Version 1
: Received: 3 April 2021 / Approved: 6 April 2021 / Online: 6 April 2021 (17:59:47 CEST)
How to cite:
Zaidi, T. A Network Intrusion Based Detection System for Cloud Computing Environment. Preprints2021, 2021040183. https://doi.org/10.20944/preprints202104.0183.v1
Zaidi, T. A Network Intrusion Based Detection System for Cloud Computing Environment. Preprints 2021, 2021040183. https://doi.org/10.20944/preprints202104.0183.v1
Zaidi, T. A Network Intrusion Based Detection System for Cloud Computing Environment. Preprints2021, 2021040183. https://doi.org/10.20944/preprints202104.0183.v1
APA Style
Zaidi, T. (2021). <strong>A Network Intrusion Based Detection System for Cloud Computing Environment</strong>. Preprints. https://doi.org/10.20944/preprints202104.0183.v1
Chicago/Turabian Style
Zaidi, T. 2021 "<strong>A Network Intrusion Based Detection System for Cloud Computing Environment</strong>" Preprints. https://doi.org/10.20944/preprints202104.0183.v1
Abstract
Cloud computing is an emerging area which provide on demand computing resources and services through internet. It is faster and efficient technique but prone to severe security attacks. In this paper author have proposed a Network Intrusion Detection System (NIDS) to detect attacks at front end and backend when bulky flow of data packets flowing in a cloud environment. In our framework we used Signature based detection system for identifying the intruder and the Anomaly based detection system for detecting network attacks. The NIDS sensors were placed in a collaborative manner to prevent the attacks and to update the knowledge bases. Author have used supervised learning model to detect abnormal behavior of packets from network traffic. The dataset were trained and tested in terms of precision, recall, accuracy and model build time to select the best machine-learning model for detection of intruder and to improve the computational time and performance.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
