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IEEE/ICACT20260079 Question.4 Questioner: hripsime.hovhannisyan@rau.am 2026-02-10 오후 4:54:34	IEEE/ICACT20260079 Answer.4 Answer by Auhor juniorokangondoloshima@esp.sn 2026-02-10 오후 4:54:34
Did you observe any specific drop in accuracy or "forgetting" of certain malware classes after the pruning and quantization process?	Based on our experiments, we did not observe any significant “catastrophic forgetting” of specific malware classes after pruning and quantization. The optimizations led to only a marginal and controlled reduction in performance, while class-wise precision and recall remained well balanced across malware families. This stability is mainly due to the hybrid multimodal fusion (CNN–LSTM–Autoencoder) and the use of transfer learning and incremental learning, which help preserve discriminative features even after model compression. As a result, the optimized model maintains high accuracy (94.8%) and a low false positive rate (3.8%) in real-world conditions, demonstrating that pruning and quantization improved efficiency without degrading class-level detection capability

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