Index_T

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S

SAN (storage area network) devices, 327

scam baiting, 242–254

scamming spam

advance fee payments, 238–242

419 scams, 238–254, 363

fraudulent charity donations, 237–238

phishing attacks, 231–238

real-world scam-baiting example, 242–254

role of mules, 229–231

scripts, CGI, role in sending spam, 53–62

scripts, security flaw, 75

Secure BGP, 68

Secure Sockets Layer (SSL), 359

security flaws

finding out about, 61

newsletter list example, 75

self-help Web sites, 132

send-safe.com, 39

Sender ID, 220–225, 271

Sender Policy FrameWork (SPF), 220–225, 271

sendmail, 36, 37–38, 81

server-side spam filtering, 381–398

Service Set Identifiers (SSIDs), 63

sexual content. See Label for E-mail Messages Containing Sexually Oriented Material Act

sexual performance enhancement products

beating Bayesian filters, 217–220

and personal insecurities, 98

as popular spam item, 337

spam example, 296–305

Shim, Choon, 350

signatures, PGP, 176–177

Simple Mail Transfer Protocol (SMTP)

future, 352

role in sending spam, 36–39

SmartScreen, 372–381

Smathers, Jason, 92

SMTP (Simple Mail Transfer Protocol)

future, 352

role in sending spam, 36–39

snail mail addresses, 112, 113

Socks protocol, 32

SocksChain, 35

software, counterfeit, 360

spackers, 72–76

spam

analyzing, 290–318

bandwidth and storage aspects, 324–327

blocking, 151–170

bounty hunters, 274

as a business, 14–16

calculating true cost, 320–334

closing comments, 367–369

common sending methods, 32–68

defeating filters, 171–201

designing successful e-mail messages, 98–102

devising reply addresses, 175–176

devising subject lines, 177–180

effect on mail servers, 322–323

effect on time for “real” work, 320–324

example of poorly constructed message, 100, 101

example of well-constructed message, 99, 100

FAQs, 358–365

financial aspects, 126–129

finding products or services to sell, 18–21

format comparison, 102–107

future, 346–355

global aspects, 272–274

as great circle, 86

hard copy vs. electronic, 265

hatred of, 16

history, 78–79

host providers, 115–118

how it works, 17–27

impact of using whitelists, 166

innocent-looking, 174–189

legal aspects, 282, 351–352

legitimate vs. phishing, 228–229

as marketing, 15–16

mindset required for sending, 30–32

“Mort gageQuotes” example, 291–296

overview, 72, 228, 290–291

perfect message example, 312–318

products that sell, 97–98

race between spammers and anti-spam groups, 30

random data in messages, 113–115, 123

random words example, 305–312

reducing amount received, 109–110

responding to, 96–97

sample scenario for generating and sending, 17–27

sexual performance enhancement example, 296–305

size range, 325–326

statistics, 334–344

statistics on amounts, 340–344

statistics on senders, 338–340

statistics on top sending countries, 334–335

statistics on types sent, 336–337

statistics on yearly trends, 341–344

total cost example, 327–332

SPAM, as prefix on subject line, 363

Spam Assassin, 161–165, 170, 200, 225

Spam Cartel, 2

spam filters

and 419 scams, 240–242

Bayesian overview, 166–169

beating Bayesian filters, 215–220

client-side, 372–381

combining types, 169–170

default whitelists for, 213–215

effect on spam statistics, 321

evading SPF-based technology, 223–225

hash-based, 171–201, 318

host-based, 151–161

how to evade, 171–201

and HTML messages, 106–107

intelligent, 204–205, 353–354

mixing and matching, 169–170

network-based, 151–161

overview, 151

rule-based, 161–170

server-side, 381–398

vs. spammers, 204–205

using noise, 205–213

spam-hashing applications, 160–161, 170

Spam over Internet Telephony (SPIT), 349–351

spam providers

role in sending spam, 39–41

trustworthiness of, 40

spam scams, 228–254

spam-sending companies

role in sending spam, 39–41

trustworthiness of, 40

SpamArrest.com, 165–166

Spamcop, 358, 386

SpamHaus, 189–191

spammers

vs. anti-spam groups, 30

buying mailing lists from spackers, 72–76

corporate, 92–94, 116–118

and ethics, 31

getting paid, 126–129

hatred of, 16

how worthwhile is it, 364

legal cases against, 279–287

relationship to hackers, 72–76

suing, 364

trustworthiness of, 18, 40

SpamNet, 381

SPF (Sender Policy FrameWork), 220–225, 271

SPIT (Spam over Internet Telephony), 349–351

Squid, 32

SSIDs (Service Set Identifiers), 63

SSL (Secure Sockets Layer), 359

statistical probabilities, 166–169

stock trading accounts, phishing for, 231–238

storage charges, 324–327

Sub-7, 41–42

subject lines, 177–180

suing spammers, 364

system administrators, 31

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