國立虎尾科技大學 |

Detecting Machine-Generated News Using Fine-Tuned Transformers.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Detecting Machine-Generated News Using Fine-Tuned Transformers./
Author:	Kuk, John Howard.
Description:	1 online resource (122 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Information technology. -
Online resource:	click for full text (PQDT)
ISBN:	9798383628850

Detecting Machine-Generated News Using Fine-Tuned Transformers.
Kuk, John Howard.

Detecting Machine-Generated News Using Fine-Tuned Transformers. - 1 online resource (122 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Thesis (D.Engr.)--The George Washington University, 2024.

Includes bibliographical references

Phishing is a leading source of cybercrime, and attacks have become more sophisticated where social media is growing as an effective attack vector. The emergence of generative artificial intelligence (GenAI) has raised questions about its application. Democratizing the technology has made using GenAI easier to use. Malicious actors are finding creative ways to use GenAI to generate fake news, impersonate individuals, spread disinformation, and create complex social engineering campaigns for phishing attempts. This research focused on fine-tuning large language models (LLMs) for classifying machine-generated fake and human-created real news articles. The dataset for this research was a collection of pre-generated GPT-2 news articles, along with real news articles gathered from the internet using Common Crawl. This data combined with the latest popular LLMs such as Llama, Gemma, and Mistral constructed new detection models for fake news. LLMs Meta Llama 2 70b, Meta Llama 3 70B, Mistral AI Mixtral 8x 7B, Mixtral AI Mixtral 8x 22B produced the best results achieving an Accuracy of greater than 99.9%. This research further explained the challenges and outcomes of using commercial-off-the-shelf (COTS) hardware to successfully create and evaluate multiple fine-tuned transformer models. This work will be useful for the cybersecurity information technology industry in its continuing efforts to finding solutions for detecting malicious content generated by machine.

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

Mode of access: World Wide Web

ISBN: 9798383628850Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

DetectIndex Terms--Genre/Form:

554714
Electronic books.

Detecting Machine-Generated News Using Fine-Tuned Transformers.
LDR:02875ntm a22004097 4500 001 1150534
005 20241028051516.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383628850
035 $a (MiAaPQ)AAI31487602
035 $a AAI31487602
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Kuk, John Howard. $3 1477027
245 1 0 $a Detecting Machine-Generated News Using Fine-Tuned Transformers.
264 0 $c 2024
300 $a 1 online resource (122 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
500 $a Advisor: Appulingam, Arthiraiyan;Etemadi, Amir.
502 $a Thesis (D.Engr.)--The George Washington University, 2024.
504 $a Includes bibliographical references
520 $a Phishing is a leading source of cybercrime, and attacks have become more sophisticated where social media is growing as an effective attack vector. The emergence of generative artificial intelligence (GenAI) has raised questions about its application. Democratizing the technology has made using GenAI easier to use. Malicious actors are finding creative ways to use GenAI to generate fake news, impersonate individuals, spread disinformation, and create complex social engineering campaigns for phishing attempts. This research focused on fine-tuning large language models (LLMs) for classifying machine-generated fake and human-created real news articles. The dataset for this research was a collection of pre-generated GPT-2 news articles, along with real news articles gathered from the internet using Common Crawl. This data combined with the latest popular LLMs such as Llama, Gemma, and Mistral constructed new detection models for fake news. LLMs Meta Llama 2 70b, Meta Llama 3 70B, Mistral AI Mixtral 8x 7B, Mixtral AI Mixtral 8x 22B produced the best results achieving an Accuracy of greater than 99.9%. This research further explained the challenges and outcomes of using commercial-off-the-shelf (COTS) hardware to successfully create and evaluate multiple fine-tuned transformer models. This work will be useful for the cybersecurity information technology industry in its continuing efforts to finding solutions for detecting malicious content generated by machine.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Information technology. $3 559429
650 4 $a Computer science. $3 573171
653 $a Detect
653 $a Fine-tuning
653 $a GenAI
653 $a Large language models
653 $a Phishing
653 $a Ttransformer
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0489
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Cybersecurity Analytics. $3 1467608
773 0 $t Dissertations Abstracts International $g 86-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31487602 $z click for full text (PQDT)