iCAD: information-Centric network Architecture for DDoS Protection in the Smart Grid

Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments to obtain efficient, reliable, and resilient solutions. Quantum computing (QC) is presented as a strong candidate to help MLs reach their best performance especially for cybersecurity issues and digital defense. This paper presents quantum support vector machine (QSVM) model to detect distributed denial of service (DDoS) attacks on smart micro-grid (SMG). An evaluation of our approach against a real dataset of DDoS attack instances shows the effectiveness of our proposed model. Finally, conclusions and some open issues and challenges of the fitting of ML with QC are presented.

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iCAD: information-Centric network Architecture for DDoS Protection in the Smart Grid

Cited by 3 publications

References 17 publications

Detection of Distributed Denial of Charge (DDoC) Attacks Using Deep Neural Networks With Vector Embedding

Detection of Distributed Denial of Charge (DDoC) Attacks Using Deep Neural Networks With Vector Embedding

Class-Based Grid Field Area Communication with Named Data Networking Approach

Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid

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