Download PDF by Markus Jakobsson: Understanding Social Engineering Based Scams

By Markus Jakobsson

This ebook describes tendencies in e-mail scams and provides instruments and techniques

to determine such tendencies. It additionally describes automatic countermeasures

based on an figuring out of the kind of persuasive equipment used by

scammers. It experiences either consumer-facing scams and company scams,

describing in-depth case experiences with regards to Craigslist scams and Business

Email Compromise Scams. This ebook offers a superb start line for

practitioners, determination makers and researchers in that it includes

alternatives and complementary instruments to the at present deployed email

security instruments, with a spotlight on figuring out the metrics of scams.

Both execs operating in protection and advanced-level students

attracted to privateness or purposes of machine technological know-how will locate this book

a necessary reference.

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Understanding Social Engineering Based Scams - download pdf or read online

This e-book describes tendencies in e mail scams and gives instruments and techniquesto determine such developments. It additionally describes computerized countermeasuresbased on an knowing of the kind of persuasive equipment used byscammers. It stories either consumer-facing scams and firm scams,describing in-depth case reports in terms of Craigslist scams and BusinessEmail Compromise Scams.

Additional info for Understanding Social Engineering Based Scams

Sample text

3, identity theft was one of the most frequently reported scams until 2010 and was not included afterwards. References 19 References 1. M. Beals, M. DeLiema, M. Deevy, Framework for a taxonomy of fraud. pdf (2015) 2. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 3. Federal Bureau of Investigation, Internet Crime Complaint Center (IC3) annual reports. aspx 4. M. Jakobsson, Z. Ramzan, Crimeware: Understanding New Attacks and Defenses, 1st edn. (Addison-Wesley Professional, Indianapolis, 2008) 5.

Assume we want to measure the likely yield of two potential phishing attacks with slightly different messaging. The first attack may be the phishing attack shown in Fig. 2 Measuring Credibility 23 Fig. 1 Assessing message credibility. The figure shows a phishing email that is commonly used by scammers to gain a foothold in a targeted enterprise by gaining access to the email account of a user Fig. 2 Assessing message credibility. The figure shows a new variant of a scam that has existed for more than 10 years, in which the recipient is asked to respond to a survey in return for a financial award.

2. Say that we wish to know which pitch is likely to be most successful. We will describe the “modular” approach to determining the likely yield of each of the variants. This is based on asking a sufficiently large number of “test takers” what the primary risk associated with the email is—preferably for a large number of emails, some of which are not risky at all—where the subjects select the primary risk from a list. See Fig. 1 for the first list of potential answers that the subject can choose from.

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