TY - GEN
T1 - Development of Intrusion Detection System using Residual Feedforward Neural Network Algorithm
AU - Rushendra,
AU - Ramli, Kalamullah
AU - Hayati, Nur
AU - Ihsanto, Eko
AU - Gunawan, Teddy Surya
AU - Halbouni, Asmaa Hani
N1 - Funding Information:
ACKNOWLEDGMENT The publication is supported by Kementerian Riset Dan Teknologi/Badan Riset Dan Inovasi Nasional – Republik Indonesia through Hibah Penelitian Disertasi Doktor Scheme under contract number NKB-332/UN2.RST/HKP.05.00/2021, in which Prof. Dr-Ing. Kalamullah Ramli is the corresponding author.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, the intrusion detection system (IDS) must be precise and quick to analyze real-Time traffic data. Despite extensive research, there is still a need to improve detection accuracy and speed due to the tremendous increase in internet traffic volume and variety. This paper introduces a novel, efficient, and accurate approach for real-Time intrusion detection and classification based on the Residual Feedforward Neural Network (RFNN) algorithm. The RFNN algorithm is developed to avoid overfitting, improve detection accuracy, and accelerate training and inference. Additionally, the suggested algorithm is highly adaptable and straightforward to accommodate different types of intrusion. The prominent NSL-KDD dataset was utilized for training and testing in this study. The accuracy obtained for two and five classes was 84.7 percent and 90.5 percent, respectively. Additionally, the identification speed was $15\ \mu\mathrm{s}$ and $14\ \mu\mathrm{s}$, respectively, indicating that real-Time detection is feasible.
AB - An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, the intrusion detection system (IDS) must be precise and quick to analyze real-Time traffic data. Despite extensive research, there is still a need to improve detection accuracy and speed due to the tremendous increase in internet traffic volume and variety. This paper introduces a novel, efficient, and accurate approach for real-Time intrusion detection and classification based on the Residual Feedforward Neural Network (RFNN) algorithm. The RFNN algorithm is developed to avoid overfitting, improve detection accuracy, and accelerate training and inference. Additionally, the suggested algorithm is highly adaptable and straightforward to accommodate different types of intrusion. The prominent NSL-KDD dataset was utilized for training and testing in this study. The accuracy obtained for two and five classes was 84.7 percent and 90.5 percent, respectively. Additionally, the identification speed was $15\ \mu\mathrm{s}$ and $14\ \mu\mathrm{s}$, respectively, indicating that real-Time detection is feasible.
KW - classification
KW - feedforward neural network
KW - intrusion detection system
KW - network security
KW - residual neural network
UR - http://www.scopus.com/inward/record.url?scp=85126661756&partnerID=8YFLogxK
U2 - 10.1109/ISRITI54043.2021.9702773
DO - 10.1109/ISRITI54043.2021.9702773
M3 - Conference contribution
AN - SCOPUS:85126661756
T3 - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
SP - 539
EP - 543
BT - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Y2 - 16 December 2021
ER -