Scammer的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Hustle Hard

為了解決Scammer的問題，作者Saundra 這樣論述：

Three friends who escaped the streets. Now they're cashing in on a brilliant hustle and living their million-dollar dreams. But it may cost them their friendship--and their lives. For Isis, Secret, and Penny, the good times just keep getting better. As "managers" of Miami's hottest custom car sh

op, they're moving vast kilos of product faster than their scammer boss, Kirk, can pimp out rides--and living la vida luxurious. So they've got things under control when Secret and Penny's alcoholic mother shows up needing help. And when Isis' one-time love, Bobbi, begs for another chance--complete

with a huge payoff--she's got his loot on lock and temptation at arm's length ... But fast money brings ever more lethal problems. Penny falls into a vicious spiral of addiction no amount of cold hard cash can break. As Secret and Isis struggle to save her, a vengeful enemy goes hard after Kirk and

the business, putting all three women in the crosshairs. And as they fight to save what they've built, staying alive means risking unthinkable options--and betrayal no friendship can forgive ... "Tough, smart characters. . . . Fans of Cydney Rax will enjoy this fast-moving drama." --Booklist on Hu

stle Hard

Scammer進入發燒排行的影片

竟是一場空:網路愛情詐騙被害歷程之研究

為了解決Scammer的問題，作者池晉煒 這樣論述：

中文摘要國內在網路詐騙犯罪領域的相關研究中，對於透過網路進行之愛情詐騙，鮮少提及或討論。是故本研究以國內外先前網路詐欺研究之基礎，配合日常活動理論與生活方式暴露理論，採半結構式深度訪談之方法，對國內網路愛情詐騙被害者之被害歷程，進行質化個案訪談。以立意抽樣之方式，針對5名被害者進行訪談，根據訪談所得之內容，透過分析個案間被害歷程，建構出網路愛情詐騙之歷程圖，並提出適當建議。本研究發現，在基本資料及狀況方面，被害者均為中高齡女性，具有善良、無心機、易於相信他人等特質，且防詐意識薄弱，而其陷入網路愛情詐騙被害，似與感情生活不美滿有關。在日常生活型態方面，被害者生活型態與人際關係單純，且非網路重度

使用者。在被害歷程方面則可分為六階段：初相遇、初步認識、深入了解、愛戀與承諾、詐騙錢財、剩餘價值利用。一開始被害者均對詐騙集團所塑造或假冒之形象相當傾心，而後關係進展迅速，被害者均會遭遇對方動之以情及誘之以利兩種方式，並搭配假冒之重要配角(聯合國等)詐騙錢財，被害者羞於開口向同住親人求助。在被害者發覺遭詐時之情境與發現遭詐後之狀況方面，被害者均經由家人或朋友點醒或陪同報案，發現遭詐當下均充滿負面情緒，遭到詐騙後經濟狀況均有出現困頓。在防詐之作為、回應與建議方面，被害者對網路交友平台與金融機構較無建議，另建議執法單位可更加重視並加強詐騙案件之查緝，社政單位可提供被害者輔導與協助。最後，研究者依據

本研究之結果提出以下建議：與他國執法單位建立長期跨境合作查緝機制、執法單位與網路社群平台或通訊軟體系統商合作、建立通報機制並主動積極提供被害者相關諮商與輔導、進行男性被害者之研究並比較性別間被害歷程之異同。關鍵字：網路詐騙、愛情詐騙、被害歷程

Online Danger: How to Protect Yourself and Your Loved Ones from the Evil Side of the Internet

為了解決Scammer的問題，作者Cole, Eric 這樣論述：

Cyberspace is an informative, fun, and educational place for the entire family, but danger lurks everywhere you turn online. From phishing to cyber bullying to identity theft, there are a myriad of ways that you and your loved ones could be harmed online, often with irreparable damage. Fortunately,

there are precautions that everyone can take to protect themselves, their families, and their businesses that don't require advanced (or even any) technical knowledge. In this book, cyber security expert, Dr. Eric Cole, provides a layman's look at how to protect yourself online.Dr. Cole makes the ca

se that everyone is a potential target, not just the rich and famous. And we all have the ability to make it more difficult for cyber criminals to make us their next victim. He explains in plain language how to make your computer safer, protect your email, and guard your online accounts.Online Dange

r: How to Protect Yourself and Your Loved Ones from the Evil Side of the Internet applies to organizations as well as individuals, and across professions. Doctors, lawyers, and teachers - anyone responsible for safeguarding a client's or student's data - will learn something they can use right away.

Whether you're a parent wanting to know how to keep your children safe as they use the Internet, or a senior citizen who doesn't want to fall prey to an online scammer, or a daily technology user who wants to learn more about how to protect themselves in cyberspace, this book is for you.

人工智慧應用於技術支援詐騙偵測與特徵分析

為了解決Scammer的問題，作者陳昱蓁 這樣論述：

隨著資訊傳播越發快速，網路使用者接收各式各樣的資訊，其中難免夾雜惡意內容，通常搭配不同社交工程(Social Engineering)手法達到網路詐騙的目的。而駭客會將攻擊手法與時俱進以欺騙使用者藉以控制、竊取資料，並以此作為傳播惡意程式至終端設備的主要途徑。技術支援詐騙(Technical Support Scam, TSS)為近年較新穎的詐騙手法，使用者視窗會跳出終端系統商的警告畫面，並要求使用者撥打電話給技術支援，但其實是假警告，其目的為騙取使用者信任從而騙取維修費與相關個資，甚至於使用者終端植入惡意程式。本研究為防範「技術支援詐騙」網路威脅，基於行為特徵提出一智慧化學習系統-AI@T

SS系統，其概念結合網頁樣本分析、建立特徵與特徵評估等機制打造偵測模型，期望此模型能夠提供防毒軟體公司或相關業者在防禦技術支援詐騙之惡意攻擊時有更明確的策略。本研究收集惡性與技術支援詐騙網頁，並切割為訓練與測試資料集，其命名為AI@TSS資料集。為了驗證資料集可信度，本研究藉由Principle Component Analysis與Autoencoder降維機制繪製散點圖，從散點圖能證實AI@TSS資料集的樣本類別標記正確。為了將技術支援詐騙網頁從惡意網頁中偵測出來，本研究分析網頁行為並提出42項特徵，分別為惡意網頁偵測導向之host-based特徵與技術支援詐騙網頁偵測導向之code-ba

sed特徵，並經由LightGBM特徵評估機制證實本研究所提出的自定義特徵具有鑑別度。最終將最佳特徵組合所訓練出之偵測模型通過本研究的實際測試，其效能在LightGBM演算法之偵測模型可達到98%的準確度，Random Forest演算法之偵測模型可達到95.84%的準確度，而Deep Neural Network架構可達到93%的準確度。由數據結果可知，AI@TSS系統之偵測模型優於現有技術支援偵測方法。