A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza, Salma; Ridouani, Mohammed; Salahdine, Fatima; Hayar, Aawatif

doi:10.3390/s22176477

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…This graph illustrates the model's ability to recognize various class In Table 4 and Fig. 10, the overall classification performance of ODL-MUDSS technique with recent models are given [21]. The results indicate that the SVM model offers poor performance whereas the LR, NB, and stacking techniques attain slightly boosted outcomes.…”

Section: Resultsmentioning

confidence: 95%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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Malicious user recognition for spectrum sensing in Cognitive Radio Networks (CRNs) is a serious safety feature to safeguard effective and trustworthy process of these systems. Spectrum sensing permits CRNs to identify and employ accessible spectrum bands. As well as it is available to prospective interference and mischievous actions. To preserve network integrity, recognition of malicious consumers is vital. Deep learning (DL) based malicious consumer classification powers advanced neural network frameworks to recognize and flag possible threats inside a network. By examining numerous amounts of information, DL techniques can distinguish patterns as well as anomalies that are connected with malicious user performance plus system intrusions, scams or irregular action. This technique provides flexibility benefit that permits a network to learn and develop in evolving threats. It also offers an effectual revenue of improving network security in the difficult and active digital landscape. Therefore, this article develops an Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing (ODL-MUDSS) in the CRN. The main intention of ODL-MUDSS model focused on automated identification and classification of MUs in CRN. To accomplish this, the ODL-MUDSS model primarily applies deep belief network (DBN) methodology for automated and accurate detection of MUs. In addition, recognition performance of DBN technique can be enhanced by use of sand cat swarm optimization (SCSO) algorithm and thereby improves the detection results. The performance validation of ODL-MUDSS technique is observed under different processes. The comprehensive outcomes stated enhanced performance of ODL-MUDSS model over other existing models with maximum accuracy of 97.75%, precision of 97.75%, recall of 97.75%, and F-score of 97.75%.

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Section: Resultsmentioning

confidence: 95%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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“…Additionally, they introduce a novel variant denoted as k, transforming the problem from optimizing the perturbation δ to optimizing k to circumvent the box constraints. The optimization problem is formulated by below Expressions (7) and (8).…”

Section: Carlini and Wagnermentioning

confidence: 99%

“…To detect and mitigate cyberattacks, Intrusion Detection Systems (IDSs) [4], Malware Detection Systems (MDSs) [5], and Device Identification Systems (DISs) [6] are often employed to monitor IoT network traffic and detect malicious activities [7][8][9]. ML [10,11] techniques, including Deep Learning (DL) [12,13], have shown promise in enhancing the effectiveness of these systems, by leveraging the ability of ML algorithms to learn from data and identify patterns that indicate anomalous behavior.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

Khazane,

Ridouani,

Salahdine

et al. 2024

Future Internet

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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.

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“…Several advanced technologies have been employed in the domain of deepfake detection, such as machine learning [83][84][85] and media forensics-based approaches [86]. However, it is widely acknowledged that deep learning-based models currently exhibit the most remarkable performance in discerning between fabricated and authentic digital media.…”

Section: Deep Learning Models For Deepfake Detectionmentioning

confidence: 99%

Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions

Naitali,

Ridouani,

Salahdine

et al. 2023

Computers

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Recent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extremely realistic and challenging to identify from authentic content. Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud. This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as associated challenges and future research trends. By synthesizing existing knowledge and research, this survey aims to facilitate further advancements in deepfake detection and mitigation strategies, ultimately fostering a safer and more trustworthy digital environment.

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A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Cited by 11 publications

References 46 publications

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions

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