Dr Djamel Djenouri

Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs (2025)
Presentation / Conference Contribution

This paper proposes an integrated approach called FL-RSD, leveraging the key advantages of Federated Learning (FL) and Recursive Self-Distillation (RSD) for malware detection in the Internet of Vehicles (IoV). The proposed FL-RSD framework enhances m... Read More about Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs.

Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency (2025)
Journal Article

This survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments , focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key chal... Read More about Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency.

An integrated approach to mitigate poisoning attacks in federated learning frameworks (2025)
Presentation / Conference Contribution

While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (... Read More about An integrated approach to mitigate poisoning attacks in federated learning frameworks.

Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers (2024)
Journal Article

This research aims to find an optimal balance between privacy and performance in forecasting mental health sentiment. This paper investigates federated learning (FL) augmented with a novel data obfuscation (DO) technique, where synthetic data is used... Read More about Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers.

A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT (2024)
Presentation / Conference Contribution

Despite its distributed nature and being privacy-preserving by nature, Federated Learning (FL) is vulnerable to poisoning attacks in which malicious actors can inject fake model parameters or false data to compromise the learning process. This articl... Read More about A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database (2024)
Presentation / Conference Contribution

Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and... Read More about A novel hybrid framework for realistic UAV detection using a mixed RF signal database.

A survey on emerging blockchain technology platforms for securing the Internet of Things (2024)
Journal Article

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressin... Read More about A survey on emerging blockchain technology platforms for securing the Internet of Things.

TG-SPRED: Temporal graph for sensorial data PREDiction (2024)
Journal Article

This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network's operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readi... Read More about TG-SPRED: Temporal graph for sensorial data PREDiction.

Social web in IoT: Can evolutionary computation and clustering improve ontology matching for social web of things? (2023)
Journal Article

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we sug... Read More about Social web in IoT: Can evolutionary computation and clustering improve ontology matching for social web of things?.

DPFTT: Distributed particle filter for target tracking in the Internet of Things (2023)
Presentation / Conference Contribution

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (... Read More about DPFTT: Distributed particle filter for target tracking in the Internet of Things.

Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES (2023)
Presentation / Conference Contribution

This work targets a comprehensive model enabling energy-constrained IoT (Internet of Things) sensor devices to be inactive for extended periods while estimating their readings of real-time events. Although events seem semantically uncoupled, they are... Read More about Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES.

Knowledge guided deep learning for general-purpose computer vision applications (2023)
Presentation / Conference Contribution

This research targets general-purpose smart computer vision that eliminates reliance on domain-specific knowledge to reach adaptable generic models for flexible applications. It proposes a novel approach in which several deep learning models are trai... Read More about Knowledge guided deep learning for general-purpose computer vision applications.

A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting (2023)
Journal Article

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV m... Read More about A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting.

Deep learning for estimating sleeping sensor’s values in sustainable IoT applications (2022)
Presentation / Conference Contribution

The aim of this work is to develop a deep learning model that uses spatial correlation to enable turning turn off a subset of sensors while predicting their readings. This considerably saves the energy that would be consumed by those sensors both for... Read More about Deep learning for estimating sleeping sensor’s values in sustainable IoT applications.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads (2022)
Journal Article

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. T... Read More about Vehicle detection using improved region convolution neural network for accident prevention in smart roads.

Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Presentation / Conference Contribution

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Intelligent deep fusion network for anomaly identification in maritime transportation systems (2022)
Journal Article

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime dat... Read More about Intelligent deep fusion network for anomaly identification in maritime transportation systems.

Emergent deep learning for anomaly detection in internet of everything (2021)
Journal Article

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environment... Read More about Emergent deep learning for anomaly detection in internet of everything.

Dr Djamel Djenouri's Outputs (110)