Index_I


I

IBM

Datajoiner, 71

Intelligent Miner for Text, 147

Icmpenum, 305

iCrossReader, 148–49

defined, 148

TextRoller, 148–49

See also Text mining tools

Identities

crimes of, 291–92

tracking, 336–37

IDNET-INS, 53

IF/THEN rules, 208, 230, 231

Illegal billing, 282

Incidents.org, 326

InfoGIST, 122

Information retrieval, 140

InfoSysSec, 326

Insurance fraud, 281–87

cost estimation, 281

death claims case study, 287–88

defined, 281

detection methods, 286–87

excessive/inappropriate testing, 283–84

false claims, 282

illegal billing, 282

miscoding, 284–85

personal injury mills, 284

Intelligent agents, 2, 6–8, 107–23

abilities, 6

autonomy, 109

bio-surveillance case study, 117–20

communication, 110

database, 114

data mining, 120

defined, 6, 108–9

development kits, 121

features, 109–11

functioning of, 113–14

functions of, 107–8

illustrated, 7

importance, 111–12

information retrieval, 121, 122

intelligence, 111, 116–17

Internet, 112–13

intranet, 113

investigative mining tasks, 108

monitoring, 121

open sources, 112–13

perception, 109–10

purpose, 110

reasoning, 114–16

search engines vs., 123

secured sources, 113

technology, 108

tools, 121–23

Interactive crime maps, 345–46

GIS, 345

illustrated, 346

See also Crime maps

Internet agents, 112–13

Internet data, 55–59

cookies, 57–58

defined, 55–56

forms, 59

log files, 56–57

Web bugs, 59

See also Data

Internet forms, 59

Interstate Identification Index (III), 52

Intranet agents, 113

Introduction to Intrusion Detection Systems, An, 326

Intrusion

patterns, 309

types, 302

Intrusion detection, 301–26

anomaly, 309–10

audit data and, 311

cybercrimes and, 301–2

Internet resources, 326

misuse, 310

MITRE case study, 313–18

Intrusion detection systems (IDSs), 302, 310–13

advanced, 323–24

advantages, 311

anomaly, 319–20

categorization, 318

data mining, 321–23

defined, 310–11

host-based, 318

hybrid, 321

issues addressed by, 312

with machine-learning algorithms, 322

metalearning, 323

misuse, 318–19

multiple-based, 321

network-based, 318, 321

with neural networks, 322

system errors and, 312

system monitoring, 311–12

types of, 318

uses, 311

Intrusion MOs, 302–9

attack, 307

control, 307

intelligence, 303–5

probing, 306

scanning, 305–6

stealth, 308–9

Investigative data warehousing, 4–5, 39–74




Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
ISBN: 0750676132
EAN: 2147483647
Year: 2005
Pages: 232
Authors: Jesus Mena

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net