Authors :
Neeraj Sandeep Solanki; Devaang Nadkarni; Vadlamudi Neel Vittal Bharath; Mehul Kumar; Prajakta Biradar
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/bdhx5urp
Scribd :
https://tinyurl.com/3mkaxdmd
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR093
Abstract :
The advent of 6G networks ushers in a new
era of intelligent network management, necessitating
robust security measures to safeguard against emerging
threats. This paper presents a comprehensive framework
for anomaly detection tailored specifically for 6G
Software-Defined Networks (SDNs), leveraging
innovative ML), (DL), and dynamic telemetry
techniques. The proposed framework, termed Anomaly
Detection System for 6G SDNs, integrates ensemble
learning (EL) algorithms and deep neural networks
(DNNs) to detect anomalies within network traffic.
Beginning with the preprocessing and feature selection
stages, the proposed system employs an amalgam EL
method to enhance the efficacy of anomaly detection.
Datasets including CICDDOS2019, NSL KDD,
CIC_IDS2017, and NB2015 undergo dimensionality
reduction and feature subset determination to optimize
performance. Furthermore, dynamic telemetry is
seamlessly integrated into the proposed, enabling real-
time monitoring and adaptive response mechanisms
within SDN environments. By harnessing the flexibility
and programmability of SDNs, the framework ensures a
proactive defense against evolving threats, bolstering the
security posture of 6G networks. Experimental
evaluations demonstrate the effectiveness of ADS6SDN
across diverse datasets, achieving high accuracies while
minimizing false alarm rates. In conclusion, integrating
ML, DL, and dynamic telemetry within the proposed
approach offers a potent solution for enhancing the
security and responsiveness of 6G SDNs. By leveraging
the inherent advantages of SDN architectures, the
framework not only fortifies network defenses against
emerging threats but also ensures adaptability to the
budding scenario of next-generation
telecommunications.
Keywords :
Software-Defined Networks (SDNs), Ensemble Learning, Dynamic Telemetry, Network Traffic Analysis, Next-Generation Networks.
The advent of 6G networks ushers in a new
era of intelligent network management, necessitating
robust security measures to safeguard against emerging
threats. This paper presents a comprehensive framework
for anomaly detection tailored specifically for 6G
Software-Defined Networks (SDNs), leveraging
innovative ML), (DL), and dynamic telemetry
techniques. The proposed framework, termed Anomaly
Detection System for 6G SDNs, integrates ensemble
learning (EL) algorithms and deep neural networks
(DNNs) to detect anomalies within network traffic.
Beginning with the preprocessing and feature selection
stages, the proposed system employs an amalgam EL
method to enhance the efficacy of anomaly detection.
Datasets including CICDDOS2019, NSL KDD,
CIC_IDS2017, and NB2015 undergo dimensionality
reduction and feature subset determination to optimize
performance. Furthermore, dynamic telemetry is
seamlessly integrated into the proposed, enabling real-
time monitoring and adaptive response mechanisms
within SDN environments. By harnessing the flexibility
and programmability of SDNs, the framework ensures a
proactive defense against evolving threats, bolstering the
security posture of 6G networks. Experimental
evaluations demonstrate the effectiveness of ADS6SDN
across diverse datasets, achieving high accuracies while
minimizing false alarm rates. In conclusion, integrating
ML, DL, and dynamic telemetry within the proposed
approach offers a potent solution for enhancing the
security and responsiveness of 6G SDNs. By leveraging
the inherent advantages of SDN architectures, the
framework not only fortifies network defenses against
emerging threats but also ensures adaptability to the
budding scenario of next-generation
telecommunications.
Keywords :
Software-Defined Networks (SDNs), Ensemble Learning, Dynamic Telemetry, Network Traffic Analysis, Next-Generation Networks.
