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Scamadviser進入發燒排行的影片

一個跨境電商詐騙網站協助偵測機制

為了解決Scamadviser的問題，作者劉晏任 這樣論述：

隨著網際網路蓬勃發展，大多數人都曾有上網購物的經驗，增加方便性的同時卻也衍生出許多詐騙案例，而詐騙手法日新月異，過去常見的一頁式詐騙網站隨著時間逐漸演化為電商形式，相較於過去一頁式網頁粗糙的內容，電商形式的通常會有較精美的圖片及完整的框架，使消費者難就網頁內容判斷是否為詐騙網站。而市面上現存的產品，僅以安全或不安全作為回報，消費者無法得知更多的資訊，可能會因此被誤導。在本研究中，我們提出了一個框架，除了風險燈號提醒外，整合欲查詢網站的各項資料供消費者參考，並特別針對是否能與台灣有所連結進而提供相關的警告，讓消費者能在購物前有更充分的了解並評估自己使否處於風險之中。我們首先分析了台灣網路資訊中

心的詐騙網站資料集，透過將資料集進行分群，我們能初步了解目前的詐騙框架，進而可以識別未知網站是否與已知的詐騙群集有關，並利用此資料集來獲得詐騙網站的一些相關資訊占比，像是憑證頒發機構、網域存活時間或是註冊國家等等以利我們提出框架所列出的警告有所依據及參考。在實驗部分，本研究透過對比現存偵測詐騙網站工具的辨識結果以及使用者問卷調查，來驗證框架的有效性。實驗結果顯示，現存偵測詐騙網站工具 Scamadviser 成功辨識出的詐騙網站僅有54%，防詐達人為 79%，而本研究所提出的框架則在不考慮分群的前提下，可以判斷出87% 可能隱藏風險的詐騙網站；而在考慮分群結果後，在 Accuracy、Prec

ision、Recall及 F1­score 等各項指標均高於其他工具。使用者問卷調查方面，在僅提供網站頁面的階段，判斷詐騙網站與否的正確率僅有 51%，而電商型式的詐騙網站甚至僅有 37%，而在提供了一系列的資訊及警告後，正確率來到了 75%，再加上附加的商工登記公示資料查詢以及提供國內相關法律及自救程序，最終有 91% 的使用者認為此框架對於判斷詐騙網站是有效的。

應用網路域名位置特徵於監督式機器學習的詐騙域名偵測

為了解決Scamadviser的問題，作者姚昭宇 這樣論述：

Ecommerce scam is a cybercrime that affects online consumer shoppers from nearly every country. Criminal groups implement deceiving ecommerce websites that lure consumers into purchasing their products, only to make away with the consumer’s money without giving the consumer what they had promised t

o sell them. Researchers have utilized a variety of domain features, from website HTML source code features to a domain’s DNS features to create frameworks that could identify ecommerce scam websites. However, much of the previous literature regarding this subject matter has neglected the potentiall

y advantageous use of a domain’s location data to differentiate ecommerce scam websites from benign ecommerce websites. In this thesis, to find novel ways to combat ecommerce scam, the potential application of a domain’s location data as novel features to detect ecommerce scam websites was investiga

ted.The first finding is that through testing with supervised machine learning models, it was discovered that our novel domain location features, in the form of domain location co-occurrences and geographical distances are effective features to detect ecommerce scam domains. Secondly, to our knowled

ge, we are the first researchers to have done a detailed analysis of domain location features between benign and scam ecommerce domains. To which, it was revealed that the location features of ecommerce scam domains, in comparison with benign ecommerce domains, tended to have much lower location co-

occurrences and larger location distances with the country that they were marketing towards. Thirdly, an analysis was performed on the location features in our dataset at a local country level and to our knowledge, we are the first researchers to reveal the current trends in domain location data for

ecommerce scam and benign websites in Taiwan. To which, it was discovered that ecommerce scam domains in Taiwan, in comparison to benign ecommerce domains in Taiwan, evidently possessed more location associations with China and less or none with Taiwan. Conversely, benign ecommerce domains in Taiwa

n, tended to have more location associations with Taiwan, and less or none with China. Therefore, this could serve as strong evidence to suspect that for foreign scam groups targeting a specific country, it is difficult, risky, and or costlier to ensure their scam domain’s various location data are

located in the target country. Hence, the novel domain location features introduced in this thesis appear to be viable features in the detection of ecommerce scam domains, since they are likely not domain data features that scam groups are able to adapt to at a whim to evade detection.