An Optimal Reinforced Deep Belief Network for Detection of Malicious Network Traffic

Abstract

Network traffic analysis is referred to as the method to monitor the availability of the network and identify the anomalous activities like operational issues and security. Network traffic mainly occurs due to the transmission of a large amount of data over the computer network at the same time. The grouping and classification process can evaluate the network traffic and the endless determination of network traffic patterns is the most important challenge during network traffic classification. The existing approaches have some limitations because the time consumption was not able to reduce and it has low classification accuracy in the network traffic analysis. To overcome these problems, the Dove Swarm Optimized Reinforced Deep Belief Network (DSO-RDBN) is proposed which is utilized for optimizing the hyperparameters. There are no effectual training algorithms that can optimize the different hyperparameters associated with the RDBN architecture such as momentum coefficients, learning rate, number of hidden nodes, and number of hidden layers without manual tuning. The manual selection of hyperparameters leads to some delay in the network traffic classification process so the DSO algorithm is implemented for addressing the hyperparameter tuning problem. For network traffic classification analysis, the USTC-TFC2016 test dataset is used and this dataset has two categories Benign and Malicious. The performance metrics such as accuracy, precision, sensitivity, F-measure, and false alarm range (FAR) are applied for improving the performance compared to state-of-art methods. The performance rate of accuracy, precision, sensitivity, F-measure and FAR is 97.2%, 97.6%, 97.5%, 97.6% and 68% respectively.

KEYWORDS:

• Dove swarm optimized reinforced deep belief network
• Network traffic classification
• Reinforced deep belief network
• Restricted Boltzmann Machine
• UUSTC-TFC2016 test dataset

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

C. Jehan

C Jehan received the BE degree in electronics and communication engineering from Madurai Kamaraj University, Madurai in 1992 and ME degree in computer science and engineering from Madurai Kamaraj University, Madurai and 1999 respectively. Currently, pursuing PhD degree in information and communication engineering from Manonmaniam Sundaranar University Tirunelveli and working as an associate professor in Department of Computer Science and Engineering, in Chennai Institute of Technology, Chennai, Tamil Nadu, India. His areas of interest are wireless sensor network, computer networking, cyber security, AI, ML. Corresponding author. Email: [email protected]

T. Rajesh Kumar

T Rajesh Kumar received the BE degree in electronics and communication engineering from the Madras University, Tamil Nadu, India in 1996, the ME degree in computer science and engineering from the Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India in 2004, and the PhD degree in information and communication from the Anna University, Chennai, Tamil Nadu, India in 2020. From 1996 to 1998, he worked as an engineer trainee at Aquis Software Consultant, Mumbai. He is currently in the Department of Computer Science and Engineering at Saveetha School of Engineering, SIMATS, Chennai. His research interests are in image and speech signal processing, machine learning, deep learning and data mining. He published more than 28 papers. He is a reviewer in various journals and serves as TPC member in various conferences, including the IEEE International Conferences. He is a member of the IEEE, ISTE and having membership in IAET, IACSIT, IAENG, SDIWC, IRED, CSTA. Email: [email protected]