163

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