IoT Routing Attacks Detection Using Machine Learning Algorithms

Rabhi, Sana; Abbes, Tarek; Zarai, Faouzi

doi:10.1007/s11277-022-10022-7

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…WSNs are a cornerstone of the (IoT), and Rabhi et al [18] highlight their susceptibility to routing assaults in their presentation of the Routing power (RPL). We also offer a method for identifying three distinct forms of assault against RPL, and we highlight some recent research suggestions for doing so.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…If individuals are in tracing mode. Update the velocity and position based on PSO movement as formulated in Equation ( 17) -Equation (18) 𝑉 𝑖 (𝑡 + 1) = 𝜔𝑉 𝑖 (𝑡) + 𝑐 1 𝑟 1 (𝐶 𝑔 (𝑡) − 𝑋 𝑖 (𝑡)) + 𝑐 1 𝑟 1 (𝐶 𝑖 (𝑡) − 𝑋 𝑖 (𝑡))…”

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confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

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“…Machine learning IDSs can be broadly categorized into two types: (i) classificationbased [22][23][24] and (ii) anomaly-based [25]. Classification-based IDSs use machine learning algorithms to classify incoming data into different categories based on a set of features.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Psychogyios,

Papadakis,

Bourou

et al. 2024

Future Internet

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The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

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“…These attacks exploit properties of the protocol to inflict damage. Most potent of these attacks are Denial of Service (DoS) [11], [12], [14] and routing attacks [15], [16]. Specifically, RPL can be subject to several attacks when it transmits packets between nodes.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Denial of Service Attacks in RPL-Based IoT Environments: Trust-Based Approach

Azzedin

2023

IEEE Access

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In domains such as telehealth, intelligent transportation, and autonomous agriculture, ensuring secure routing of collected and exchanged data is paramount. Since its inception, there have been many research challenges for the RLP routing protocol that operates in resource-constrained environments and utilizes battery-powered IoT devices. Hence, researchers have focused on this crucial challenge by advising solutions to mitigate attacks that deplete nodes' energy and hence create energy gaps in the network. In this article, we study the impact of two energy exhaustion attacks (hello flooding and version number modification) on the RPL protocol and we present a novel mitigation solution based on behavioural trust. We present an in-depth study of the impact on radio energy consumption of the hello flooding and version number modification attacks in RPL as the number of network nodes increases. We showed that the impact of the former is localized to nodes in the vicinity of the attacker while the latter has a global impact that extends to the entire network. The obtained results from our simulations show that version number modification attack in particular has devastating impact on the network. We also propose a trust-based solution to mitigate these attacks and demonstrate its effectiveness. Accordingly, we conduct comparative study of these attacks and empirically investigate their impact on network performance by running extensive evaluation experiments. Our findings verify the effectiveness of our proposed trust system in mitigating both attacks.

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IoT Routing Attacks Detection Using Machine Learning Algorithms

Cited by 18 publications

References 19 publications

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Mitigating Denial of Service Attacks in RPL-Based IoT Environments: Trust-Based Approach

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