A novel method for effective intrusion detection based on convolutional speaking neural networks
Ying Xie, Hong Chen
Abstract
These days, most people can't imagine their life without their networks. People rely more and more on networks as new technologies like 5G and the Internet of Things proliferate, and networks are becoming larger and more complicated by the day. Consequently, the enormous networked society is under increasing danger from cyber attacks, which are also growing in number, sophistication, and variety. Users' private data can end up in the wrong hands. Data communicated via a network might be compromised in some way. It is also possible to target the computer infrastructure that is linked to the network. Thus, NIDS is crucial in ensuring that contemporary society's network communication environment is secure and dependable. This work proposes a detection model that is both lightweight and effective. This model is able to keep its high detection accuracy by combining the benefits of SNN and CNN with logical algorithm design. On top of that, it can bring attention to clear strengths in relation to computing efficiency and power usage. The suggested model was tested against a number of state-of-the-art models using an extensive and integrated set of benchmarks. With a 23 % improvement in detection accuracy (AD), a 19 % improvement in delay, and a 28 % improvement in consumption energy, the suggested technique outperformed the Spiking DFR + MLP and Spike-DHS methods in the simulations.