By: Arya Brijith, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,sia University, Taiwan, arya.brijithk@gmail.com
Abstract
Understanding network traffic is important as it allows us to monitor and manage the performance of the network, identify, and troubleshoot issues, and implement security measures to protect against unauthorized access or attacks. In this article, we shall discuss network traffic and its classification.
Introduction
Staring at your computer in anticipation of a file downloading or a webpage loading? It might be due to network traffic. Network traffic is the movement of data packets between devices. It includes the exchange of information between devices on the same network as well as those on other networks, as occurs when you visit a website housed on a server that is located somewhere else on the internet. A few of the network types across which data flow may happen include- Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
Data packets moving back and forth between networked devices are known as network traffic. Numerous actions, including emailing, streaming video, playing online games, and automatic procedures like software upgrades and cloud service synchronization, might start this loop. Network traffic can also come from criminal operations like virus transmission or denial-of-service assaults, background jobs, and system maintenance. For network managers to optimize performance, solve issues, and guarantee that strong security measures are in place, they must have a thorough grasp of these sources.
Network traffic classification
The practice of classifying or clustering devices or network traffic according to features or qualities is known as network classification. The network can be better managed, watched over, and controlled thanks to this categorization.
Network traffic classification techniques
- Machine learning technique: Deep Packet Inspection (DPI) technique is another name for this approach. Using this method, the contents of the packets are inspected in search of distinctive signs of the network applications included in the flow.[1] It is of 2 types- supervised and unsupervised.
- Port-based technique: In computer networking, port-based network classification is a basic technique. It is predicated on the notion that various services and apps exchange data via designated port numbers. These port numbers enable data to enter and exit a device by serving as digital gateways. For example, ports 80 and 443 are usually used for HTTP and HTTPS, respectively, in regular online traffic. Network administrators may use this method to put access control policies into place, making sure that only permitted traffic is allowed through particular ports. It’s crucial to understand that certain programs may use unusual ports, requiring the adoption of extra techniques like deep packet inspection in order to accurately classify them. Nevertheless, network security and traffic management continue to rely heavily on port-based classification.
- Protocol-based technique: Network categorization based on protocols is an essential technique for computer network management and security. It focuses on recognizing and classifying network data according to the particular communication protocols that are being utilized. Protocols are rules that specify how data should be structured, sent, and received between devices. For example, web surfing and file transfers are governed by different protocols, HTTP and FTP, respectively. Network administrators are able to implement customized traffic handling policies by closely examining the protocols that are being used. Granular control is made possible by this technology, which makes it possible to prioritize essential services and reduce security threats. Although protocol-based categorization offers a solid platform for network management, for a more thorough analysis of traffic, it is crucial to combine it with additional methods such as port-based and application-layer inspection.
- Payload-based technique: An advanced technique for examining and classifying network traffic is payload-based network classification. This method explores the actual content, or payload, of the data packets being delivered, in contrast to port- or protocol-based methods. It carefully examines the data in the packet to identify the kind of service or application that is causing the traffic. When standard port or protocol analysis is not sufficient to identify encrypted or masked communication, this approach is very helpful. Network administrators may implement more accurate control and security measures by analyzing the payload, which provides them with insights into the particular events and interactions taking place within the network. It is important to keep in mind, nevertheless, that payload-based categorization might be more difficult to execute and demands a larger amount of processing power than other approaches.
Conclusion
In summary, understanding network traffic complexities is critical to smooth network operation, efficient problem-solving, and strong security implementation. In this article, we have studied a variety of methods for classifying network traffic. Every method plays a vital role in improving network efficiency and guaranteeing security. Combining these methods is recommended for network administrators who want to take a thorough approach to traffic analysis and control. They can successfully protect networks from unwanted access while preserving peak performance thanks to this all-encompassing approach.
Reference
- Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., & Abdessamia, F. (2016, October). Network traffic classification techniques and comparative analysis using machine learning algorithms. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 2451-2455). IEEE.
- Azab, A., Khasawneh, M., Alrabaee, S., Choo, K. K. R., & Sarsour, M. (2022). Network traffic classification: Techniques, datasets, and challenges. Digital Communications and Networks.
- Goli, Y. D., & Ambika, R. (2018, December). Network traffic classification techniques-a review. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) (pp. 219-222). IEEE.
- Wang, L., Li, L., Li, J., Li, J., Gupta, B. B., & Liu, X. (2018). Compressive sensing of medical images with confidentially homomorphic aggregations. IEEE Internet of Things Journal, 6(2), 1402-1409.
- Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021). InFeMo: flexible big data management through a federated cloud system. ACM Transactions on Internet Technology (TOIT), 22(2), 1-22.
- Gupta, B. B., Perez, G. M., Agrawal, D. P., & Gupta, D. (2020). Handbook of computer networks and cyber security. Springer, 10, 978-3.
- Bhushan, K., & Gupta, B. B. (2017). Security challenges in cloud computing: state-of-art. International Journal of Big Data Intelligence, 4(2), 81-107.
Cite As
Brijith A. (2023) Network Traffic: An overview, Insights2Techinfo, pp.1