A Sophisticated Cybersecurity Intrusion Identification Model Using Deep Learning
Grace Mokoena, Jonas Nilsson · International Academic Journal of Science and Engineering · 2023
The data is vulnerable to numerous attacks during transmission within the network environment. Identifying breaches in network communications is becoming more critical. Scientists employ machine learning methodologies to develop efficient Intrusion Detection Systems (IDS). This paper presents an IDS incorporating preprocessing techniques and a Deep Learning (DL) framework for detecting Denial of Service (DoS) attacks. The research evaluated the proposed model utilizing the dataset commonly referenced in academic literature. The study implemented preprocessing techniques, including feature deletion, random subset choice, choosing features, duplication elimination, and normalizing on the database. Enhanced recognition efficiency was achieved for both training and testing assessments. The test results indicate that the Conventional Neural Network (CNN)-based inception-like model had the highest accuracy, with 98% for binary classification and 97% for multiclass classification among the offered models. The deductive time of the suggested framework for different test data sizes appears favorable compared to baseline models with fewer parameters that can be trained. The proposed IDS system, along with the preprocessing techniques, yields better outcomes when compared with contemporary studies.