Cuong Duc Nguyen | Searcharxiv

arXiv2024

Extended State Observer for Mismatch Disturbances Using Taylor Approximation of the Integral

The development of disturbance estimators using extended state observers (ESOs) typically assumes that the system is observable. This paper introduces an improved method for systems that are initially unobservable, leveraging Taylor expansion to approximate the integral of disturbance dynamics. A new extended system is formulated based on this approximation, enabling the design of an observer that achieves exponential stability of the error dynamics. The proposed method's efficacy is demonstrated through a practical example, highlighting its potential for robust disturbance estimation in dynamic systems.

eess.SY↗

arXiv2009

Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.

cs.NE↗