| 000 | 01638cam a2200325 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223030338.0 | ||
| 008 | 110119s2010 ua e f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.13.06.M.Sc.2010.Kh.N | ||
| 100 | 0 | _aKhaled Mohammad Ibraheem Mohammad | |
| 245 | 1 | 2 |
_aA new real-time decision tree algorithm for intrusion detection / _cKhaled Mohammad Ibraheem Mohammad ; Supervised Nevin Mahmoud Darwish |
| 246 | 1 | 5 | _aخوارزم جديد لإنشاء شجرة القرارات في الوقت الحقيقي لكشف التسلل عبر الشبكات |
| 260 |
_aCairo : _bKhaled Mohammad Ibraheem Mohammad , _c2010 |
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| 300 |
_a125 P. : _bplans ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering | ||
| 520 | _aMinimizing the time needed for taking decisions usually improves system performance. Computer anti viruses for example try to detect viruses automatically. If the virus detection needs complex computations, it may imporove the detection accuracy but system performance will dramatically decrease. In the same way, network intrusion detection requires taking a fast decision to determine whether there is intrusion or not | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aData integration | |
| 653 | 4 | _aDecision trees | |
| 653 | 4 | _aFraud detection | |
| 700 | 0 |
_aNevin Mahmoud Darwish , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aSamia _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c32749 _d32749 |
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