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

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.