ML-DDoS: A Blockchain-Based Multilevel DDoS Mitigation Mechanism for IoT Environments

Hayat, Rana Faisal; Aurangzeb, Sana; Aleem, Muhammad; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1109/tem.2022.3170519

Cited by 45 publications

(22 citation statements)

References 41 publications

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“…e proposed mechanism can detect coordinated replay attacks with full privacy. To prevent IoT devices and other computing resources from DDoS attacks, Hayat et al [28] proposed a Multilevel DDoS mitigation approach (ML-DDoS) based on blockchain. e results show that the proposed framework can accurately detect DDoS attacks in IoT, and has good performance in throughput, latency, and CPU utilization.…”

Section: Blockchain-based Traffic Detection Methodmentioning

confidence: 99%

A Blockchain-Enabled Trusted Protocol Based on Whole-Process User Behavior in 6G Network

Zhou

et al. 2022

Security and Communication Networks

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The access of massive users and devices in the 6G networks increases the risk of network attacks. Designing a trusted protocol to control user behavior can effectively improve the security capability of the network. However, most of the existing trusted protocols focus on unilateral user behavior and lack effective control over the whole process of user behavior. In this paper, we design a blockchain-enabled trusted protocol based on the whole-process user behavior. At first, we describe the Whole-Process User Behavior (WPUB) after the user accesses the network, and model the whole-process trusted control process. The proposed model establishes a trusted chain between user identity, access action, and communication traffic, and realizes the control of WPUB. Then, based on the proposed model, we design a whole-process trusted protocol with smart agents and smart contracts in combination with blockchain. Finally, we evaluate the designed protocol in the HyperLedger Fabric-based prototype system. Evaluations show that the proposed protocol can control the WPUB and reduce the risk of the network being attacked.

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Section: Blockchain-based Traffic Detection Methodmentioning

confidence: 99%

A Blockchain-Enabled Trusted Protocol Based on Whole-Process User Behavior in 6G Network

Zhou

et al. 2022

Security and Communication Networks

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“…By utilizing past data gatheblack for a particular activity to automatically optimize performance without iteration, ML may be utilized to enhance the performance of various IoT devices. [15][16][17][18][19][20][21] Specifically, the following points are the primary reasons why ML is significant in IoT applications: • The configurations that IoT systems frequently screen have a dynamic structure. As a result, it is critical to develop IoT systems that can effectively respond to these changes as a whole.…”

Section: Iotmentioning

confidence: 99%

“…The IoT enables the connection of millions of objects, including sensors and mobile phones/devices, to carry out numerous functions. By utilizing past data gatheblack for a particular activity to automatically optimize performance without iteration, ML may be utilized to enhance the performance of various IoT devices 15–21 . Specifically, the following points are the primary reasons why ML is significant in IoT applications: The configurations that IoT systems frequently screen have a dynamic structure.…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

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“…Compromised systems could be used to steal sensitive information and credentials and push malicious firmware updates. Hence there is a need to encompass added security and trust in the IoT environment 2 . Cybercriminals' increasing use of botnets can easily lead to stealing sensitive information and large complex Distributed Denial of Service (DDoS) attacks, which may vandalize a target website.…”

Section: Introductionmentioning

confidence: 99%

“…Hence there is a need to encompass added security and trust in the IoT environment. 2 Cybercriminals' increasing use of botnets can easily lead to stealing sensitive information and large complex Distributed Denial of Service (DDoS) attacks, which may vandalize a target website. DDoS attacks work by consuming resources and occupying bandwidth to take down websites.…”

Section: Introductionmentioning

confidence: 99%

SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

Priyadarshini

Mohanty

Alkhayyat

et al. 2023

Trans Emerging Tel Tech

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The Internet of Things (IoT) is connecting more devices every day. Security is critical to ensure that the devices operate in a trusted environment. The lack of proper IoT security encourages cybercriminals to target many smart devices across the network and gain sensitive information. Distributed Denial of Service (DDoS) attacks are common in the IoT infrastructure and involve hijacking IoT devices to consume resources and interrupt services. This may specifically vandalize the application running the service that the end users are trying to access (application layer DDoS attacks) or flood the network bandwidth leading to network failure (software defined network DDoS attacks). This article proposes a hybrid attention‐based bidirectional long short term memory (LSTM) with convolutional neural networks (CNN) to identify DDoS attacks in the application layer and SDN. We deploy several other machine learning models like logistic regression, decision trees, random forests, support vector machines, K‐nearest neighbors, extreme gradient boosting, artificial neural networks, CNN, LSTM, CNN‐LSTM to evaluate the performance of our proposed model. The evaluation metrics considered for the study are accuracy, precision, recall, and F‐1 score. The experimental analysis on multiple datasets exhibits that the proposed model performs the classification efficiently with an accuracy of 99.74% and 99.98%.

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ML-DDoS: A Blockchain-Based Multilevel DDoS Mitigation Mechanism for IoT Environments

Cited by 45 publications

References 41 publications

A Blockchain-Enabled Trusted Protocol Based on Whole-Process User Behavior in 6G Network

A Blockchain-Enabled Trusted Protocol Based on Whole-Process User Behavior in 6G Network

A systematic literature review of machine learning applications in IoT

SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

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