Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
Date of Award
Doctor of Philosophy (PhD)
Michele C. Weigle
Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). This work also includes two prevention mechanisms for DoS attacks, namely a one- time password (OTP) and through the use of machine learning. Furthermore, we focus on an exploit of the widely used IEEE 802.11 protocol for wireless local area networks (WLANs). The work proposed here presents a threefold approach for intrusion detection to remedy the effects of malware and an Internet protocol exploit employing machine learning as a primary tool. We conclude with a comparison of dimensionality reduction methods to a deep learning classifier to demonstrate the effectiveness of these methods without compromising the accuracy of classification.
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AL-Maksousy, Hassan H..
"Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems"
(2018). Doctor of Philosophy (PhD), Dissertation, Computer Science, Old Dominion University, DOI: 10.25777/8w8w-sa92
Digital Communications and Networking Commons, Information Security Commons, Theory and Algorithms Commons