The Impact of DoS Attacks onResource-constrained IoT Devices:A Study on the Mirai Attack

Tushir, Bhagyashri; Sehgal, Hetesh; Nair, Rohan; Dezfouli, Behnam; Liu, Yuhong

doi:10.48550/arxiv.2104.09041

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…An investigation of IoT security vulnerabilities has been conducted by Neshenko et al [2], Zhou et al [3], and Panchal et al [4] to discuss numerous attack-types and the corresponding impact on IoT sub-systems. There has been a rapid increase in vulnerable activities that exploit the security gaps of IoT networks [5]. Moreover, there is a huge demand for IoT devices owing to the popularity of microdevices that provide intelligent digital assistance and have been demonstrated to minimize manual labor.…”

Section: Research Domainmentioning

confidence: 99%

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

et al. 2022

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More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes. Machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. However, the ML-training procedure incorporates large data which is transferable to the central server since data are created continually by IoT devices at the edge. In other words, conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy. The Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. By exchanging updated weights with the centralized FL-server, the data are kept on local IoT devices while federating training cycles over GRUs (Gated Recurrent Units) models. The ensemble module of the technique assesses updates from several sources for improving the accuracy of the global ML technique. Experiments have shown that the proposed method surpasses the state-of-the-art techniques in protecting user data by registering enhanced performance measures of Statistical Analysis, Energy Efficiency, Memory Utilization, Attack Classification, and Client Accuracy Analysis for the identification of attacks.

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Section: Research Domainmentioning

confidence: 99%

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

et al. 2022

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“…A single Distributed DoS (DDoS) attack can compromise thousands of devices [88] through Botnets where victims themselves join the attack by being turned into "bots" [89]. An example is the 2016 massive "Mirai" attack that brought down the Domain Name System (DNS) Dyn [90], and blocked access to Netflix, Reddit, Spotify, and Twitter [91], [92] , accessing the equipment of major cybersecurity providers [93], and also congesting IoT and IP networks [94]. Thus as soon as a Botnet attempts an attack, it is crucial to detect it, if possible block it, and especially avoid its propagation and proliferation.…”

Section: Contributions To the Security Of The Iotmentioning

confidence: 99%

Review of Some Recent European Cybersecurity Research and Innovation Projects

Caglayan¹

2022

Infocommunications journal

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This paper reviews research from several EU Projects that have addressed cybersecurity using techniques based on Machine Learning, including the security of Mobile Networks and the Internet of Things (IoT). These research projects have considered IoT Gateways and their design, security and performance, the security of digital health systems that are interconnected across Europe to provide health services to pople who travel through the EU, and related issues of the energy consumption and sustainability in Information and Communication Technologies (ICT) and their cybersecurity. The methods used in much of these research projects are based on Machine Learning both for attack detection and dynamic attack mitigation, as well as performance analysis and measurement techniques based on applied probability models.

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“…In addition to network-wide effects, Botnets significantly increase network congestion and power consumption, and processor and memory usage at the device level, hence pos- ing challenges for resource-constrained devices [17]. Thus, given the nefarious impact of Botnets at both network and device level in Massive IoT networks, it is crucial to identify malicious packets and compromised IoT devices in real-time during an attack, so as to prevent the attack from spreading.…”

Section: Introductionmentioning

confidence: 99%

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

Gelenbe

Nakıp

2022

IEEE Access

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A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.

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The Impact of DoS Attacks onResource-constrained IoT Devices:A Study on the Mirai Attack

Cited by 5 publications

References 6 publications

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

Review of Some Recent European Cybersecurity Research and Innovation Projects

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

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