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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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