Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 3
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-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.
Authors and Affiliations
Fatima Isiaka
Keywords
Related Articles
- EP ID EP752189
- DOI -
- Views 5
- Downloads 0
Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches
Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring...
Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations
Nowadays, multi-criteria decision-making (MCDM) methods possess manifold applications in many areas from engineering to supply chain and management. The analytic network process (ANP) method is one of the most widely use...
ResNet for Histopathologic Cancer Detection, the Deeper, the Better?
Histopathological image classification has become one of the most challenging tasks for researchers, due to the varied categories and detailed differences within diseases. In this study, we investigate the critical role...
Identifying Risk Factors for Heart Failure: A Case Study Employing Data Mining Algorithms
Heart diseases are increasingly present in the lives of human beings and are diseases that affect the heart and blood vessels and can lead the person who develops to death. In this article, we analyzed an open and public...
Applications of Quantum Computing in Health Sector
The purpose of this paper is to provide an overview of the current state of quantum computing in the health sector and to explore its potential future applications. Quantum computing has the potential to revolutionize a...