GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization

Talha Ahmed Khan; Khizar Abbas; Afaq Muhammad; Adeel Rafiq; Wang Cheol Song

doi:10.1109/ICTC49870.2020.9289564

GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization

Talha Ahmed Khan
, Khizar Abbas
, Afaq Muhammad
, Adeel Rafiq
, Wang Cheol Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

The requirements of next-generation of networks have imposed a multi-dimensional/ directional complexity over network management. As a result, the only way forward is through the automation of administrative and management procedures of networks. However, the best solution for having an automation system is through Machine Learning. The next-generation network services have complex requirements that can be determined using service graphs. However, autonomous translation of high-level user requirements to a service graph is itself a complex problem and requires an intelligent solution. Hence in this manuscript, a GAN (generative adversarial network) based fake service graph generation and DRL (Deep reinforcement learning) based optimized graph selection mechanism is considered for automatic intent translation.

Original language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	347-352
Number of pages	6
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289564
State	Published - 21 Oct 2020
Externally published	Yes
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

DRL
GAN
IBN
LCM
and OSM

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC49870.2020.9289564

Cite this

Khan, T. A., Abbas, K., Muhammad, A., Rafiq, A., & Song, W. C. (2020). GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 347-352). Article 9289564 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289564

@inproceedings{bb9f8ea7531f454b9776eae2a6596bb8,

title = "GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization",

abstract = "The requirements of next-generation of networks have imposed a multi-dimensional/ directional complexity over network management. As a result, the only way forward is through the automation of administrative and management procedures of networks. However, the best solution for having an automation system is through Machine Learning. The next-generation network services have complex requirements that can be determined using service graphs. However, autonomous translation of high-level user requirements to a service graph is itself a complex problem and requires an intelligent solution. Hence in this manuscript, a GAN (generative adversarial network) based fake service graph generation and DRL (Deep reinforcement learning) based optimized graph selection mechanism is considered for automatic intent translation.",

keywords = "DRL, GAN, IBN, LCM, and OSM",

author = "Khan, \{Talha Ahmed\} and Khizar Abbas and Afaq Muhammad and Adeel Rafiq and Song, \{Wang Cheol\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "21",

doi = "10.1109/ICTC49870.2020.9289564",

language = "English",

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publisher = "IEEE Computer Society",

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}

Khan, TA, Abbas, K, Muhammad, A, Rafiq, A & Song, WC 2020, GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289564, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 347-352, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289564

GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization. / Khan, Talha Ahmed; Abbas, Khizar; Muhammad, Afaq et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 347-352 9289564 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization

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AU - Song, Wang Cheol

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AB - The requirements of next-generation of networks have imposed a multi-dimensional/ directional complexity over network management. As a result, the only way forward is through the automation of administrative and management procedures of networks. However, the best solution for having an automation system is through Machine Learning. The next-generation network services have complex requirements that can be determined using service graphs. However, autonomous translation of high-level user requirements to a service graph is itself a complex problem and requires an intelligent solution. Hence in this manuscript, a GAN (generative adversarial network) based fake service graph generation and DRL (Deep reinforcement learning) based optimized graph selection mechanism is considered for automatic intent translation.

KW - DRL

KW - GAN

KW - IBN

KW - LCM

KW - and OSM

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M3 - Conference contribution

AN - SCOPUS:85098982900

T3 - International Conference on ICT Convergence

SP - 347

EP - 352

BT - ICTC 2020 - 11th International Conference on ICT Convergence

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ER -

Khan TA, Abbas K, Muhammad A, Rafiq A, Song WC. GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society. 2020. p. 347-352. 9289564. (International Conference on ICT Convergence). doi: 10.1109/ICTC49870.2020.9289564

GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this