Titlebook: Understanding Social Engineering Based Scams; Markus Jakobsson Book 2016 Springer Science+Business Media New York 2016 419 Scam.Scam.Busin

Inflamed

Identifying Scams and Trendsit to cluster scam emails into major scam categories. Then an analysis of different trends from each scam category is presented. Our analysis shows a clear trend that spam-like . are decreasing continuously while . with specific victims have been getting more prevalent over the last 10 years.

食草

Persuasion in Scamsdistinct trends in their usage. We argue that with a better understanding of how scammers work at a psychological level, one could devise new techniques to detect persuasion in scam emails and build tools that more closely emulate human interaction with those emails.

委托

Scams and Targeting,m filters and related technologies, and as such, vastly improves the profits scammers reap. We overview how to estimate the yield of attacks, and how to identify scams that are likely to become more common.

critic

Identifying Scams and Trends a large-scale compendium of scam emails collected from various sources, and then present an analysis regarding what kind of scams exist, what their structures are, and how they are related to each other. We then describe a machine learning classifier built based upon the taxonomy analysis, and use

死亡

BACLE

Persuasion in Scamsf human persuasion that they integrate. We discuss and compare both the terms and principles used over time within a sample of scam emails collected between 2006 and 2014. Our analyses shows that different scam email categories use various principles of persuasion and that it is possible to observe

Ganglion-Cyst

Traditional Countermeasures to Unwanted EmailKIM, SPF, DMARC), blacklisting (e.g., DNSBL), and content-based spam filtering (e.g., Naive Bayes Classifier). We explain the extent to which they can be useful to block scam, and point out evasion techniques that help spammers and scammers survive.

indubitable

Obfuscation in Spam and Scamor to modify any message to a homograph version of the same message, thereby avoiding digest and signature based detection methods. We measure the success of this potential attack, showing a total success against Hotmail, Gmail and Yahoo mail. While the attack is bothersome both in terms of its simp

引起

Proclaim

Case Study: Sales Scamsopular online market websites, with over 60 million monthly visitors in the U.S. alone. In spite of the prevalence of scams on Craigslist, the community’s understanding of these is still very much lacking, and in this chapter and the two chapters following it, we present in-depth measurement studies