面對間諜程式的來勢洶洶，目前各大防毒軟體廠商紛紛投入研發，連微軟與雅虎也開始發展相關的防護軟體。但在學術研究方面，到目前為止僅有一篇針對間諜程式的論文[29]發表於2004年。因本論文針對目前危害性較大的監視型間諜程式(Surveillance Spyware)加以研究探討，利用有別於目前一般防毒軟體的偵測技術，讓我們的系統不僅能有效偵測目前現有的間諜程式，更具備偵測新型未知間諜程式的能力。本論文的主要貢獻在於使用靜態與動態的分析技術去蒐集間諜程式的相關特徵，再利用資訊增益(Information Gain)和支援向量機(Support Vector Machine)兩種資料探勘(Data Mining)技術的結合發展出一套間諜程式偵測系統(Spyware Detection System, SDS) 並提出一套整體的運作架構。我們的系統不僅對已知的監視型間諜程式有高達98％的偵測率，當面對新型未知亦有96％的良好偵測效果。並且在我們的運作架構基礎下，系統將擁有自動蒐集間諜程式的新特徵並重新訓練偵測模組的能力，如此即使間諜程式不斷的推成出新，仍可有效的偵測，將其危害降到最低。

Nowadays, the problem of spyware is incredibly serious; some famous anti-virus software vendors such as Norton, Trend Micro had entered the spyware -detection field last year. Even Microsoft and Yahoo also had thrown themselves into the battle of anti-spyware. But there are still less effort to understand it in the research community. At present, there is only one research [29] about the spyware in 2004. In this thesis, we proposed an integrated architecture to defend against surveillance spyware. For overcoming the lacks of usual anti-spyware products, we combine the methods of static analysis and dynamic analysis to extract feature of spyware. By adopting the concepts of machine learning and data-mining, we construct a spyware detection system (SDS) which has 98% detecting rate for known spyware and 96% detecting rate for unknown or novel spyware.

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