AI and Machine Learning Enabled Software Defined Networks

Title

AI and Machine Learning Enabled Software Defined Networks

Creator

Satheesh K.K.S.V.A.; Janani M.; Venkateswarlu S.C.; Kumar R.G.; Gupta A.; Kotaiah B.

Description

The telecommunications industry has not been exempt from the technology sectors massive artificial intelligence (AI) and machine learning (ML) boom in recent years. Artificial intelligence (AI) and machine learning (ML) provide advanced analytics and automation that are in line with modern networking concepts like software-defined networking (SDN) and software-defined wide-area networks (SD-WAN). Work is being done to determine how AI/ML can benefit SD-WAN and to demonstrate these benefits in a real SD-WAN network using a workable example. Modern ML techniques and algorithms are the extent of AI/ML. Todays Internet is under constant threat from DDoS (Distributed Denial of Service) attacks. As the volume of Internet traffic grows, its getting harder and harder to tell whats legitimate and whats malicious. The DDoS attack was detected using a machine learning approach that makes use of a Random Forest classifier. To better detect DDoS attacks, we tweak the Random Forest algorithm. The proposed machine learning approach outperforms, as demonstrated by our results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-446, pp. 131-144.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; Distributed denial of service and software-defined networking; Machine learning

Coverage

Satheesh K.K.S.V.A., School of Computer Science and Artificial Intelligence, SR University, Hanuma Konda, Warangal, India; Janani M., IT Department, St. Josephs College of Engineering, Hyderabad, India; Venkateswarlu S.C., Electronics and Communication Engineering, Institute of Aeronautical Engineering, Telangana, Dundigal, Hyderabad, India; Kumar R.G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Gupta A., Amity Business School, Amity University, Uttar Pradesh, Greater Noida, India; Kotaiah B., Department of CS and IT, Maulana Azad National Urdu Central University, Telangana, Gachibowli, Hyderabad, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981191558-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-1559-8_14

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135029294&doi=10.1007%2f978-981-19-1559-8_14&partnerID=40&md5=1e7b4d33e4a808fbb3e51eb3d93f5fb9