Wireguard: An Efficient Solution for Securing IoT Device Connectivity
- URL: http://arxiv.org/abs/2402.02093v1
- Date: Sat, 3 Feb 2024 09:11:11 GMT
- Title: Wireguard: An Efficient Solution for Securing IoT Device Connectivity
- Authors: Haseebullah Jumakhan, Amir Mirzaeinia,
- Abstract summary: The proliferation of vulnerable Internet-of-Things (IoT) devices has enabled large-scale cyberattacks.
This research evaluates if Wireguard, an emerging VPN protocol, can provide efficient security tailored for resource-constrained IoT systems.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The proliferation of vulnerable Internet-of-Things (IoT) devices has enabled large-scale cyberattacks. Solutions like Hestia and HomeSnitch have failed to comprehensively address IoT security needs. This research evaluates if Wireguard, an emerging VPN protocol, can provide efficient security tailored for resource-constrained IoT systems. We compared Wireguards performance against standard protocols OpenVPN and IPsec in a simulated IoT environment. Metrics measured included throughput, latency, and jitter during file transfers. Initial results reveal Wireguard's potential as a lightweight yet robust IoT security solution despite disadvantages for Wireguard in our experimental environment. With further testing, Wireguards simplicity and low overhead could enable widespread VPN adoption to harden IoT devices against attacks. The protocols advantages in setup time, performance, and compatibility make it promising for integration especially on weak IoT processors and networks.
Related papers
- zk-IoT: Securing the Internet of Things with Zero-Knowledge Proofs on Blockchain Platforms [0.0]
This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems.
Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices.
arXiv Detail & Related papers (2024-02-13T09:34:23Z) - SunBlock: Cloudless Protection for IoT Systems [7.267200149618047]
Many existing IoT protection solutions are cloud-based, sometimes ineffective, and might share consumer data with unknown third parties.
This paper investigates the potential for effective IoT threat detection locally, on a home router, using AI tools combined with classic rule-based traffic-filtering algorithms.
Our results show that with a slight rise of router hardware resources, a typical home router instrumented with our solution is able to effectively detect risks and protect a typical home IoT network.
arXiv Detail & Related papers (2024-01-25T17:30:08Z) - 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) - Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
This paper provides a classification of IoT malware.
Major targets and used exploits for attacks are identified and referred to the specific malware.
The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
arXiv Detail & Related papers (2023-12-01T16:10:43Z) - SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices [67.65883495888258]
We present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes.
SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices.
We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud.
arXiv Detail & Related papers (2023-09-26T08:11:38Z) - RIS-assisted UAV Communications for IoT with Wireless Power Transfer
Using Deep Reinforcement Learning [75.677197535939]
We propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from unmanned aerial vehicle (UAV) communications.
In a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission.
We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.
arXiv Detail & Related papers (2021-08-05T23:55:44Z) - 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) - Lightweight IoT Malware Detection Solution Using CNN Classification [2.288885651912488]
The security aspect of IoT devices is an infant field, which is why it is our focus in this paper.
We developed a system that can recognize malicious behavior of a specific IoT node on the network.
Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network.
arXiv Detail & Related papers (2020-10-13T10:56:33Z) - Zero-Bias Deep Learning for Accurate Identification of Internet of
Things (IoT) Devices [20.449229983283736]
We propose an enhanced deep learning framework for IoT device identification using physical layer signals.
We have evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation.
arXiv Detail & Related papers (2020-08-27T20:50:48Z) - Smart Home, security concerns of IoT [91.3755431537592]
The IoT (Internet of Things) has become widely popular in the domestic environments.
People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed.
Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
arXiv Detail & Related papers (2020-07-06T10:36:11Z) - IoT Device Identification Using Deep Learning [43.0717346071013]
The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers.
The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks.
In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network.
arXiv Detail & Related papers (2020-02-25T12:24:49Z)
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.