Skripsi

PENGEMBANGAN THREAT INTELLIGENCE KNOWLEDGE GRAPH DENGAN ENTITY EXTRACTION TERHADAP ADVANCED PERSISTENT THREAT MENGGUNAKAN PRE-TRAINED DEEPSEEK

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This research aims to address complex cybersecurity challenges, namely Advanced Persistent Threat (APT), by developing a Threat Intelligence Knowledge Graph. The study proposes a Natural Language Processing based approach to extract entities and relationships from APT reports, using a pre trained Deepseek model. This model was fine-tuned specifically for Named Entity Recognition (NER) and Relation Extraction tasks. The research findings show that the fine-tuned Deepseek model achieved an F1-score of 0.960, outperforming the BERT model, which only achieved 0.694 under the same conditions and dataset. The primary output of this research is a knowledge graph that effectively visualizes attack entities, such as threat actors, malware, and tactics, into a structured representation that complies with the Structured Threat Information eXpression (STIX) standard. These findings demonstrate that a knowledge graph can be a reliable tool for security analysts to analyze APT attack patterns more quickly and in-depth.

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Availability

Detail Information

Series Title

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Call Number

T1848052025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 173 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.807

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Keamanan Komputer
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

TUTI

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