A systematic literature review on attacks defense mechanisms in RPL-based 6LoWPAN of Internet of Things

Al-Amiedy, Taief Alaa; Anbar, Mohammed; Belaton, Bahari; Bahashwan, Abdullah Ahmed; Hasbullah, Iznan H.; Aladaileh, Mohammad Adnan; Mukhaini, Ghada AL

doi:10.1016/j.iot.2023.100741

Cited by 33 publications

(12 citation statements)

References 74 publications

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“…The attacks that can occur on RPL version numbers are mentioned in [10][11][12]. A probabilistic attacking model measures the potential impact, which analyzes power consumption, packet delivery ratio, delay, and control packet overhead.…”

Section: Lecture Survey and Contributionmentioning

confidence: 99%

DDoS Analysis: The Effect of Version Number Attack in RPL

Sayed,

Abdelaziz,

Hussein

et al. 2024

Preprint

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This paper examines the Version Number Attack (VNA), which is a specialized form of Denial of Service (DoS) threat, within the context of the Routing Protocol for Low Power and Lossy Networks (RPL) in Wireless Sensor Networks (WSNs). The research explores the dynamics and impacts of VNAs conducted by multiple attackers, including an in-depth look at the complex Hulk DDoS attack variant. The study systematically assesses instantaneous power consumption, average radio duty cycle, and overall power consumption.By providing a detailed analysis of the threat landscape, the study offers valuable insights into the vulnerabilities of RPL-based networks. It serves as a foundational study for strategies for behavior advanced multi-attacker scenarios.

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Section: Lecture Survey and Contributionmentioning

confidence: 99%

DDoS Analysis: The Effect of Version Number Attack in RPL

Sayed,

Abdelaziz,

Hussein

et al. 2024

Preprint

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“…Moreover, the rigidity of the conventional network decreases elasticity and increases operational expenses. As a result, those challenges impede the momentum of emerging technologies, including cloud computing, IoT technologies, and big data, which progressively demand more bandwidth, flexibility, and manageability [1,2]. Under those circumstances, SDN [3] emerged and was touted as an innovative networking model capable of handling and addressing the growing demands of next-generation networks and emerging technologies.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Bahashwan

Anbar

Manickam

et al. 2023

Sensors

Self Cite

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Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

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“…IoT networks are more vulnerable to attacks because of the wide variety of connected devices, each of which may have different processing speeds, storage capacities, and security mechanisms. These gadgets’ wireless communication methods are also susceptible to interference and eavesdropping, which might compromise sensitive information and personal privacy [ 4 ]. Furthermore, IoT-specific attacks, such as Mirai [ 5 ] and Hajime [ 6 ], pose significant risks, including damage to reputation and consumer trust.…”

Section: Introductionmentioning

confidence: 99%

“…However, regularly updating IoT devices with security patches, implementing data encryption, and maintaining backups are vital practices to ensure ongoing security. By adopting these measures, we can enhance the resilience of IoT networks and safeguard the integrity and privacy of user data [ 4 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

Alabsi

Anbar

Rihan

2023

Sensors

Self Cite

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The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic. The second CNN utilizes the features identified by the first CNN to build a robust detection model that accurately detects IoT attacks. The proposed approach is evaluated using the BoT IoT 2020 dataset. The results reveal that the proposed approach achieves 98.04% detection accuracy, 98.09% precision, 99.85% recall, 98.96% recall, and a 1.93% false positive rate (FPR). Furthermore, the proposed approach is compared with other deep learning algorithms and feature selection methods; the results show that it outperforms these algorithms.

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A systematic literature review on attacks defense mechanisms in RPL-based 6LoWPAN of Internet of Things

Cited by 33 publications

References 74 publications

DDoS Analysis: The Effect of Version Number Attack in RPL

DDoS Analysis: The Effect of Version Number Attack in RPL

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

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