[HTML][HTML] Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
In fifth Generation (5G) networks, protection from internal attacks, external breaches,
violation of confidentiality, and misuse of network vulnerabilities is a challenging task …
violation of confidentiality, and misuse of network vulnerabilities is a challenging task …
Deep learning-based approach for DDoS attacks detection and mitigation in 5G and beyond mobile networks
B Bousalem, VF Silva, R Langar… - 2022 IEEE 8th …, 2022 - ieeexplore.ieee.org
In this demo, we present a 5G prototype for attacks detection and mitigation in sliced
networks leveraging Machine Learning (ML). Our prototype, based on OpenAirInterface …
networks leveraging Machine Learning (ML). Our prototype, based on OpenAirInterface …
A deep learning assisted software defined security architecture for 6G wireless networks: IIoT perspective
MA Rahman, MS Hossain - IEEE Wireless Communications, 2022 - ieeexplore.ieee.org
The 6G wireless network is expected to drive cyber-physical systems (CPS) from merely
connected things to securely connected intelligence. While 6G will offer real-time …
connected things to securely connected intelligence. While 6G will offer real-time …
DDoS attacks detection and mitigation in 5G and beyond networks: A deep learning-based approach
B Bousalem, VF Silva, R Langar… - … 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Network slicing, where a single physical network is partitioned into several fit-for-purpose
virtual networks with different degrees of isolation and quality of service (QoS), is a key …
virtual networks with different degrees of isolation and quality of service (QoS), is a key …
SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices
5G Network slicing is one of the key enabling technologies that offer dedicated logical
resources to different applications on the same physical network. However, a Denial-of …
resources to different applications on the same physical network. However, a Denial-of …
Attacks Detection in 6G Wireless Networks using Machine Learning
Unlike the fifth generation (5G), which is well recognized for network cloudification with
micro-service-oriented design, the sixth generation (6G) of networks is directly tied to …
micro-service-oriented design, the sixth generation (6G) of networks is directly tied to …
Real-time clustering based on deep embeddings for threat detection in 6G networks
Trials and deployments of sixth Generation (6G) wireless networks, delivering extreme
capacity, reliability, and efficiency, are expected as early as 2030. Attempts from both …
capacity, reliability, and efficiency, are expected as early as 2030. Attempts from both …
A GRU deep learning system against attacks in software defined networks
The management of modern network environments is becoming more and more complex
due to new requirements of devices' heterogeneity regarding the popularization of the …
due to new requirements of devices' heterogeneity regarding the popularization of the …
A Novel Deep Hierarchical Machine Learning Approach for Identification of Known and Unknown Multiple Security Attacks in a D2D Communications Network
Intrusion Detection Systems (IDSs) have played a crucial role in identifying cyber threats for
a very long time. Still, their significance has increased significantly with the advent of 5G/6G …
a very long time. Still, their significance has increased significantly with the advent of 5G/6G …
SDN-based architecture for transport and application layer DDoS attack detection by using machine and deep learning
NM Yungaicela-Naula, C Vargas-Rosales… - IEEE …, 2021 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks
targeting conventional and new generation networks, such as the Internet of Things (IoT) …
targeting conventional and new generation networks, such as the Internet of Things (IoT) …