Ensemble Feature Selection Approach for Detecting Denial of Service Attacks in RPL Networks

Al-Amiedy, Taief Alaa; Anbar, Mohammed; Belaton, Bahari; Kabla, Arkan Hamoodi; Khudayer, Baidaa Hamza

doi:10.1007/978-981-16-8059-5_21

Cited by 5 publications

(8 citation statements)

References 48 publications

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“…The solution has been evaluated through various metrics such as false positive and false negative to estimate its accuracy. Authors in [41] focused on denial of service attacks by proposing a solution based on three bio-inspired algorithms to obtain an optimal set of features for enhancing the detection accuracy. They implemented and SVM (Support Vector Machine) as a classification algorithm.…”

Section: Related Workmentioning

confidence: 99%

Parent supervision lightweight solution against version number attacks for IoT networks

Rouissat

Belkheir

Mokaddem

2023

Preprint

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With the rapid involvment of IoT networks in almost all aspects of our daily life, the security concern is being more challenging to protect the enormous amount of the data exchanged over theses networks. Statisitcs reveal that threats against IoT networks are more and more growing , while DoS attacks are taking the lead. However, mitigation solutions against attacks targetting IoT networks must be developped while taking into consideration the network scalability, but also they should fit with the limited resources of IoT devices in terms of processing, storage, energy efficiency, and connectivity-loss. For thi aim, the present work proposes a lightweight detection and mitigation solution against the version number attack "VNA" which is considered as one among the haitful DoS attacks against IoT networks. The proposed decentralized algorithm targets the edge part of the netowrk. Upon an illigitimate incremented version number the sink node is informed via an alert DAO message by the malicious node parent, and it disseminates this information through the overall network to advertise the malicois originator. Beside its efficiency, the proposed algorithm does not involve any encryption algorithm, it does not generale any heavyweight computation, nor a specific extra node is needed for monitoring the network. Simulations were carried out to demonstrate the effectiveness of the solution in terms of control overhead, energy efficiency, latency, and memory footrpint.

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Section: Related Workmentioning

confidence: 99%

Parent supervision lightweight solution against version number attacks for IoT networks

Rouissat

Belkheir

Mokaddem

2023

Preprint

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“…Therefore, several protocols have been devised and standardized to allow and manage the communication amongst LLN's resource-constrained devices. One of the most popular proposed protocols for routing purposes in LLNs is RPL [11].…”

Section: Routing Protocol For Low Power and Lossy Network (Rpl)mentioning

confidence: 99%

“…The OF determines numerous metrics, such as rank of nodes, selection of the parent node, and route optimization. The versatility of RPL to interact with many limited devices is the primary reason for its adoption in LLNs [11].…”

Section: Routing Protocol For Low Power and Lossy Network (Rpl)mentioning

confidence: 99%

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Al-Amiedy

Anbar

Belaton

et al. 2022

Sensors

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The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

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“…It also provides more excellent features flexibility where it is easy to select the most significant ones. Moreover, it gives a comprehensive understanding of the dataset [30] [31].…”

Section: Simplicity and Speeding Up Of The Solution Via Featurementioning

confidence: 99%

Eth-PSD: A Machine Learning-Based Phishing Scam Detection Approach in Ethereum

et al. 2022

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Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminals' attention to launch blockchain-based attacks such as Ponzi schemes, Scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated and balanced dataset that can be used for evaluating Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.

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Ensemble Feature Selection Approach for Detecting Denial of Service Attacks in RPL Networks

Cited by 5 publications

References 48 publications

Parent supervision lightweight solution against version number attacks for IoT networks

Parent supervision lightweight solution against version number attacks for IoT networks

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Eth-PSD: A Machine Learning-Based Phishing Scam Detection Approach in Ethereum

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