Exploring Machine Learning with FNNs for Identifying Modified DGAs through Noise and Linear Recursive Sequences (LRS)

Outlet Title

2024 Cyber Awareness and Research Symposium (CARS)

Document Type

Conference Proceeding

Publication Date

2024

Abstract

The study proposes a comprehensive technique to identify novel variations within Domain Generating Algorithm (DGA) families, crucial for securing critical infrastructures. This technique incorporates Damerau-Levenshtein Distance to enhance Feedforward Neural Network (FNN) adaptability to diverse DGA manifestations, including Linear Recursive Sequence (LRS) modification. By strategically selecting features, it demonstrates robustness against domain-specific noise, vital in detecting increasingly sophisticated cyber threats. The approach is systematically evaluated for adaptability to various noise forms, ensuring real-time threat detection and incident response efficacy. With an accuracy rate of 100%, the method proves its versatility in handling diverse cyber threats, making it a valuable asset for network security practitioners.

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