An Advanced Fusion Neural Network Paradigm for Intelligent Cyber Security Anomaly Detection
Network security anomaly detection constitutes a critical defense mechanism in contemporary cybersecurity frameworks. We present a fusion CNN-RNN (convolutional neural network-recurrent neural network) model integrating spatial pattern recognition with temporal modelling for network anomaly detection, employing three-branch architecture processing 41 attributes with regularisation for severe imbalance (U2R 0.1%). Comprehensive validation on the NSL-KDD benchmark dataset establishes that our fusion paradigm outperforms existing machine learning approaches in both dual-class and quintuple-class classification challenges, achieving performance improvements of 11.5% and 29.8%, respectively.
期刊介绍：
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO
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