Post-graduate theses
Current Record: 5042 of 6695
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Identifier |
000417253 |
Title |
Detecting traffic anomalies at colocation facilities |
Alternative Title |
Ανίχνευση ανωμαλιών κίνησης δεδομένων σε εγκαταστάσεις σύζευξης |
Author
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Μηλολιδάκης, Αλέξανδρος Κ.
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Thesis advisor
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Δημητρόπουλος, Ξενοφώντας
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Reviewer
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Παπαδοπούλη, Μαρία
Μαρκάτος, Ευάγγελος
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Abstract |
Internet eXchange Points (IXPs) as core parts of the Internet infrastructure, hosted at Colocation Facilities (Colos), facilitate the exchange of Terabytes of traffic on a daily basis by big Internet Service Providers (ISPs). Offering flexibilities, routing benefits and a safe place for operators to install their equipment, Colos provide the ideal location where new peering relations are formed. Although the equipment is usually well preserved, major traffic anomalies between Autonomous Systems (ASes) over facility peering links can take place. This thesis aims to detect anomalies at Colos and measure the impact on traffic traversing the affected entity. To achieve this, i) we use data plane information from the facilities where IXPs are located (e.g., IP addresses), ii) we utilize daily traceroute snapshots from the RIPE Atlas measurement platform to identify the facilities the traffic goes through and iii) we use statistical methods to detect unusual delay discrepancies and routing anomalies over the facility peering links. Using our system, we analyzed a timeframe between May and December 2015 ranking the observed alarms to infer significant disruptions. To demonstrate our system, we present and validate three cases: an IXP outage, a DDoS attack and a power failure in a colocation facility indicating that our proposed methods are able to detect real world outages. Furthermore, we map Colos to their metropolitan area and assess the impact of each alarm in neighboring facilities of the same area. Our results also show a time window ('the hours' around midnight) that has a higher probability of triggering an alarm, possible due to planned maintenance.
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Language |
English |
Subject |
Anomaly detection |
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IXPs |
Issue date |
2018-07-20 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/f/4/d/metadata-dlib-1531915143-680859-26774.tkl
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Views |
813 |