A Hybrid CNN-LSTM Deep Learning Model for Intrusion Detection in Smart Grid

• URL: http://arxiv.org/abs/2509.07208v1
• Date: Mon, 08 Sep 2025 20:41:31 GMT
• Title: A Hybrid CNN-LSTM Deep Learning Model for Intrusion Detection in Smart Grid
• Authors: Abdulhakim Alsaiari, Mohammad Ilyas,
• Abstract summary: This research proposes a hybrid deep learning-based Intrusion Detection System (IDS) intended to improve the cybersecurity of smart grids.<n>The results demonstrate considerable improvements in accuracy, precision, recall, and F1-score, with a detection accuracy of 99.70%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management, which allows the integration of renewable energy sources with modern communication technology. However, this interconnection has increased smart grids' vulnerability to attackers, which might result in privacy breaches, operational interruptions, and massive outages. The SCADA-based smart grid protocols are critical for real-time data collection and control, but they are vulnerable to attacks like unauthorized access and denial of service (DoS). This research proposes a hybrid deep learning-based Intrusion Detection System (IDS) intended to improve the cybersecurity of smart grids. The suggested model takes advantage of Convolutional Neural Networks' (CNN) feature extraction capabilities as well as Long Short-Term Memory (LSTM) networks' temporal pattern recognition skills. DNP3 and IEC104 intrusion detection datasets are employed to train and test our CNN-LSTM model to recognize and classify the potential cyber threats. Compared to other deep learning approaches, the results demonstrate considerable improvements in accuracy, precision, recall, and F1-score, with a detection accuracy of 99.70%.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Adaptive Intrusion Detection System Leveraging Dynamic Neural Models with Adversarial Learning for 5G/6G Networks [2.062593640149623]
  This paper presents an advanced IDS framework that leverages adversarial training and dynamic neural networks in 5G/6G networks.<n>Unlike conventional models, which require costly retraining to update knowledge, the proposed framework integrates incremental learning algorithms, reducing the need for frequent retraining.
  arXiv  Detail & Related papers  (2025-12-11T13:40:37Z)
• Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation [29.49941497527361]
  PINNs present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks.<n>This paper evaluates PINNs' capabilities as surrogate models for smart grid dynamics.<n>We demonstrate PINNs' superior generalization, outperforming data-driven models in error reduction.
  arXiv  Detail & Related papers  (2025-08-29T12:15:32Z)
• Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems [0.0]
  False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures.<n>This paper presents a machine learning-based framework for both the detection and classification of FDIAs using residential energy data.
  arXiv  Detail & Related papers  (2025-08-09T18:27:31Z)
• Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning [39.58317527488534]
  This paper proposes a novel method using hybrid statistical-deep learning for the detection, prevention, and source localization of IEC 61850 SV injection attacks.<n>It effectively discards malicious SV frames with minimal processing overhead and latency, maintains robustness against communication network latency variation and time-synchronization issues.<n>Results demonstrate the method's suitability for practical deployment in IEC 61850-compliant digital substations.
  arXiv  Detail & Related papers  (2025-07-01T07:38:22Z)
• CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid [0.5499796332553707]
  Cyberattacks on smart grids can compromise data integrity and jeopardize the reliability of the power supply.<n>Traditional intrusion detection systems often need help to effectively detect novel and sophisticated attacks.<n>This work proposes a semi-supervised method for cyber-attack detection in smart grids by leveraging the labeled and unlabeled measurement data.
  arXiv  Detail & Related papers  (2025-03-01T05:49:23Z)
• Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks [1.0678175996321808]
  New types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets.<n>We suggest a new approach to IoT intrusion detection using Self-Supervised Learning (SSL) with a Markov Graph Convolutional Network (MarkovGCN)<n>Our approach leverages the inherent structure of IoT networks to pre-train a GCN, which is then fine-tuned for the intrusion detection task.
  arXiv  Detail & Related papers  (2024-12-17T17:40:14Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies [41.994460245857404]
  This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources.<n>We develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages.
  arXiv  Detail & Related papers  (2024-03-11T17:28:46Z)
• CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks [36.01752474571776]
  Mobile Edge Caching (MEC) integrated with Deep Neural Networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks. The MEC network's effectiveness heavily relies on its capacity to predict and dynamically update the storage of caching nodes with the most popular contents. To be effective, a DNN-based popularity prediction model needs to have the ability to understand the historical request patterns of content.
  arXiv  Detail & Related papers  (2023-03-21T15:57:46Z)
• Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model [0.1529342790344802]
  Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. The smart home gateways act as a centralized point of communication between the IoT devices, which can create a backdoor into network data for hackers. In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model.
  arXiv  Detail & Related papers  (2021-05-25T17:32:03Z)
• An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids [2.281079191664481]
  Electricity Theft Detection (EDT) algorithms are typically used for such purpose since this Non-Technical Loss (NTL) may lead to significant challenges in the power system. In this paper, an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed.
  arXiv  Detail & Related papers  (2021-02-10T18:27:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.