A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks

Alghamdi, Rubayyi; Bellaïche, Martine

doi:10.1016/j.cose.2022.103014

Cited by 29 publications

(11 citation statements)

References 16 publications

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“…In the paper [37], security and privacy issues have arisen as a result of the proliferation of IoT networks. Routing Protocol for Low-Power and Lossy Networks (RPL) assaults, such as wormhole attacks, are a common example of a threat to a network's security because of the havoc they wreak on the navigation information of a system.…”

Section: ) Wormhole Attackmentioning

confidence: 99%

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Abbas,

Ahmad,

Syed

et al. 2024

IEEE Access

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With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.

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Section: ) Wormhole Attackmentioning

confidence: 99%

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Abbas,

Ahmad,

Syed

et al. 2024

IEEE Access

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“…The authors of [ 24 ] proposed cascaded wormhole detection for an IoT-based network using deep learning. The attacks were evaluated based on their TPR, a 96.4% blackhole attack, 98.7% opportunistic attack, 98.7% DDoS attack, 99.9% sinkhole attack, and 98% wormhole attack, with an overall accuracy of 96%.…”

Section: Related Workmentioning

confidence: 99%

“…Additional papers [ 23 , 24 , 25 , 26 , 27 , 31 , 32 , 33 , 34 ] presented other approaches to detect DDoS attacks. These include using deep learning [ 25 ], traffic authentication [ 26 ], a cascaded federated deep learning framework [ 25 ], and artificial intelligence merged methods [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

“…In summary, the papers present various machine-learning techniques to detect DDoS attacks in various network systems such as mobile ad-hoc networks [ 21 ], IoT devices [ 24 ], and web servers [ 26 ]. In Table 1 , we compared the related works in terms of their methods, the drawback of their methods and the accuracy they achieved in their results.…”

Section: Related Workmentioning

confidence: 99%

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An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar

Huang

Javed

et al. 2023

Sensors

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Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

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“…Based on the DL technology, Alghamdi and Bellaiche [20] describe a cascaded wormhole detection method for Internet of Things networks (DTF). Using a federated strategy that ensures data security and privacy at the node level, (LSTM) deep learning models were trained.…”

Section: Related Workmentioning

confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

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Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

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A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks

Cited by 29 publications

References 16 publications

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

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