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008 150528s2014 ua h f m 000 0 eng d
040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aPh.D
099 _aCai01.20.01.Ph.D.2014.Am.M
100 0 _aAmira Sayed Abdelaziz Aly
245 1 0 _aMultiagent artificial immune system for network intrusion detection /
_cAmira Sayed Abdelaziz Aly ; Supervised Sanaa Elola Hanafi , Aboulella Hassanien
246 1 5 _aنظام اصطناعي مؤمن متعدد الوكلاء لاكتشاف اختراق الشبكات
260 _aCairo :
_bAmira Sayed Abdelaziz Aly ,
_c2014
300 _a120 Leaves :
_bfacsimiles ;
_c30cm
502 _aThesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Technology
520 _aWith the expanding and increasing use of networks and accumulating number of inter- net users, network throughput has become massive and threats are more diverse and sophisticated. Network and information security are of high importance, and research is continuous in these {uFB01}elds to keep up with the increasing complexity of attacks. Intrusion Detection is a major research area that aims to identify suspicious activities in a moni- tored system, from authorized and unauthorized users, by monitoring and analyzing the system activities. In this thesis, a multi-agent network intrusion detection system is implemented, inspired by a biological immunity technique called the Negative Selection Approach. The system detects network tra{uFB03}c anomalies using detectors generated by a genetic algorithm with deterministic crowding Niching technique. As it is inspired by the negative selection mechanism of the immune system, it can detect foreign patterns in the complement (non-self) space
530 _aIssued also as CD
653 4 _aArtificial immune systems
653 4 _aMultiagent artificial immune system
653 4 _aNetwork intrusion detection
700 0 _aAboulella Hassanien ,
_eSupervisor
700 0 _aSanaa Elola Hanafi ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aAml
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
999 _c51074
_d51074