Software-Defined Networking (SDN) is a new way of thinking about networking in which the networking function is split into two planes: control plane and data plane. The aim of SDN is to give network managers greater control over network configuration, increased programmability, flexibility, and efficient use of network resources. These features have driven the rapid-fire uptake of SDN in today's communications networks, cloud and data center. However, all these appealing features can create additional security problems, increasing the attack surface and putting critical elements at risk of breaches. With the rapid emergence of SDN, network security and resilience are emerging top topics of researchers' interests. This paper provides a comprehensive survey of the SDN security by covering its architecture, key benefits and potential vulnerabilities. It explores potential attacks on the data plane, attacks on the communication channels outside the control plane, security challenges in the control plane, and existing approaches and proposed countermeasures from the literature. Moreover, the paper presents a survey of existing work, lists open challenges, research gaps and future research directions and implements some new trends related to secure, scalable and resilient SDN environments.
AJIT Karki, V. R, H. A. Akarte et al.· International journal of com...· 0 citations
Parkinson's Disease (PD) is a progressive neurodegenerative disorder, which also impacts speech and vocal characteristics at the initial stages of the disorder. This study proposes an explainable hybrid Bi-LSTM and XG-Boost for detection Parkinsons Disease using non-invasive voice recording, and is designed to be interpretable. The Bidirectional Long Short-Term Memory (Bi-LSTM) network is employed to learn deep latent representations from vocal biomarkers and the final classification is done by the XG-Boost network using both the deep latent representations and the handcrafted voice features. It includes Explainable Artificial Intelligence (XAI) methods like SHAP and LIME to improve interpretability and transparency. SHAP is to identify the globally important vocal features that affect the model prediction, while LIME gives the explanations of individual predictions by instances. The purpose of this work is not to develop a complete clinical implementation tool, as deployment in healthcare and real world will require a detailed clinical validation and regulatory clearance. Rather, in this research we will develop an explainable and transparent AI framework that can be the basis for future researchers and developers to design robust clinical support systems and healthcare applications. The proposed approach has the potential to incorporate the deep learning, ensemble learning and explainable AI concepts in an interpretable Parkinson's disease prediction.
T. Bhutia, Passang Tamang, Dewash Manger et al.· 2026 7th International Confe...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
