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博碩士論文 etd-0916121-234745 詳細資訊
Title page for etd-0916121-234745
論文名稱
Title
資安事件摘要萃取
Abstractive Summarization of Target Attacks Based on Transfer Learning
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
64
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2021-08-17
繳交日期
Date of Submission
2021-10-16
關鍵字
Keywords
網路威脅情資、APT事件、自然語言處理、自動化摘要系統、類神經網路
CTI, APT Events, NLP, Automatic Summarization System, Neural Network
統計
Statistics
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中文摘要
資通科技在硬體與軟體上的快速發展,提供企業組織與個人更加便利的生活。與此同時,也提升資訊安全的風險。隨著APT組織的出現,駭客組織攻擊頻率與複雜程度日益升級。針對單一組織與領域的攻擊接連出現。因此,有效利用網路威脅情資,提前了解駭客組織過往的行為,並將以往被動的防禦策略轉為主動的提前部屬,企業組織才能應對APT攻擊。
近年來,網路威脅情資蓬勃發展,已有許多全國知名的威脅情資交換平台。但所產生的大量CTI逐漸演變為大數據。若仰賴人工進行收集與分析,將花費許多時間。因此,企業組織如何快速的篩選自身所需的資訊成為一項必經課題。
有鑑於此,本研究提出一個專用於資訊安全威脅事件的自動化摘要系統「TISUM」(TISUM Threat Intelligence Summarizer)。收集大量的資訊安全事件新聞以及資訊安全報告。透過自然語言處理(Natural Language Processing,簡稱NLP)以及類神經網路,自動化產生資訊安全事件的摘要。「TISUM」達到ROUGE評分70%,讓企業組織可以快速理解網路威脅情資的重點。
Abstract
The rapid development of ICT (Information Communication Technology) in hardware and software distribute more convenient life to enterprises and individuals. However, it also increases information security risk. The emergence of APT (Advanced Persistent Threat) group extends complexity and frequency of cyber-attack. More cyber-attacks target at individual organization and industry, and therefore proactive defense such as Cyber Threat Intelligence (CTI) acquisition to comprehend the behaviors of hacker groups is needed for enterprises and organizations to properly respond to APT attacks, rather than the passive and conventional defense strategies.
There are many famous threat intelligences sharing platforms in recent year, representing the flourishing development of CTI. However, it takes much time to collect and analyze the accumulated CTI information manually. Therefore, filtering out the needed information is a crucial issue for enterprises and organizations.
To solve the abovementioned issues, this study proposes an automated summarization system “TISUM” (Threat Intelligence Summarizer) to gather plenty of news and APT reports and produce summary of information security incidents automatically by utilizing Natural Language Processing (NLP) and neural networks. The proposed system can reach 70% in ROUGE evaluation, which means enterprises and organizations can comprehend the key point of cyber threat intelligences with the proposed system.
目次 Table of Contents
論文審定書.....................................................................................................................i
摘要................................................................................................................................ii
Abstract........................................................................................................................ iii
目錄...............................................................................................................................iv
圖次...............................................................................................................................vi
表次..............................................................................................................................vii
第一章 緒論............................................................................................................1
1.1 研究背景....................................................................................................1
1.2 研究動機....................................................................................................2
第二章 文獻探討....................................................................................................5
2.1 背景相關研究............................................................................................5
2.2 網路威脅情資............................................................................................7
2.3 機器學習與類神經網路............................................................................8
2.4 摘要技術..................................................................................................15
2.4.1 威脅行為擷取..........................................................................................17
2.4.2 實體萃取..................................................................................................17
2.4.3 關聯萃取..................................................................................................18
第三章 研究方法..................................................................................................19
3.1 資料蒐集..................................................................................................21
3.2 文本標註..................................................................................................21
3.2.1 標註工具..................................................................................................22
3.3 威脅實體萃取..........................................................................................24
3.4 威脅事件摘要萃取..................................................................................26
v
第四章 系統評估..................................................................................................28
4.1 實驗 1、標註工具與標註規則、數量比較與篩選...............................34
4.2 實驗 2、比較不同 BERT 優化器與參數設置對系統效能的影響.......37
4.3 實驗 3、比較威脅實體萃取模組中的三種不同神經網路...................40
4.4 實驗 4、威脅實體萃取相關論文比較...................................................44
4.5 實驗五、資安摘要萃取..........................................................................45
第五章 研究貢獻與未來展望..............................................................................50
參考文獻......................................................................................................................52
參考文獻 References
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