Internet Traffic Classification for Faster and Secured Network Service

The COVID-19 pandemic has resulted in a dramatic change in our day to day life. It affected not only the normal working of many organizations but also the traditional classroom teaching and learning methodologies. Since everyone has to maintain social distancing to follow COVID-19 guidelines, work from home is being preferred as the best alternative as a preventive measure from spreading the pandemic. In its severe impact, schools, colleges, and universities were shut down, pushing nearly 1.2 billion students out of the classroom. As a result, the education system has to suddenly adapt to a distinctive online-based e-learning approach over digital platforms. Research tells that online learning motivated more towards the retention of online resources with less cost in terms of money and time. But, it has also brought many challenges along the way. In this research work, we focus on some of the major challenges such as information security and network bandwidth problem during online teaching. The related security measures being adopted in our research work to secure personal information during any online teaching and learning process. We also focus on some basic learning models for provisioning effective online-based teaching and learning.

show abstract

An Approach Based on the Improved SVM Algorithm for Identifying Malware in Network Traffic

Chen

Qin

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%.

show abstract

Internet Traffic Classification for Faster and Secured Network Service

Cited by 3 publications

References 4 publications

Dynamic Network Traffic Management for Secure and Faster Data Transmission

Dynamic Network Traffic Management for Secure and Faster Data Transmission

Effective Online Teaching Learning during COVID-19 Pandemic

An Approach Based on the Improved SVM Algorithm for Identifying Malware in Network Traffic

Contact Info

Product

Resources

About