Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Abstract

Intrusion and Prevention technologies perform reliably in harsh conditions by fortifying many of the world’s highest security sites with few defects in high performance. This paper aims to contribute by designing an Intrusion/Preventive System using a window-based convolutional neural network (CNN), an integrated recurrent neural network (RNN) and autoencoders (AutoE) to detect and test the performance of the intrusion detection system. The data packets were converted to images where the pixels were used as input. The CNN architecture shows a three (3) layer model with high predictive performance. The result shows high performance on CNN as compared to both RNN and AutoE, CNN seems to resist overfitting more than the rest of the models. The future perspective would be to test the model on other standard methods such as support vector machine (SVM) and Dynamic Control Systems.

Research on Cable Tension Prediction Based on Neural Network

“…At the same time, the algorithms used now are also improved and optimized, either through distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) [24], where the inertia weights and learning factors are adjusted during the evolutionary process, or through deep learning, eliminating the requirement for manual feature engineering [25]. The purpose of analyzing the cable is achieved by converting the measured data into images using window-based convolutional neural network (CNN), integrated recurrent neural network (RNN), and autoencoder (AutoE), where pixels are used as inputs to analyze the visual appearance of the cable [26].…”

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

“…The precision and accuracy of an acoustic-based DoA estimation are essential across a spectrum of industries, spanning vital applications in both civilian and military sectors. Encompassing critical domains such as defense, law enforcement, security [1], and surveillance [2], reliable and precise DoA estimation ensures safety, strategic decision-making [3], and operational effectiveness. Applications such as gunshot DoA estimation [4], drone DoA estimation [5], and automotive angle estimation [6] require highly accurate estimates for optimal functionality.…”