Our scientific publications
Our teams publish, prototype and drive innovation.
14 publication(s)
FORT-RAJ: a fisheye-optimized deep learning model for real-time trajectory prediction
… 24 showcases the model’s performance in a previously unseen room within our Caplogy premises, providing insight into its generalization ability to generalize across different spatial …
S Bouzayane, M Kahouadji, B Magnier
Multi-level Architecture for IoT-based Android Ransomware Detection and Family Attribution
Due to the proliferation of the Internet of Things (IoT), Android-based IoT devices are increasingly targeted by sophisticated ransomware threats. To address this issue, we propose a hierarchical architecture for ransomware detection and family attribution that leverages solely API call sequences extracted from Android apps. The architecture is designed to analyze a continuous stream of apps. It comprises three sequential levels: (1) Malware detection to distinguish benign apps from malware, (2) Ransomware detection to differentiate ransomware from other malware types, and (3) Ransomware family attribution to assign detected ransomware to its actual family. This architecture is novel in that it differs from the single-level approaches proposed in the literature and considers all types of apps, including benign, ransomware, and other malware types. In addition, we extensively evaluated the architecture on the CICAndMal2017 dataset by combining different models and data pre-processing methods, which enables the evaluation of a total of 135 machine learning workflows across all levels. The evaluation results show accuracies of 92% and 100% were obtained for ransomware detection and ransomware family attribution, respectively.
I Gharbi, A Agarwal, A Derhab
Hybrid LSTM-BiLSTM Approach for DDoS Attack Forecasting
Distributed Denial of Service (DDoS) attacks remain a critical threat to the stability and security of networked systems. To address the challenge of predicting such attacks in advance, …
Y Yakhlaf, R Bekkouche, H Garbouge
Visual-HAR: A Real-Time Human Activity Recognition Model Based on Visual Sensors
The paper presents a human activity recognition model, Visual-HAR, which leverages visual sensors to identify activities of elderly individuals. The goal of Visual-HAR is to accurately and promptly detect whether an elderly person is in danger by recognizing their activities in real time. This model utilizes convolutional neural networks (CNNs) and focuses on improving the ConViVit model through hyperparameter optimization. The performance of both Visual-HAR and Con-ViVit models was evaluated using three human action datasets: NTU RGB+D60, PKU-MMD, and PKU-MMD2. The evaluation criteria included both accuracy and execution time. The results indicated that Visual-HAR significantly outperformed ConViVit with an average testing performance improvement of 7.41% and a reduction in execution time by at least 36%, which is critical for real-time decision-making. These findings highlight Visual-HAR's potential as a reliable tool for monitoring elderly individuals.
A Sabrina, B Sarra, M Khawla
Anticipating Cyber Threats: Deep Learning Approaches for DDoS Attacks Forecasting
This paper investigates the application of deep learning (DL) techniques for DDoS attacks forecasting (i.e., predicting future attacks beforehand). We adopted a uniform modeling approach that allowed us to compare the performances of various DL algorithms, namely Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The results show that both short-term (few seconds ahead) and mid-term (up to 20 seconds ahead) DDoS traffic prediction with a very high accuracy were possible. In addition, the results showed that LSTM model outperforms both RNN and GRU, as well as machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (KNN).
DB Ali, M Belaoued, S Dawaliby
Fort-raj: a hybrid fisheye model for real-time pedestrian trajectory prediction
This paper introduces FORT-RAJ, a hybrid model designed for pedestrian trajectory prediction in the context of top-view fisheye images. To achieve this, FORT-RAJ merges the FORT (Fisheye Online Realtime Tracking) algorithm, which tracks people using fisheye cameras without prediction capabilities, with the GATraj model, known for trajectory prediction but not yet adapted for fisheye images. The proposed method, FORT-RAJ, is designed to detect pedestrians, track their trajectories, and predict their future positions. It leverages the wide field of view of fisheye cameras while addressing the distortions inherent in such images. The experiments demonstrated that the FORT-RAJ model performs satisfactorily on fisheye images, achieving an Average Displacement Error (ADE) of 0.38 meters and an Final Displacement Error (FDE) of 0.42 meters.
Y Amrouche, S Bouzayane
Parca: proactive anti-ransomware cybersecurity approach
Modern ransomware implement innovative techniques and tactics to bypass existing security measures. Hence, predicting and forecasting such kind of malware is crucial for enhancing the overall cybersecurity posture in an increasingly digital and interconnected world. In this paper, we propose a proactive approach for predicting ransomware attacks. We integrated a dynamic deep learning algorithm for analyzing memory-based features. This allows us to detect the existence of ransomware indicators regardless of the usage of obfuscation techniques, such as code encryption. Thus, offering advantages in terms of obfuscation resistance, realtime insights threat analysis and adaptability to evolving ransomware threats landscape. Experimental results using recent datasets demonstrate the effectiveness of the proposed approach in identifying modern ransomware samples with a weighted average of 99.98%, 99.96%, 100%, and 99.98% for accuracy, precision, recall and fl-score respectively.
A Djenna, M Belaoued, N Lifa, DE Moualdi
Real-Time Human Activity Recognition Using Wi-Fi CSI: A Deep CNN Architecture Approach
Human activity recognition (HAR) using Wi-Fi signals has gained significant attention due to its non-invasive nature, ubiquity, and respect for privacy, in contrast to camera-based systems. This study aims to develop a robust deep learning model for real-time HAR using Channel State Information (CSI) from Wi-Fi signals. We propose a deep Convolutional Neural Network (CNN) architecture incorporating an attention mechanism to process CSI data and classify various human activities. Our model was trained and tested using a comprehensive dataset of Wi-Fi CSI, employing advanced data augmentation techniques to enhance model generalization. The proposed model achieved an impressive accuracy of 99.69%, significantly outperforming existing approaches in terms of precision, recall, and F1 score. These findings demonstrate the potential of using Wi-Fi CSI with deep CNNs and attention mechanisms for accurate and real-time HAR, paving the way for applications in smart homes, healthcare monitoring, and other domains requiring efficient and privacy-preserving activity recognition. Future work will explore adapting this methodology to a variety of datasets and integrating an emotion recognition system to further expand its capabilities.
M Karim, S Imène, G Aymen
Comparative analysis of conventional object detection models on fisheye images
Fisheye cameras are increasingly used for object detection and tracking across various sectors. However, fish-eye images present distortions that complicate their processing compared to flat images. Two main approaches exist to tackle this challenge: calibrating fisheye images to convert them into flat images before applying traditional image processing models, or directly employing these models on fisheye images. In this study, we investigated both approaches using two fisheye image databases. Our findings indicate that calibration results in information loss and inadequate time savings. Furthermore, traditional models, despite optimization efforts, exhibit limited performance of approximately 20%, emphasizing the necessity for developing specific algorithms tailored to this technology.
B Sarra, A Arezki, M Houssam
ConViViT-A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition
The Transformer architecture has gained significant popularity in computer vision tasks due to its capacity to generalize and capture long-range dependencies. This characteristic makes it well-suited for generating spatiotemporal tokens from videos. On the other hand, convolutions serve as the fundamental backbone for processing images and videos, as they efficiently aggregate information within small local neighborhoods to create spatial tokens that describe the spatial dimension of a video. While both CNN-based architectures and pure transformer architectures are extensively studied and utilized by researchers, the effective combination of these two backbones has not received comparable attention in the field of activity recognition. In this research, we propose a novel approach that leverages the strengths of both CNNs and Transformers in an hybrid architecture for performing activity recognition using RGB videos. Specifically, we suggest employing a CNN network to enhance the video representation by generating a 128-channel video that effectively separates the human performing the activity from the background. Subsequently, the output of the CNN module is fed into a transformer to extract spatiotemporal tokens, which are then used for classification purposes. Our architecture has achieved new SOTA results with 90.05 %, 99.6%, and 95.09% on HMDB51. UCF101. and ETRI-Activity3D respectively.
DR Reda, F Chaieb, H Drira
Système de détection de la fraude financière à l'aide d'approches et de techniques d'intelligence artificielle
La fraude financière est devenue un défi majeur à l'ère numérique moderne, posant des menaces à la stabilité économique, à la confiance des clients et à l'intégrité des systèmes financiers. Les approches traditionnelles basées sur des règles et guidées par des experts pour la détection de la fraude ont montré des limites dans la résolution de la nature dynamique et évolutive des activités frauduleuses. Cette thèse présente une exploration approfondie d'un nouveau paradigme qui combine la puissance de l'Intelligence Artificielle (IA) basée sur la Connaissance et des techniques d'Apprentissage Automatique (ML) pour une détection efficace et robuste des fraudes financières.L'objectif principal de cette recherche est double : d'une part, examiner la viabilité de l'utilisation des méthodes ML et DL pour détecter la fraude financière et d'autre part, évaluer l'efficacité d'une approche basée sur l'IA basée sur la Connaissance pour traiter les subtilités du paysage frauduleux en évolution. La recherche commence par proposer deux hypothèses : premièrement, que les méthodes ML et DL peuvent identifier efficacement les schémas frauduleux et deuxièmement, qu'une approche basée sur l'IA basée sur la Connaissance offre un cadre flexible pour intégrer des connaissances spécifiques au domaine pour améliorer la détection de la fraude.Pour tester ces hypothèses, un cadre expérimental approfondi est utilisé, impliquant divers algorithmes ML tels que XGBoost, Random Forest, les réseaux neuronau
A Hussaini
Deep learning for windows malware analysis
Malwares, such as ransomware, Trojans, spyware, and botnets, are the most common cyber-threats that can cause significant damages for organizations, governments, and individuals. …
M Belaoued, A Derhab, N Chekkai, C Ramdane
Cyber-attack Proactive Defense Using Multivariate Time Series and Machine Learning with Fuzzy Inference-based Decision System
… belaoued@ caplogy. com 4 Caplogy, Poissy, France … The authors would like to thank Caplogy for supporting this work, which is the result of their collaboration in 2022. …
M Belaoued
The NP-completeness of quay crane scheduling problem
This paper discusses the computational complexity of the quay crane scheduling problem (QCSP) in a maritime port. To prove that a problem is NP-complete, there should be no polynomial time algorithm for the exact solution, and only heuristic approaches are used to obtain near-optimal solutions but in reasonable time complexity. To address this, first we formulate the QCSP as a mixed integer linear programming to solve it to optimal, and next we theoretically prove that the examined problem is NP-complete.
A Skaf, S Dawaliby, A Aberkane
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