A Novel QoS‐Oriented Intrusion Detection Mechanism for IoT Applications

Noorwali, Abdulfattah; Alvi, Ahmad Naseem; Khan, Mohammad Zubair; Javed, Muhammad Awais; Boulila, ‪Wadii; Pattanaik, Priyadarshini Adyasha

doi:10.1155/2021/9962697

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Noorwali et al, 2021 89 proposed an adversary detection technique for the IEEE 802.15.4 standard, which is a central medium access protocol utilized in WSN-based IoT applications. A soft-decision-based method is employed to detect collisions and fatigue attacks by determining collisions and effectively transmitting probabilities using the soft function scale.…”

Section: Data Link Layer Attacksmentioning

confidence: 99%

Wireless sensor network security: A recent review based on state-of-the-art works

Faris

Mahmud

Salleh

et al. 2023

International Journal of Engineering Business Management

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Wireless sensor networks (WSNs) are a major part of the telecommunications sector. WSN is applied in many aspects, including surveillance battlefields, patient medical monitoring, building automation, traffic control, environmental monitoring, and building intrusion monitoring. The WSN is made up of a vast number of sensor nodes, which are interconnected through a network. However, despite the growing usage of applications that rely on WSNs, they continue to suffer from restrictions, such as security issues and limited characteristics due to low memory and calculation power. Security issues lead to a lack of communication between sensors, wasting more energy. The need for efficient solutions has increased, especially with the rise of the Internet of Things, which relies on the effectiveness of WSNs. This review focuses on security issues by reviewing and addressing diverse types of WSN assaults that happened on each layer of the WSN that were published in security issues in the previous 3 years. As a consequence, this paper gives a taxonomy of security threats for each layer and different algorithmic solutions that numerous researchers who seek to counter this attack have explored. This study also presents a framework for constructing an intrusion detection system in the WSN by emphasising the drawbacks of each approach suggested by researchers to defend against specific forms of assault. In order to diminish the impact of this attack, this summary shows which attacks the majority of researchers have dealt with as well as which ones they have not yet addressed in their academic work.

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Section: Data Link Layer Attacksmentioning

confidence: 99%

Wireless sensor network security: A recent review based on state-of-the-art works

Faris

Mahmud

Salleh

et al. 2023

International Journal of Engineering Business Management

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“…Fig 1 presents a cyberattack scenario in which the botnet is used to launch a DDoS attack on an IIoT network, specifically targeting industrial servers. An intrusion detection system (IDS) is a sophisticated software employed to scrutinize network traffic and detect malevolent activities within the network [10]- [17]. Its primary purpose is to identify and raise alarms on any potentially malicious or unauthorized behavior occurring within the network [18], [19].…”

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confidence: 99%

MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Ullah,

Boulila,

Koubâa

et al. 2023

IEEE Access

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The increasing prevalence of the Industrial Internet of Things (IIoT) in industrial environments amplifies the potential for security breaches and compromises. To monitor IIoT networks, intrusion detection systems (IDS) have been introduced to detect malicious activities within the network flow, in which machine learning (ML) and deep learning (DL) play an important role. However, existing IDSs face challenges during training when dealing with imbalanced training data and a higher number of classes. These issues can significantly reduce the IDS's performance and may result in missed network attacks, especially those with fewer training samples. To address these challenges, this paper introduces a multi-head attention-based gated recurrent unit (MAGRU) that scrutinizes IIoT network traffic to detect malicious activities. In the proposed model, the multi-head attention (MA) has the ability to enhance the learning capability of the model to handle limited sample classes. The gated recurrent unit (GRU) is employed for the detection of IIoT network behavior. The proposed MAGRU is evaluated using two publicly available datasets, namely Edge-IIoTset and MQTTset. To validate the performance of the proposed MAGRU, various ML and DL models were implemented and compared against MAGRU using the same dataset. The proposed model outperformed the other models, achieving an average precision, recall, F1-score, and accuracy of 99.62%, 99.67%, 99.64%, and 99.97%, respectively, for the aforementioned datasets. These results demonstrate optimal performance in the detection of intrusions in IIoT networks.

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Cybersecurity Challenges and Implications for the Adoption of Cloud Computing and IoT: DDoS Attacks as an Example

Al-Tamimi,

Almoamari,

Nehme

et al. 2024

Advances in Information Security

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A Novel QoS‐Oriented Intrusion Detection Mechanism for IoT Applications

Cited by 6 publications

References 22 publications

Wireless sensor network security: A recent review based on state-of-the-art works

Wireless sensor network security: A recent review based on state-of-the-art works

MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Cybersecurity Challenges and Implications for the Adoption of Cloud Computing and IoT: DDoS Attacks as an Example

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