國立虎尾科技大學 |

Predicting Network Failures with AI Techniques.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting Network Failures with AI Techniques./
作者:	Saha, Chandrika.
面頁冊數:	1 online resource (99 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Web studies. -
電子資源:	click for full text (PQDT)
ISBN:	9798381028829

Predicting Network Failures with AI Techniques.
Saha, Chandrika.

Predicting Network Failures with AI Techniques. - 1 online resource (99 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381028829Subjects--Topical Terms:

1148502
Web studies.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting Network Failures with AI Techniques.
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500 $a Advisor: Haque, Anwar.
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520 $a 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.
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538 $a Mode of access: World Wide Web
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