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Improving anomaly detection in border gateway protocol time-series data / Mahmoud Elsayed Hashem Mahmoud ; Supervised Samir Ibrahim Shaheen , Ahmed Refaat Bashandy

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mahmoud Elsayed Hashem Mahmoud , 2020Description: 132 P. : charts , facimiles ; 30cmOther title:
  • تحسين الكشف عن الشذوذ في بيانات السلاسل الزمنية لبروتوكول البوابة الحدودية [Added title page title]
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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide basic routing information. Where the abnormal routing events, resulting from direct or indirect anomalies, weaken the Internet connection and network balance. For this purpose, we have proposed several algorithms to improve the detection of anomalies in BGP time-series data. Our methods show that detected anomalies are more realistic and that the selected features are generally consistent across time-series. The performance evaluation is offered using various machine learning techniques
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.Ph.D.2020.Ma.I (Browse shelf(Opens below)) Not for loan 01010110082675000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.Ph.D.2020.Ma.I (Browse shelf(Opens below)) 82675.CD Not for loan 01020110082675000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide basic routing information. Where the abnormal routing events, resulting from direct or indirect anomalies, weaken the Internet connection and network balance. For this purpose, we have proposed several algorithms to improve the detection of anomalies in BGP time-series data. Our methods show that detected anomalies are more realistic and that the selected features are generally consistent across time-series. The performance evaluation is offered using various machine learning techniques

Issued also as CD

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