𝙎𝙋𝙀𝘾𝙄𝘼𝙇 𝙄𝙎𝙎𝙐𝙀 𝙤𝙣 𝘼𝙙𝙖𝙥𝙩𝙞𝙫𝙚 𝙄𝙣𝙩𝙧𝙪𝙨𝙞𝙤𝙣 𝘿𝙚𝙩𝙚𝙘𝙩𝙞𝙤𝙣 𝙎𝙮𝙨𝙩𝙚𝙢 𝙪𝙨𝙞𝙣𝙜 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙞𝙣 𝙒𝙞𝙧𝙚𝙡𝙚𝙨𝙨 𝙎𝙚𝙣𝙨𝙤𝙧 𝙉𝙚𝙩𝙬𝙤𝙧𝙠𝙨

This special issue in 𝗢𝗽𝗲𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 (𝗜𝗙 𝟮𝟬𝟮𝟮: 𝟭.𝟱) focuses on A vast variety of detection nodes make up a Wireless Sensor Networks (WSN), which collects and sends data to a central point. However, distribution tactics, communications channels, and limited supply nodes provide a number of safety concerns for WSN. Therefore, in order to enhance the security aspects of wireless sensor networks, it is imperative to identify unauthorized access. These functions are given to the network-by-network intrusion detection systems, and they are necessary for all interactions on the network. Intrusion Detection Systems (IDS) frequently include Machine Learning (ML) approaches; yet, ML techniques' effectiveness is subpar when managing unbalanced assaults. According to their inadequate rechargeable energy supply, small bandwidth assistance, data travel over multiple hop nodes, reliance on intermediaries or other nodes, dispersed nature, and organization, WSN nodes are vulnerable to a variety of security-related attacks.

The widespread use of WSN presents issues for maintaining their secrecy, credibility, and reliability. Intrusion detection serves as a strong first line of defense for the WSNs and is a crucial active defense mechanism. The disparity between dependable information distribution and restricted sensing power, as well as the dispute over the impact of detection and the scarcity of network resources, must be balanced given the special characteristics of WSN. WSN may overcome the drawbacks of conventional monitoring techniques, which considerably lower the expense of detection while also streamlining the laborious procedure. WSN assaults are visible at every model tier. Because of this, wireless sensor nodes face a variety of problems. Some are connected to technical concerns, while others arise as a result of assaults. To identify and stop threats, defensive and network monitoring systems must be built. The ability to create an affordable ML model for quick intrusion detection and prevention in border regions using WSN is made possible by the notable rise in appeal of accessible ML algorithms and the sharp rise in the utilization of artificial data. However, extra protection precautions need to be taken since WSNs differ from typical networks in terms of design and technology. To guarantee WSN security, an IDS is modelled in this work. A hybrid architecture that combines the use of authorization, overuse, and anomaly-based detection techniques with intrusion detection systems is suggested because these techniques alone are unable to offer reliability. A compact intelligent intrusion detection model for WSN is proposed in this special issue. With the use of anomalous network data, our model can detect attack behaviors in WSNs with speed and accuracy. Therefore, the supervised neural network satisfies the criterion of convenient data analysis in contrast with different machine learning methods

Potential topics include, but are not limited to:
• Machine learning approaches for Intrusion detection in wireless sensor networks.
• A hierarchical neural network-based intrusion detection solution for wireless sensor networks.
• A cooperative intrusion detection technology optimized for wireless sensor networks.
• An extensively artificial network-based self-adaptive technique for wireless intrusion detection.
• A thin-layer proactive intrusion detection structure for wireless sensor networks.
• Regarding the viability of machine learning for intrusion detection in sensor networks.
• Efficient supporting vector machine-based intrusion detection method for wireless sensor networks.
• Machine learning-based intrusion detection system with optimization support in wireless sensor networks.
• Designing Intrusion Detection Systems in Wireless Sensor Networks via Machine Learning Technologies.
• A wireless Internet of things intrusion detection solution powered by machine learning.
• Developing autonomous machine learning network intrusion detection solutions for abuse.

Authors are requested to submit their full revised papers complying with the general scope of the journal. The submitted papers will undergo the standard peer-review process before they can be accepted. Notification of acceptance will be communicated as we progress with the review process.

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𝑮𝑼𝑬𝑺𝑻 𝑬𝑫𝑰𝑻𝑶𝑹𝑺

Tarek Moulahi, Qassim University, Saudi Arabia.
Rateb Jabbar, Qatar University, Qatar.
Musab Al-Ghadi, La Rochelle University, France.

𝘼𝘿𝙑𝙄𝙎𝙊𝙍𝙔 𝙀𝘿𝙄𝙏𝙊𝙍
Christos Anagnostopoulos, University of Glasgow, United Kingdom

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𝑫𝑬𝑨𝑫𝑳𝑰𝑵𝑬

The deadline for submissions is 𝗝𝗨𝗡𝗘 𝟭𝟱, 𝟮𝟬𝟮𝟰, but individual papers will be reviewed and published online on an ongoing basis.

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𝑯𝑶𝑾 𝑻𝑶 𝑺𝑼𝑩𝑴𝑰𝑻

All submissions to the Special Issue must be made electronically via the online submission system Editorial Manager:

𝗵𝘁𝘁𝗽𝘀://𝘄𝘄𝘄.𝗲𝗱𝗶𝘁𝗼𝗿𝗶𝗮𝗹𝗺𝗮𝗻𝗮𝗴𝗲𝗿.𝗰𝗼𝗺/𝗼𝗽𝗲𝗻𝗰𝘀/𝗱𝗲𝗳𝗮𝘂𝗹𝘁𝟮.𝗮𝘀𝗽𝘅

Please choose the article type “𝙎𝙄: 𝘼𝙙𝙖𝙥𝙩𝙞𝙫𝙚 𝙄𝙣𝙩𝙧𝙪𝙨𝙞𝙤𝙣 𝘿𝙚𝙩𝙚𝙘𝙩𝙞𝙤𝙣 𝙎𝙮𝙨𝙩𝙚𝙢 𝙪𝙨𝙞𝙣𝙜 𝙈𝙇 𝙞𝙣 𝙒𝙎𝙉”.

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𝑪𝑶𝑵𝑻𝑨𝑪𝑻

𝗼𝗽𝗲𝗻𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀𝗰𝗶𝗲𝗻𝗰𝗲@𝗱𝗲𝗴𝗿𝘂𝘆𝘁𝗲𝗿.𝗰𝗼𝗺

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𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝗽𝗹𝗲𝗮𝘀𝗲 𝘃𝗶𝘀𝗶𝘁 𝗼𝘂𝗿 𝘄𝗲𝗯𝘀𝗶𝘁𝗲.

https://www.degruyter.com/journal/key/comp/html#overview