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Author: 沈介國
Chieh-kuo Shen
Thesis Title: 透過階層式警報分類器及資料探勘技術降低假警報
False Alarm Reduction via Hierarchical Alert Classifier and Data Mining Approach
Advisor: 李育杰
Yuh-Jye Lee
Committee: 鮑興國
Hsing-Kuo Kenneth Pao
項天瑞
Tien-Ruey Hsiang
楊傳凱
Chuan-Kai Yang
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2009
Graduation Academic Year: 97
Language: 英文
Pages: 50
Keywords (in Chinese): 入侵偵測假警報RIPPER階層式警報分類器
Keywords (in other languages): intrusion detection, false alarm, RIPPER, hierarchical alert classifier
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  • 入侵偵測系統(IDS)是一套架設用來監控系統及網路狀態中的軟硬體設備,偵測企圖洩漏機密及危害系統資源的入侵行為。然而大量的假警報是入侵偵測系統需要面對的嚴峻問題,分析研究大量的警報對於資安人員來說是難以實現的。在此篇論文中,我們提出一種架構能降低專家在處理資安警報的負擔。此架構結合了能產生更具資訊的KDD'99(一種經常應用在入侵資料分析的數據)資料型態之KDD事件產生器及一種能判定真實攻擊並降低大量的假警報的階層式警報分類器。為了能讓警報分類器能在不同的網路環境下處理警報,我們提出KDD事件產生器將IDS警報從封包轉為KDD事件。並且採取結合了不當行為與異常行為偵測的階層式警報分類器來降低假警報。我們設計了模擬真實環境的腳本,並且使用我們的架構來進行實驗。實驗結果證明階層式警報分類器的確能改善原本的入侵偵測系統。


    Intrusion Detection System (IDS) is a software or hardware device deployed to monitor host activities and network for detecting intrusions, which are action that attempt to compromise the confidentiality, integrity and availability of computer resources. Nevertheless, IDSs are faced with a serious problem on a huge number of false alarms. It is really infeasible for security analysts to investigate lots of these alarms. In this thesis, we proposed a framework incorporated with the Informative KDD Instance Generator that is able to generate the more informative KDD'99 (a common used intrusion dataset) type instances and a hierarchical alert classifier that identifies true attacks and filters out the highly possible false alarms to alleviate a security analyst's burden. In order to make the alert classifier fit to different network environments, we propose the Informative KDD Instance Generator which is capable to convert the alert of IDS view from packet to KDD type instance. For reducing the false alarm, we adopt hierarchical alert classifier which combined misuse intrusion detection and anomaly intrusion detection. Our experiments were designed for simulating the scenario for applying our proposed framework to real world security systems. The experimental results demonstrate that the hierarchical alert classifier will improve the original IDS.

    教授推薦書 ..................................... i 論文口試委員審定書 ................................ ii 中文摘要 ...................................... iii 英文摘要 ...................................... iv 誌謝 ......................................... v 目錄 ......................................... vi 表目錄 ......................................... viii 圖目錄 ........................................ ix 演算法目錄 ..................................... x 1 Introduction ................................... 1 1.1 Background ................................ 3 1.2 ThesisOrganization............................ 4 2 IntrusionDetectionSystem........................... 5 2.1 IncidentsandAttacks........................... 5 2.2 TaxonomyofIDSs ............................ 8 2.2.1 Host-based IDS vs. Network-based IDS ....... 8 2.2.2 Anomalyvs.Misusedetection .................. 9 2.3 Snort.................................... 9 2.4 IntrusionPreventionSystem ....................... 11 3 DataMiningMethodsandSystemFramework ............... 12 3.1 RIPPER.................................. 12 3.2 SystemFramework ............................ 16 3.2.1 Informative KDD Instance Generator ............ 16 3.2.2 Hierarchical Alert Classifier ................... 17 4 ExperimentsandResults ............................ 20 4.1 Dataset Descriptions and Preprocessing ........ 20 4.1.1 DARPA1999Dataset ...................... 20 4.1.2 Alert Labeling and Separated Alerts Dataset .......... 21 4.1.3 FeatureExtraction ........................ 21 4.2 BuildingHierarchicalAlertClassifier ....... 24 4.2.1 Blacklist .............................. 24 4.2.2 Whitelist.............................. 27 4.3 Hierarchical Alert Classification Experiment .... 27 4.3.1 1st-Tier:MisuseDetector .................... 29 4.3.2 2nd-Tier:AnomalyDetector ................... 30 4.4 NumericalComparison .......................... 30 5 ConclusionsandDiscussions .......................... 35 Bibliography .................................... 37

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