Author

Istiaque Ahmed

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#machine learning Jul 2026

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

Benign-Anchored Ranking and Selection (BARS), a two-stage filter that replaces CMD's global anchor with the benign-class mean and applies an order-preserving decorrelation step, is proposed, which reduces false alarms by up to 32% while maintaining similar detection rates, with larger gains under stronger imbalance.

A. Ahmad, Istiaque Ahmed · arXiv.org · 0 citations

#machine learning Jun 2026

nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection

Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fail to capture the asym...

A. Ahmad, Istiaque Ahmed · arXiv.org · 0 citations

#machine learning Preprint Aug 2026

Reflex-Guard: A Low-Latency Guardrail for LLM Prompt Safety Using Dense Semantic Embeddings

Reflex-Guard is introduced, a lightweight guardrail that runs locally that uses jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers that enable high-accuracy prompt safety filtering with much lower latency than existing solutions.

Istiaque Ahmed, Afia Anjum Borsha, Ranat Das Prangon et al. · 0 citations