Digital Twin-Enabled Intelligent DDoS Detection Mechanism for Autonomous Core Networks

• URL: http://arxiv.org/abs/2310.12924v2
• Date: Wed, 25 Oct 2023 23:22:27 GMT
• Title: Digital Twin-Enabled Intelligent DDoS Detection Mechanism for Autonomous Core Networks
• Authors: Yagmur Yigit, Bahadir Bal, Aytac Karameseoglu, Trung Q. Duong, Berk Canberk
• Abstract summary: Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems.
• Score: 13.49717874638757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are three-fold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently, improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts.

Related papers

• A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification [0.0]
  This research introduces a novel approach for DDoS attack detection. The proposed method achieves a precision of 98.71%, an F1-score of 98.66%, a recall of 98.63%, and an accuracy of 98.69%.
  arXiv  Detail & Related papers  (2024-09-01T18:58:33Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN) [0.41436032949434404]
  This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic. The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms. The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks, achieving an accuracy of.9883 on 2000 unseen flows in network traffic.
  arXiv  Detail & Related papers  (2023-09-11T17:37:35Z)
• Predict And Prevent DDOS Attacks Using Machine Learning and Statistical Algorithms [0.0]
  This study uses several machine learning and statistical models to detect DDoS attacks from traces of traffic flow. The XGboost machine learning model provided the best detection accuracy of (99.9999%) after applying the SMOTE approach to the target class.
  arXiv  Detail & Related papers  (2023-08-30T00:03:32Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains [0.13999481573773068]
  This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework. Overall, the detection performance achieves approximately 99% accuracy for the detection of attacks from botnets.
  arXiv  Detail & Related papers  (2022-07-15T14:09:08Z)
• Modelling DDoS Attacks in IoT Networks using Machine Learning [21.812642970826563]
  TCP-specific attacks are one of the most plausible tools that attackers can use on Cyber-Physical Systems. This study compares the effectiveness of supervised, unsupervised, and semi-supervised machine learning algorithms for detecting DDoS attacks in CPS-IoT.
  arXiv  Detail & Related papers  (2021-12-10T12:09:26Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• Automating Botnet Detection with Graph Neural Networks [106.24877728212546]
  Botnets are now a major source for many network attacks, such as DDoS attacks and spam. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically.
  arXiv  Detail & Related papers  (2020-03-13T15:34:33Z)

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.