Internet of Things (IoT) security dataset evolution: Challenges and future directions

Kaur, Barjinder Pal; Dadkhah, Sajjad; Shoeleh, Farzaneh; Neto, Euclides Carlos Pinto; Xiong, Pulei; Iqbal, Shahrear; Lamontagne, Philippe; Ray, Suprio; Ghorbani, Ali A.

doi:10.1016/j.iot.2023.100780

Cited by 60 publications

(18 citation statements)

References 79 publications

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“…The survey on intrusion detection using machine learning by S. V. N. Santhosh Kumar et al [6], S. Fraihat et al [21], and B. Kaur et al [22] supports the hypothesis of this paper. The recent research in intrusion detection using IoT focuses on performance improvement while ignoring the development of application models.…”

Section: Literature Reviewsupporting

confidence: 79%

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A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Alazab,

Awajan,

Alazzam

et al. 2024

Sensors

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The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available.

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Section: Literature Reviewsupporting

confidence: 79%

“…That is why the He weight initialization method has been used in this paper [42]. It is defined by Equation (22), where N (0, 2 n input ) is the Gaussian distribution in which the mean is zero, and 2 n input is the standard deviation.…”

Section: Weight Initializationmentioning

confidence: 99%

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Alazab,

Awajan,

Alazzam

et al. 2024

Sensors

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show abstract

“…Additionally, in order to demonstrate whether the model created using this dataset can be used to detect the most recent attacks, it is also preferable to identify the most recent types of attacks in the dataset. This is done by highlighting the various attack types that can affect IoT systems [25] as well as the vulnerable system layers [26]. Value: a list of the available attacks.…”

Section: A Novel Framework For Selecting Iot Datasetmentioning

confidence: 99%

A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems

Arief,

Gunawan,

Septiadi

et al. 2024

IJ-AI

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To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.

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“…However, in the study, the authors failed to discuss the potential impact of deploying emerging technologies as a means of ensuring the security of IIoT networks. In a study by Kaur et al [24] attacks, datasets, as well as machine learning algorithms and structures employed in the context of intrusion detection systems for IoT devices were highlighted. A categorisation of attacks targeting IoT devices across various layers and protocols was done.…”

Section: Related Workmentioning

confidence: 99%

Trust-Based Approaches Towards Enhancing IoT Security: A Systematic Literature Review

Okporokpo,

Olajide,

Ajienka

et al. 2023

Cryptography and Blockchain

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The continuous rise in the adoption of emerging technologies such as Internet of Things (IoT) by businesses has brought unprecedented opportunities for innovation and growth. However, due to the distinct characteristics of these emerging IoT technologies like real-time data processing, Self-configuration, interoperability, and scalability, they have also introduced some unique cybersecurity challenges, such as malware attacks, advanced persistent threats (APTs), DoS /DDoS (Denial of Service & Distributed Denial of Service attacks) and insider threats. As a result of these challenges, there is an increased need for improved cybersecurity approaches and efficient management solutions to ensure the privacy and security of communication within IoT networks. One proposed security approach is the utilization of trust-based systems and is the focus of this study. This research paper presents a systematic literature review on the Trustbased cybersecurity security approaches for IoT. A total of 23 articles were identified that satisfy the review criteria. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats and grouped them into three major categories, namely: Observation-Based, Knowledge-Based & Cluster-Based systems. Finally, several open issues were highlighted, and future research directions presented.

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Internet of Things (IoT) security dataset evolution: Challenges and future directions

Cited by 60 publications

References 79 publications

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems

Trust-Based Approaches Towards Enhancing IoT Security: A Systematic Literature Review

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