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Description
The primary focus of our research is to evaluate the effectiveness of converting network traffic data, PCAPs, into image-based representations for anomaly-based network intrusion detection. We aim to analyze PCAPs to detect malware, or malicious software in hopes of creating a useful approach for anomaly detection against cyber threats including Advanced Persistent Threats (APTs). With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology, by protecting and defending against threat agents. Our research will apply novel machine learning (ML) techniques to detect potential malware transmitted over a network effectively. The overall approach involves evaluating conversion of packet-based data into image form and deriving features that can be used to train traditional classifiers such as Random Forest, Decision Trees, and others. We are interested in whether these methods are as effective as other methods such as Deep Learning models and Convolutional Neural Networks (CNN). Our methodology will involve selection of appropriate datasets in PCAP format, derivation of packet-based information, transformation of packets into RGB images, and then application of machine learning techniques. Our research questions are aimed to identify the best image mapping schemes, the most effective classifiers, and identification of best image features.
Publication Date
3-2025
Department
Computer Science
City
Mobile
Disciplines
Cybersecurity | Information Security | Other Computer Sciences
Recommended Citation
Suon, Chakriya, "Using Image-Based Representation for Network Intrusion Detection" (2025). Shelby Hall Graduate Research Forum Posters. 8.
https://jagworks.southalabama.edu/southalabama-shgrf-posters/8
