TY - GEN
T1 - Securing NetSoftIoT Environments with Enhanced Attack Detection Using RandomForest and LSTM in SDN
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Chui, Kwok Tai
AU - Arya, Varsha
AU - Wu, Jinsong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the field of NetSoftIoT, where network softwarization converges with the proliferation of IoT devices, ensuring robust security in SDN environments is paramount. This paper presents a novel approach that integrates RandomForest for optimized feature selection and LSTM networks for attack detection. Our methodology capitalizes on the LSTM's sequential data processing capability to discern patterns indicative of DDoS attacks within network traffic with an accuracy of 83 %. Leveraging a dataset comprising varied traffic types, our model demonstrated precision in identifying DDoS traffic with a recall of 0.94. The results, validated by confusion matrices and classification reports, indicate the model's efficacy in maintaining network integrity against malicious threats. This study advances the frontier of cybersecurity in SDN, crucial for the burgeoning landscape of IoT applications.
AB - In the field of NetSoftIoT, where network softwarization converges with the proliferation of IoT devices, ensuring robust security in SDN environments is paramount. This paper presents a novel approach that integrates RandomForest for optimized feature selection and LSTM networks for attack detection. Our methodology capitalizes on the LSTM's sequential data processing capability to discern patterns indicative of DDoS attacks within network traffic with an accuracy of 83 %. Leveraging a dataset comprising varied traffic types, our model demonstrated precision in identifying DDoS traffic with a recall of 0.94. The results, validated by confusion matrices and classification reports, indicate the model's efficacy in maintaining network integrity against malicious threats. This study advances the frontier of cybersecurity in SDN, crucial for the burgeoning landscape of IoT applications.
KW - DDoS Attack Detection IoT Security
KW - Intrusion Detection System
KW - LSTM Neural Networks
KW - Network Softwarization
UR - https://www.scopus.com/pages/publications/85206194135
U2 - 10.1109/VTC2024-Spring62846.2024.10683489
DO - 10.1109/VTC2024-Spring62846.2024.10683489
M3 - Conference contribution
AN - SCOPUS:85206194135
T3 - IEEE Vehicular Technology Conference
BT - 2024 IEEE 99th Vehicular Technology Conference, VTC2024-Spring 2024 - Proceedings
T2 - 99th IEEE Vehicular Technology Conference, VTC 2024-Spring
Y2 - 24 June 2024 through 27 June 2024
ER -