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Predicting Network Failures with AI ...

Saha, Chandrika.

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Predicting Network Failures with AI Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Network Failures with AI Techniques./
Author:	Saha, Chandrika.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	99 p.
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Operating systems. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30737648
ISBN:	9798381028829

Predicting Network Failures with AI Techniques.
Saha, Chandrika.

Predicting Network Failures with AI Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 99 p.

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.Sc.)--The University of Western Ontario (Canada), 2023.

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to run faultlessly and without delay. However, we lack a suitable generic network failure identification and prediction system due to the unavailability of publicly accessible failure data. This study simulates network traffic to gather failure data based on a general network failure guideline. Furthermore, various state-of-the-art Machine Learning and Deep Learning methods were applied to the generated data. Notably, our proposed Deep Learning model for failure identification provides accuracy, precision, recall, and F1 scores in the range of 97% to 99% for three different demonstration networks. Additionally, our proposed Long Short Term Memory model gives low root mean square error rates of 0.9751 for failure prediction.

ISBN: 9798381028829Subjects--Topical Terms:

3681934
Operating systems.

Predicting Network Failures with AI Techniques.
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