NETWORK INTRUSION DETECTION WITH A DEEP LEARNING APPROACH

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2022-2-7

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Kültür, Ebru

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With the rapid growth of the information technology in several areas, providing security of those systems has gained more importance. As a result of this development in information technology, the complexity of cyber-attacks has also significantly increased. Therefore, traditional security tools such as Signature-based Intrusion Detection Systems (SIDS) have become insufficient for detecting new attacks. Intrusion Detection Systems (IDS) are used to monitor network traffic and capture malicious traffic. Traditional IDS are signature-based, meaning that they are capable of taking action against known threats using only predefined or custom rule sets. Traditional IDS fail to detect such attacks if unknown attacks or modified known attacks which does not correspond to the static signature occur. New behavior-based anomalous activity detection approaches, such as deep learning, can offer a solution together with signature-based IDS in order to increase the performance of detecting new types of attacks and reduce the FP (false positive) and FN (false negative) rates. Since deep learning algorithms can learn from data and patterns, it will be possible to increase the detection rate of real malicious activities by estimating which traffic is normal or attack traffic. At the same time, they are capable of automating the detection process without the need for manual configuration in order to reduce false alarms. In this thesis, we aim to investigate the efficiency of applying deep learning approaches by focusing on recurrent neural network architectures for network flow-based intrusion detection.

Subject Keywords

Cybersecurity, network flow analysis, deep learning, intrusion detection

URI

https://hdl.handle.net/11511/96310

Collections

Graduate School of Informatics, Thesis

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Citation Formats

E. Kültür, “NETWORK INTRUSION DETECTION WITH A DEEP LEARNING APPROACH,” M.S. - Master of Science, Middle East Technical University, 2022.