The growing prevalence of vulnerabilities in embedded devices poses a significant risk to critical infrastructure. While deep learning has advanced vulnerability classification, its effectiveness is often hindered by limitations in word representation. Traditional word embeddings struggle with out-of-vocabulary (OOV) words common in domain-specific reports, while pre-trained language models (PLMs), despite their contextual power, may lack specialized domain knowledge. To address these challenges, we propose a novel Two-Stream hybrid embedding architecture that combines Vuln2Vec, a custom domain-specific word embedding, with a large pre-trained language model (PLM) using a learnable weighted feature fusion. Our approach leverages the rich domain-specific vocabulary of Vuln2Vec to understand specialized terminology, while the PLM captures broader contextual relationships and effectively handles OOV words. We validate our method through rigorous experiments, including ablation studies and comparative analyses on vulnerability databases such as the National Vulnerability Database (NVD), the Chinese Vulnerability Database (CNNVD), and a challenging manually collected dataset. Our experiments demonstrate that the proposed hybrid embedding method achieves a state-of-the-art F1-score of 94.25% and an accuracy of 94.88% on the challenging test dataset, validating the superiority of fusing specialized and general-purpose knowledge for this critical task.

Object detection and classification is one of the core functions of Intelligence Transport Systems (ITS). It is typically based on extracted features and learning algorithms. Different approaches seem to be appropriate. Researchers should compare and evaluate existing approaches to apply the most efficient. In this paper, we propose a moving vehicle-detection vision system. Two solutions are examined in terms of performance and energy-efficient. The first is a classical Adaboost approach based on the Haar-like in feature extraction whereas the second handles a Local Binary Pattern descriptor that will undergo extraction with Adaboost classifier. Comparison results are illustrated based on the GTI vehicle image dataset. The most pertinent is the Haar-like +Adaboost, leading a DR of 90.1% instead of 87.9% for the LBP+Adaboost. However, LBP+Adaboost shows a low energy consumption, which is very important in any embedded systems.

Abstract Software‐defined networking (SDN) is a relatively new approach in network management that proposes to separate the network control (Control plane) and the forwarding process (Data plane) to optimize the network infrastructure and improve network performance, controllability, manageability and flexibility. However, like every technology, SDN has brought its own new challenges in terms of security and scalability which are very important aspects that should be considered to design and build a resilient architecture in order to meet carrier grade network requirements. In this paper, we propose a secure SDN architecture with IEEE 802.1X port‐based authentication where we also consider the controller's scalability issue by devolving the access control capability to the data plane. In this way, we reduce the high demand and the workload on the SDN controller. Our proposed model presents a novel SDN network architecture and logical network segmentation which provides an optimal and secure network access with low latency. We have implemented and tested our architecture to show its performance (authentication delays). Copyright © 2016 John Wiley & Sons, Ltd.

Abstract With the advent of cloud computing, many new networking concepts have been introduced to simplify network management and bring innovation through network programmability. The emergence of the software‐defined networking (SDN) paradigm is one of these adopted concepts in the cloud model so as to eliminate the network infrastructure maintenance processes and guarantee easy management. In this fashion, SDN offers real‐time performance and responds to high availability requirements. However, this new emerging paradigm has been facing many technological hurdles; some of them are inherent, while others are inherited from existing adopted technologies. In this paper, our purpose is to shed light on SDN related issues and give insight into the challenges facing the future of this revolutionary network model, from both protocol and architecture perspectives. Additionally, we aim to present different existing solutions and mitigation techniques that address SDN scalability, elasticity, dependability, reliability, high availability, resiliency, security, and performance concerns. Copyright © 2017 John Wiley & Sons, Ltd.