Lightweight Dataset for Decoy Development to Improve IoT Security
- URL: http://arxiv.org/abs/2407.19926v1
- Date: Mon, 29 Jul 2024 12:01:50 GMT
- Title: Lightweight Dataset for Decoy Development to Improve IoT Security
- Authors: David Weissman, Anura P. Jayasumana,
- Abstract summary: 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.
- Score: 0.1227734309612871
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, the authors introduce a lightweight dataset to interpret IoT (Internet of Things) activity in preparation to create decoys by replicating known data traffic patterns. The dataset comprises different scenarios in a real network setting. This paper also surveys information related to other IoT datasets along with the characteristics that make our data valuable. Many of the datasets available are synthesized (simulated) or often address industrial applications, while the IoT dataset we present is based on likely smart home scenarios. Further, there are only a limited number of IoT datasets that contain both normal operation and attack scenarios. A discussion of the network configuration and the steps taken to prepare this dataset are presented as we prepare to create replicative patterns for decoy purposes. The dataset, which we refer to as IoT Flex Data, consists of four categories, namely, IoT benign idle, IoT benign active, IoT setup, and malicious (attack) traffic associating the IoT devices with the scenarios under consideration.
Related papers
- IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio.
Machine learning presents a rich opportunity to automatically process IoT data at scale.
We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
arXiv Detail & Related papers (2024-07-13T08:20:37Z) - Speech Emotion Recognition under Resource Constraints with Data Distillation [64.36799373890916]
Speech emotion recognition (SER) plays a crucial role in human-computer interaction.
The emergence of edge devices in the Internet of Things presents challenges in constructing intricate deep learning models.
We propose a data distillation framework to facilitate efficient development of SER models in IoT applications.
arXiv Detail & Related papers (2024-06-21T13:10:46Z) - 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) - TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual
Environments [84.6017003787244]
This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets.
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
arXiv Detail & Related papers (2022-08-16T20:46:08Z) - 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) - Computational Intelligence and Deep Learning for Next-Generation
Edge-Enabled Industrial IoT [51.68933585002123]
We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework.
In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
arXiv Detail & Related papers (2021-10-28T08:14:57Z) - Software-Defined Edge Computing: A New Architecture Paradigm to Support
IoT Data Analysis [21.016796500957977]
We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions.
Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis.
arXiv Detail & Related papers (2021-04-22T11:19:20Z) - 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) - Machine learning and data analytics for the IoT [8.39035688352917]
We review how IoT-generated data are processed for machine learning analysis.
We propose a framework to enable IoT applications to adaptively learn from other IoT applications.
arXiv Detail & Related papers (2020-06-30T07:38:31Z)
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.