Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research

• URL: http://arxiv.org/abs/2502.03134v1
• Date: Wed, 05 Feb 2025 12:51:18 GMT
• Title: Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research
• Authors: Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi, Omer Rana, Pietro Carnelli, Aftab Khan,
• Abstract summary: Gotham testbed is an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. Network traffic was captured in Packetdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnete Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format.
• Score: 2.056126049000989
• License:
• Abstract: In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices operating on various protocols, including MQTT, CoAP, and RTSP. Network traffic was captured in Packet Capture (PCAP) format using tcpdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnet Brute Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data were subsequently processed in Python for feature extraction using the Tshark tool, and the resulting data was converted to Comma Separated Values (CSV) format and labelled. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format. Our dataset was collected in a distributed manner, where network traffic was captured separately for each IoT device at the interface between the IoT gateway and the device. Our dataset was collected in a distributed manner, where network traffic was separately captured for each IoT device at the interface between the IoT gateway and the device. With its diverse traffic patterns and attack scenarios, this dataset provides a valuable resource for developing Intrusion Detection Systems and security mechanisms tailored to complex, large-scale IoT environments. The dataset is publicly available at Zenodo.

Related papers

• MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification [59.96233305733875]
  Classifying traffic is essential for detecting security threats and optimizing network management. We propose a Multi-Instance Encrypted Traffic Transformer (MIETT) to capture both token-level and packet-level relationships. MIETT achieves results across five datasets, demonstrating its effectiveness in classifying encrypted traffic and understanding complex network behaviors.
  arXiv  Detail & Related papers  (2024-12-19T12:52:53Z)
• Lightweight Dataset for Decoy Development to Improve IoT Security [0.1227734309612871]
  This paper introduces a lightweight dataset to interpret IoT (Internet of Things) activity in preparation to create decoys. The dataset comprises different scenarios in a real network setting.
  arXiv  Detail & Related papers  (2024-07-29T12:01:50Z)
• 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)
• IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways [45.44831696628473]
  This paper presents IoTScent, an open-source forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis. IoTScent is specifically designed to operate over IEEE5.4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread. This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic.
  arXiv  Detail & Related papers  (2023-10-05T09:10:05Z)
• Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network [0.0]
  Internet of Things (IoT) has altered living by controlling devices/things over the Internet. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset.
  arXiv  Detail & Related papers  (2022-07-16T11:12:32Z)
• CoAP-DoS: An IoT Network Intrusion Dataset [0.0]
  Internet of Things (IoT) devices are susceptible to denial-of-service attacks. There are many network traffic data sets but very few that focus on IoT network traffic. We develop a new data set by collecting network traffic from real CoAP denial of service attacks.
  arXiv  Detail & Related papers  (2022-06-29T00:50:15Z)
• Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection [3.3659034873495632]
  Internet of Things (IoT) devices are constructed to build a huge communications network. These devices are insecure in reality, it means that the communications network are exposed by the attacker. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection.
  arXiv  Detail & Related papers  (2021-04-05T08:51:24Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• 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)
• Taurus: A Data Plane Architecture for Per-Packet ML [59.1343317736213]
  We present the design and implementation of Taurus, a data plane for line-rate inference. Our evaluation of a Taurus switch ASIC shows that Taurus operates orders of magnitude faster than a server-based control plane.
  arXiv  Detail & Related papers  (2020-02-12T09:18:36Z)

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.