A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number

Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers

Tabular Data Generation to Improve Classification of Liver Disease Diagnosis

Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol abuse. This causes abnormal nerve function, bloody coughing or vomiting, insufficient kidney function, hepatic failure, jaundice, and liver encephalopathy.. The diagnosis of this disease is very expensive and complex. Therefore, this work aims to assess the performance of various machine learning algorithms at decreasing the cost of predictive diagnoses of chronic liver disease. In this study, five machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Artificial Neural Network (ANN) algorithm. In this work, we examined the effects of the increased prediction accuracy of Generative Adversarial Networks (GANs) and the synthetic minority oversampling technique (SMOTE). Generative opponents’ networks (GANs) are a mechanism to produce artificial data with a distribution close to real data distribution. This is achieved by training two different networks: the generator, which seeks to produce new and real samples, and the discriminator, which classifies the augmented samples using supervised classifications. Statistics show that the use of increased data slightly improves the performance of the classifier