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2026-10-20, Week 43
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ICACT20260103 Question.1
Questioner: hripsime.hovhannisyan@rau.am    2026-02-10 ¿ÀÈÄ 5:20:29
ICACT20260103 Answer.1
Answer by Auhor arjunvirvashisht@gmail.com   2026-02-10 ¿ÀÈÄ 5:20:29
Chrome Click!!
Regarding the practical application of this framework, what is the total parameter count? This is a critical point for practical cybersecurity. In our experimental setup, the average inference latency per URL is approximately 0.15 to 0.25 milliseconds on standard hardware. This efficiency is achieved because the final classification is offloaded to a Support Vector Machine (SVM) using the deep temporal embeddings extracted by the BiLSTM. For real-time line rate defense, our framework supports asynchronous processing: while the 4-phase feature selection pipeline (MI, RFE, importance ranking, and correlation filtering) ensures only relevant data is processed, the low latency SVM prediction makes it highly suitable for integration into browser extensions or edge level firewall APIs.
ICACT20260103 Question.2
Questioner: hripsime.hovhannisyan@rau.am    2026-02-10 ¿ÀÈÄ 4:44:20
ICACT20260103 Answer.2
Answer by Auhor arjunvirvashisht@gmail.com   2026-02-10 ¿ÀÈÄ 4:44:20
Chrome Click!!
What is the inference latency of the model, and is it low enough to support real-time line-rate cyber defense? This is a critical point for practical cybersecurity. In our experimental setup, the average inference latency per URL is approximately 0.15 to 0.25 milliseconds on standard hardware. This efficiency is achieved because the final classification is offloaded to a Support Vector Machine (SVM) using the deep temporal embeddings extracted by the BiLSTM. For real-time line-rate defense, our framework supports asynchronous processing: while the 4-phase feature selection pipeline (MI, RFE, importance ranking, and correlation filtering) ensures only relevant data is processed, the low latency SVM prediction makes it highly suitable for integration into browser extensions or edge level firewall APIs.