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Special Issue Editors

Dr. Fatima Salahdine

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Guest Editor

The William States Lee College of Engineering, Electrical and Computer Engineering Department, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: spectrum sensing; compressive sensing; cognitive radio; wireless communication; cybersecurity; machine learning; LoRa, internet of things; federated learning; adversarial attacks; edge computing

Dr. Hassan El Alami

E-Mail Website
Guest Editor

Department of Computer Science and Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Interests: wireless communication and networking; security; autonomous systems; internet of things; wireless sensor networks; smart grids; modeling; optimization; artificial intelligence

Dr. Mohammed Ridouani

E-Mail Website
Guest Editor

Computer Sciences department in High School of Technology at the Hassan II University, Casablanca 20000, Morocco
Interests: wireless communications; machine learning; data science & smart system

Special Issue Information

Dear Collogues,

5G cooperative cognitive radio continues to be a subject of great interest to researchers in wireless communications. It mitigates the radio spectrum scarcity by enabling opportunistic access to the spectrum. With spectrum sensing techniques, unlicensed users detect the available spectrum and use it for their transmissions without interfering with the licensed users.

In cooperative scenarios, unlicensed users collaborate and report their sensing results to a fusion center for the final decision about the spectrum occupancy. However, malicious users can easily interfere by eavesdropping or reporting falsified measurements to impact the sensing decision. These attacks negatively impact spectrum sensing accuracy. Examples of these attacks include primary user emulation, belief manipulation, eavesdropping, and malicious traffic injection. Therefore, detecting and effectively mitigating these attacks is required toward securing the cooperative spectrum sensing.

Artificial intelligence technology has been heralded as the revolutionary technology needed to successfully solve any problem of today’s networks. Integrating artificial intelligence into 5G networks allows efficiently detecting the presence of malicious users and other security concerns facing the 5G cooperative cognitive radio networks. In this context, this Special Issue is an opportunity to investigate how artificial intelligence can detect and mitigate security challenges facing cooperative spectrum sensing.

Dr. Fatima Salahdine
Dr. Hassan El Alami
Dr. Mohammed Ridouani
Guest Editors

Manuscript Submission Information

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Keywords

5G and beyond
cooperative networks
cognitive radio networks
security
artificial intelligence
machine learning
deep learning
federated learning

Published Papers (2 papers)

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Research

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16 pages, 457 KiB

Open AccessArticle

Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework

by Yves Adou, Ekaterina Markova and Yuliya Gaidamaka

Future Internet 2022, 14(10), 299; https://doi.org/10.3390/fi14100299 - 18 Oct 2022

Cited by 5 | Viewed by 3434

Abstract

The Network Slicing (NS) technology, recognized as one of the key enabling features of Fifth Generation (5G) wireless systems, provides very flexible ways to efficiently accommodate common physical infrastructures, e.g., Base Station (BS), multiple logical networks referred to as Network Slice Instances (NSIs). To ensure the required Quality of Service (QoS) levels, the NS-technology relies on classical Resource Reservation (RR) or Service Prioritization schemes. Thus, the current paper aims to propose a Preemption-based Prioritization (PP) scheme “merging” the classical RR and Service Prioritization schemes. The proposed PP-scheme efficiency is evaluated or estimated given a Queueing system (QS) model analyzing the operation of multiple NSIs with various requirements at common 5G BSs. As a key result, the proposed PP-scheme can provide up to 100% gain in terms of blocking probabilities of arriving requests with respect to some baseline. Full article

(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)

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Review

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42 pages, 2733 KiB

Open AccessReview

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

by Hassan Khazane, Mohammed Ridouani, Fatima Salahdine and Naima Kaabouch

Future Internet 2024, 16(1), 32; https://doi.org/10.3390/fi16010032 - 19 Jan 2024

Cited by 2 | Viewed by 1932

Abstract

With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks. Full article

(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)

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