臺灣博碩士論文加值系統

近年來，不必要的應用程式已經成為使用者主要的安全威脅之一。並隨著雲端的世代的來臨，網頁瀏覽器控制大量的資訊，以及社交互動，更是儲存了許多重要的敏感資料。
即使可能不必要的應用程式本身並非惡意的行為，但仍會對使用者造成侵入性的困擾，例如：廣告注入、挾持瀏覽器設定、同時伴隨其他不必要的應用程式下載及監控使用者上網及瀏覽行為等。廣告注入器就是可能不必要的應用程式的最好的例子之一。安裝的媒介有3種：瀏覽器擴充功能型、二進制型、網路型。

在本篇研究，我們的目標為針對二進制型的廣告注入器，藉由觸發廣告注入行為進行觀察。我們提供了一個大幅減少人工分析廣告注入器時間的方法，透過瀏覽器的特徵作為辨認惡意網址的依據。我們開發了一套過濾方法濾掉部分不需要參考的網址，留下可疑、值得被分析的網址。

本研究結果顯示，濾掉的URLs可以達到準確率98.10\%以及召回率98.10\%。本研究提出了幾點貢獻：(1)發展一套可以重複使用的方法來偵測影響Google用戶的廣告注入； (2)使用廣告注入關鍵的特徵以及與二進制型的廣告注入器進行互動，藉此觸發注入行為； (3)提出了一種利用瀏覽器特徵分析識別由二進制型廣告注入器產生惡意URL的方法； (4)呈現一個完整的案例探討，藉此說明廣告注入器是如何攻擊使用者。

In recent years, unwanted applications have become one of the major security threats to users. With the coming of the cloud generation, web browsers control a lot of information, as well as social interaction, but also save a lot of important sensitive information.
Even if the potentially unwanted programs may not be malicious in itself, they will still be intrusive to the user, such as ad injection, hijacking browser settings, and bundling other unwanted software to download and monitoring the users' Browsing behavior and so on. The ad injector is one of the best examples of unwanted programs. There are three kinds of installed vectors: extension-based, binary-based, network-based.

In this study, our goal is to target ad injectors for binary execution, by triggering ad injection behavior. We've provided an approach to significantly reduce the time it takes to manually analyze ad injectors, which is based on the characteristics of the browser to identify malicious URLs. We've developed a set of filtering methods. To filter out URLs that do not require reference and leave suspicious and worthwhile URLs.

The results of this study show that the URLs are filtered at a precision rate of 98.10\% and a recall rate of 98.10\%. This study presents several contributions: (1) developing a reusable method to detect Google User's ad injection; (2) triggering injection behavior by using ad injection key features and interacting with the binary ad injector; (3) identifying malicious URLs from binary-based ad injectors by browser features analysis; (4) presenting a comprehensive case study of how the ad injector attacks the user.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Challenges and Goals . . . . . . . . . . . . . . . . . . . 4
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 The Outline of Thesis . . . . . . . . . . . . . . . . . . . 5
2 Background and RelatedWork 6
2.1 Pay-Per-Install Network . . . . . . . . . . . . . . . . . 6
2.2 Potentially Unwanted Program . . . . . . . . . . . . . . 9
2.3 Ad Injectors . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 The Severity of Ad Injectors . . . . . . . . . . . 11
2.3.2 Malvertising . . . . . . . . . . . . . . . . . . . 12
2.3.3 Ad Injection Practices . . . . . . . . . . . . . . 12
2.3.4 Extension-based Ad Injectors . . . . . . . . . . 13
2.3.5 Binary-based Ad Injectors . . . . . . . . . . . . 15
3 Identifying Malicious URLs from Binary-based Ad Injectors
by Browser Features Analysis 17
3.1 Ad Injectors Network Behaviors Recorder . . . . . . . . 19
3.1.1 Identifying Potential Triggering Websites . . . . 20
3.1.2 Using Bare-metal Machine to Defeat Anti-techiniques 20
3.1.3 Decrypting SSL or TLS Traffic with Wireshark . 21
3.2 Browser Information Crawler . . . . . . . . . . . . . . . 21
3.3 Ad Injection URLs Filter . . . . . . . . . . . . . . . . . 23
3.3.1 Heterogeneous Data Resources Integration . . . 24
3.3.2 Browser Feature Extractor . . . . . . . . . . . . 24
3.3.3 Decision Tree Classification . . . . . . . . . . . 27
3.3.4 Ad Injectors Detection . . . . . . . . . . . . . . 28
3.3.5 Time Series Reconstruction . . . . . . . . . . . 28
4 Experiments and Results 30
4.1 Experiment Design and Dataset . . . . . . . . . . . . . . 30
4.1.1 Experiment Concept and Description . . . . . . 31
4.1.2 Datasets . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . 33
4.3 Effectiveness Analysis . . . . . . . . . . . . . . . . . . 34
4.3.1 Filtering Ratio . . . . . . . . . . . . . . . . . . 35
4.3.2 The Comparison of Bare-metal and Virtual Machine
. . . . . . . . . . . . . . . . . . . . . . . 36
4.3.3 Case Studies . . . . . . . . . . . . . . . . . . . 39
4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . 43
5 Conclusions and FurtherWork 44
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Further Work . . . . . . . . . . . . . . . . . . . . . . . 45

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