# Omni SCADA Intrusion Detection Using Deep Learning Algorithms

arXiv: https://arxiv.org/abs/1908.01974 · HTML: https://mlsanity.com/paper/1908.01974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach](https://mlsanity.com/paper/2204.04837/) (2022)
- [L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)](https://mlsanity.com/paper/2510.07363/) (2025)
- [SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic](https://mlsanity.com/paper/2402.14862/) (2024)
- [Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network](https://mlsanity.com/paper/2406.06595/) (2024)

## Abstract (arXiv)

We investigate deep learning based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this paper, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an F1 of 99.967±0.005% but correlated attacks as low as 58±2%. In contrast, long-short term memory (LSTM) detects correlated attacks at 99.56±0.01% while uncorrelated attacks at 99.3±0.1%. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with F1 of 99.68±0.04% regardless the temporal correlations among the data packets.