Detection of Distributed Denial of Service Attack in an Internet of Things Environment -A Review

Varalakshmi, I.; Thenmozhi, M.; Sasi, R.

doi:10.1109/icscan53069.2021.9526378

Cited by 10 publications

(4 citation statements)

References 8 publications

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“… Denial of Service (DoS): A cyber-attack makes IoT systems or resources on the network unavailable to the intended legitimate users. These attacks are launched by the perpetrators to temporarily or permanently disrupt a host IoT system’s services [ 41 , 42 , 43 ]. A distributed denial-of-service (DDoS): The attack is a malicious network attack that can disrupt the regular traffic and the network’s services.…”

Section: Iot System and Security Issuesmentioning

confidence: 99%

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Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Gyamfi

Jurcut

2022

Sensors

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The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

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Section: Iot System and Security Issuesmentioning

confidence: 99%

Section: Iot System and Security Issuesmentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Gyamfi

Jurcut

2022

Sensors

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“…Among them, distributed denial-of-service (DDoS) and ransomware are the most powerful attacks. DDoS and denial-of-service attacks cause a high load because of excessive network traffic to the IoT, thus interrupting the regular operation of the victim device; additionally, ransomware encrypts the victim's data to disturb legitimate user access [7][8][9]. Furthermore, IoT devices cannot perform their normal operations, making it difficult for organizations to provide smooth services.…”

Section: Introductionmentioning

confidence: 99%

Malicious Traffic Compression and Classification Technique for Secure Internet of Things

Lee,

Park,

Kim

et al. 2023

Computers, Materials &Amp; Continua

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With the introduction of 5G technology, the application of Internet of Things (IoT) devices is expanding to various industrial fields. However, introducing a robust, lightweight, low-cost, and low-power security solution to the IoT environment is challenging. Therefore, this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment. The first method, compressed sensing and learning (CSL), compresses an event log in a bitmap format to quickly detect attacks. Then, the attack log is detected using a machine-learning classification model. The second method, precise re-learning after CSL (Ra-CSL), comprises a two-step training. It uses CSL as the 1st step analyzer, and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer. In the experiment, the bitmap rule was set based on the boundary value, which was 99.6% true positive on average for the attack and benign data found by analyzing the training data. Experimental results showed that the CSL was effective in reducing the training and detection time, and Ra-CSL was effective in increasing the detection rate. According to the experimental results, the data compression technique reduced the memory size by up to 20% and the training and detection times by 67% when compared with the conventional technique. In addition, the proposed technique improves the detection accuracy; the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.

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“…Various technologies such as Machine Learning, Deep Learning, Blockchain, and Cyber Security have been applied by researchers for handling DDoS attacks. Securing data in an IoT network is challenging due to its decentralized nature and data being shared among millions of devices (Varalakshmi et al, 2021;Stergiou et al, 2021;A. Singh & Gupta, 2022).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Distributed Denial of Services (DDoS) Attack Detection in Intelligent Information Systems

Alhalabi

Gaurav

Arya

et al. 2023

International Journal on Semantic Web and Information Systems

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The danger of distributed denial of service (DDoS) attacks has grown in tandem with the proliferation of intelligent information systems. Because of the sheer volume of connected devices, constantly shifting network circumstances, and the need for instantaneous reaction, conventional DDoS detection methods are inadequate for the IoT. In this context, this study aims to survey the current state of the art in the topic by reading relevant articles found in the Scopus database, with a brief overview of the IoT and DDoS as this study examines neural networks and their applicability to DDoS detection. Finally, a decision tree-based model is developed for the detection of DDoS attacks. The analysis sheds light on the present trends and issues in this field and suggests avenues for further study.

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Detection of Distributed Denial of Service Attack in an Internet of Things Environment -A Review

Cited by 10 publications

References 8 publications

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Malicious Traffic Compression and Classification Technique for Secure Internet of Things

Machine Learning-Based Distributed Denial of Services (DDoS) Attack Detection in Intelligent Information Systems

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