Master Thesis: “Intrusion detection system with deep packet inspection”
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Date
2024
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Faculty of Engineering and Natural Science
Abstract
The rapid expansion of the internet has revolutionized modern life, yet it also poses significant risks due to vulnerabilities in increasingly complex software and networks. Addressing these risks requires robust methods for preventing attacks. Firewalls serve as critical security tools in network protection, distinguishing between secure and less secure networks and regulating information flow. However, they alone cannot fully safeguard against malicious activities. Intrusion Detection Systems (IDS), particularly those incorporating Deep Packet Inspection (DPI), have emerged as effective means of identifying and mitigating network threats. The research aims to evaluate the effectiveness of DPI algorithms in detecting and responding to network intrusions. By reviewing existing literature and conducting experiments, the study seeks to identify the most efficient algorithms based on speed and accuracy metrics. The research objectives include proposing algorithm designs to enhance IDS performance and developing an intrusion detection system algorithm. The methodology involves studying various DPI algorithms, normalizing datasets, and comparing algorithm performance through experimentation. Preliminary results from algorithms like K-Nearest demonstrate ongoing progress in algorithm development. While the project faces challenges such as research environment limitations, the commitment to understanding the subject thoroughly remains unwavering. By leveraging Linux operating systems and diligent research, the study aims to contribute valuable insights into intrusion detection system effectiveness, potentially leading to the development of more efficient algorithms
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Nauruzbayeva F / Master Thesis: “Intrusion detection system with deep packet inspection” / 2024 / Computer Science - 7M06102