FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm

• URL: http://arxiv.org/abs/2508.09056v1
• Date: Tue, 12 Aug 2025 16:16:29 GMT
• Title: FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm
• Authors: Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal,
• Abstract summary: Intrusion Detection Systems (IDS) have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors.<n>Recent deep learning based developments have resulted in significant improvements in the performance of IDS systems.<n>We present FetFIDS, where we explore the employment of feature embedding instead of positional embedding to improve intrusion detection performance.
• Score: 10.662159185662796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intrusion Detection Systems (IDS) have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors. Recent deep learning based developments have resulted in significant improvements in the performance of IDS systems. In this paper, we present FetFIDS, where we explore the employment of feature embedding instead of positional embedding to improve intrusion detection performance of a transformer based deep learning system. Our model is developed with the aim of deployments in edge learning scenarios, where federated learning over multiple communication rounds can ensure both privacy and localized performance improvements. FetFIDS outperforms multiple state-of-the-art intrusion detection systems in a federated environment and demonstrates a high degree of suitability to federated learning. The code for this work can be found at https://github.com/ghosh64/fetfids.

Related papers

• One-Class Intrusion Detection with Dynamic Graphs [46.453758431767724]
  Machine learning-based intrusion detection constitutes a promising approach for improving security.<n>We propose a novel intrusion detection method, TGN-SVDD, which builds upon modern dynamic graph modelling and deep anomaly detection.<n>We demonstrate its superiority over several baselines for realistic intrusion detection data and suggest a more challenging variant of the latter.
  arXiv  Detail & Related papers  (2025-08-18T12:36:55Z)
• Federated Intrusion Detection System Based on Unsupervised Machine Learning [0.6990493129893112]
  Intrusion Detection System (IDS) research has increasingly moved towards the adoption of machine learning methods.<n>Most IDS systems rely on supervised learning approaches, necessitating a fully labeled training set.<n>We propose an IDS architecture that utilizes unsupervised learning to reduce the need for labeling.
  arXiv  Detail & Related papers  (2025-03-28T01:01:58Z)
• A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series [45.31237646796715]
  This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions.<n>The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training.<n>Results indicate significant performance improvements across all settings.
  arXiv  Detail & Related papers  (2024-07-03T07:19:41Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Improving Transferability of Network Intrusion Detection in a Federated Learning Setup [11.98319841778396]
  Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. We propose two techniques to significantly improve the transferability of a federated intrusion detection system.
  arXiv  Detail & Related papers  (2024-01-07T17:52:41Z)
• Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection [2.7225008315665424]
  This paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection architecture based on the G-Network model with collaborative learning. The performance evaluation results using public Kitsune and Bot-IoT datasets show that DOF-ID significantly improves the intrusion detection performance in all of the collaborating components.
  arXiv  Detail & Related papers  (2023-06-22T16:46:00Z)
• Learned-Database Systems Security [46.898983878921484]
  We develop a framework for identifying vulnerabilities that stem from the use of machine learning (ML)<n>We show that the use of ML cause leakage of past queries in a database, enable a poisoning attack that causes exponential memory blowup and crashes it in seconds.<n>We find that adversarial ML is an universal threat against learned components in database systems.
  arXiv  Detail & Related papers  (2022-12-20T15:09:30Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services [0.0]
  Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Traditional intrusion detection systems (IDS) do not detect any anomalies in the publish/subscribe method. This report presents an experimental work on simulation and application of Deep Learning for their detection.
  arXiv  Detail & Related papers  (2021-06-12T12:53:38Z)
• Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning [30.536369182792516]
  Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network. We present and evaluate two deep learning approaches to the reliable IoT device-type identification.
  arXiv  Detail & Related papers  (2021-02-16T14:49:34Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach [44.46178415547532]
  We propose a robust presentation attack detection (PAD) solution with improved cross-material and cross-sensor generalization. Specifically, we build on any CNN-based architecture trained for fingerprint spoof detection combined with cross-material spoof generalization. We also incorporate adversarial representation learning (ARL) in deep neural networks (DNN) to learn sensor and material invariant representations for PAD.
  arXiv  Detail & Related papers  (2020-04-06T19:03:05Z)
• Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM [31.480769801354413]
  This work aims to develop secure distributed algorithms to protect the learning from data poisoning and network attacks. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (SVMs) and an attacker who is capable of modifying training data and labels. The numerical results show that distributed SVM is prone to fail in different types of attacks, and their impact has a strong dependence on the network structure and attack capabilities.
  arXiv  Detail & Related papers  (2020-03-08T18:54:17Z)

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.