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Novel Optimizer AdamW+ implementation in LSTM Model for DGA Detection


Awais Javed, Adnan Rashdi, Imran Rashid, Faisal Amir


Vol. 23  No. 11  pp. 133-141


This work take deeper analysis of Adaptive Moment Estimation (Adam) and Adam with Weight Decay (AdamW) implementation in real world text classification problem (DGA Malware Detection). AdamW is introduced by decoupling weight decay from L2 regularization and implemented as improved optimizer. This work introduces a novel implementation of AdamW variant as AdamW+ by further simplifying weight decay implementation in AdamW. DGA malware detection LSTM models results for Adam, AdamW and AdamW+ are evaluated on various DGA families/ groups as multiclass text classification. Proposed AdamW+ optimizer results has shown improvement in all standard performance metrics over Adam and AdamW. Analysis of outcome has shown that novel optimizer has outperformed both Adam and AdamW text classification based problems.


DGA Detection, Deep learning, LSTM, Adam, AdamW AdamW+.