# Densely Connected Residual Network for Attack Recognition

arXiv: https://arxiv.org/abs/2008.02196 · HTML: https://mlsanity.com/paper/2008.02196/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents](https://mlsanity.com/paper/2503.23804/) (2025)
- [Retrieval-Augmented Review Generation for Poisoning Recommender Systems](https://mlsanity.com/paper/2508.15252/) (2025)
- [Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector](https://mlsanity.com/paper/2104.08044/) (2021)

## Abstract (arXiv)

High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densely-ResNet) for attack recognition. Densely-ResNet is built with several basic residual units, where each of them consists of a series of Conv-GRU subnets by wide connections. Our evaluation shows that Densely-ResNet can accurately discover various unknown threats that appear in edge, fog and cloud layers and simultaneously maintain a much lower false alarm rate than existing algorithms.