Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection

• URL: http://arxiv.org/abs/2503.14618v1
• Date: Tue, 18 Mar 2025 18:13:51 GMT
• Title: Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection
• Authors: Leonardo Henrique de Melo, Gustavo de Carvalho Bertoli, Michele Nogueira, Aldri Luiz dos Santos, Lourenço Alves Pereira Junior,
• Abstract summary: Distributed denial-of-service (DDoS) attacks remain a critical threat to Internet services.<n>Current solutions struggle with multi-domain environments where attacks must be detected across heterogeneous networks.<n>This paper introduces Anomaly-Flow, a novel framework that combines Federated Learning (FL) with Generative Adversarial Networks (GANs) for privacy-preserving, multi-domain DDoS detection.
• Score: 2.5072568692549964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed denial-of-service (DDoS) attacks remain a critical threat to Internet services, causing costly disruptions. While machine learning (ML) has shown promise in DDoS detection, current solutions struggle with multi-domain environments where attacks must be detected across heterogeneous networks and organizational boundaries. This limitation severely impacts the practical deployment of ML-based defenses in real-world settings. This paper introduces Anomaly-Flow, a novel framework that addresses this critical gap by combining Federated Learning (FL) with Generative Adversarial Networks (GANs) for privacy-preserving, multi-domain DDoS detection. Our proposal enables collaborative learning across diverse network domains while preserving data privacy through synthetic flow generation. Through extensive evaluation across three distinct network datasets, Anomaly-Flow achieves an average F1-score of $0.747$, outperforming baseline models. Importantly, our framework enables organizations to share attack detection capabilities without exposing sensitive network data, making it particularly valuable for critical infrastructure and privacy-sensitive sectors. Beyond immediate technical contributions, this work provides insights into the challenges and opportunities in multi-domain DDoS detection, establishing a foundation for future research in collaborative network defense systems. Our findings have important implications for academic research and industry practitioners working to deploy practical ML-based security solutions.

Related papers

• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds.<n>The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.<n>This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• TabSec: A Collaborative Framework for Novel Insider Threat Detection [8.27921273043059]
  In the era of the Internet of Things (IoT) and data sharing, users frequently upload their personal information to enterprise databases to enjoy enhanced service experiences. However, the widespread presence of system vulnerabilities, remote network intrusions, and insider threats significantly increases the exposure of private enterprise data on the internet. This paper proposes a novel threat detection framework, TabITD, to address these challenges.
  arXiv  Detail & Related papers  (2024-11-04T04:07:16Z)
• Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks [19.697853431302768]
  Digital twins (DTs) embody real-time monitoring, predictive, and enhanced decision-making capabilities.<n>This study investigates the security challenges in distributed network DT systems, which potentially undermine the reliability of subsequent network applications.
  arXiv  Detail & Related papers  (2024-07-02T03:32:09Z)
• CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems [17.351539765989433]
  A growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. Most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models. In this paper, we present CANEDERLI, a novel framework for securing CAN-based IDSs.
  arXiv  Detail & Related papers  (2024-04-06T14:54:11Z)
• 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)
• Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection [5.787704156827843]
  In this paper, we introduce a framework aimed at mitigating the open set recognition (OSR) problem in the realm of Network Intrusion Detection Systems (NIDS) tailored for IoT environments. Our framework capitalizes on image-based representations of packet-level data, extracting spatial and temporal patterns from network traffic. The empirical results prominently underscore the framework's efficacy, boasting an impressive 88% detection rate for previously unseen attacks.
  arXiv  Detail & Related papers  (2023-09-14T06:41:45Z)
• Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning [0.0]
  Fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions. We propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network.
  arXiv  Detail & Related papers  (2023-03-28T09:06:28Z)
• RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems [41.1371349978643]
  We present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data.
  arXiv  Detail & Related papers  (2022-08-27T14:50:00Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

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.