SCAMSTOP : Scams and Fraud Detection in Voice over IP Networks FP7 contract number: 232458 D2.2 General anti-Fraud security framework for VoIP infrastructures
Identifieur interne : 002954 ( Main/Exploration ); précédent : 002953; suivant : 002955SCAMSTOP : Scams and Fraud Detection in Voice over IP Networks FP7 contract number: 232458 D2.2 General anti-Fraud security framework for VoIP infrastructures
Auteurs : Rebahi Yacine ; Mohamed Nassar [France] ; Dagiuklas Tasos ; Olivier Festor [France] ; Alefragkis Panayiotis ; Polyzos George ; Gogos ChristosSource :
Descripteurs français
- Wicri :
- topic : Fraude.
English descriptors
- mix :
Abstract
Based on the results achieved in the previous deliverable D2.1 where the fraud problem was discussed in details and an overview of the existing anti-fraud techniques was provided, the SCAMSTOP architecture is investigated and described in this document. The SCAMSTOP architecture has adopted a modular vision which allows the proposed solutions to be updated, upgraded and adjusted to the needs of the VoIP providers without any difficulty. One of these modules reflects the specification of a user signature where the behavior of this user is summarized through some statistical parameters that allow tracking the user “normal” behavior. Related issues such as signature initialization, definition, update and comparison were investigated. The integration of Intrusion Detection Systems with Fraud exploration is also one the main milestones of the SCAMSTOP architecture. The SCAMSTOP framework also took into account the user context when building the signature, for instance travels and holidays. This was done to have signatures close as much as possible to real live scenarios. Another aspect that was also considered is the correlation of the different fraud alerts that might be generated due to the use of various detection modules either in sequence or in parallel. As users „ data is mined at some extend, this deliverable also discussed the related privacy issues.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 004303
- to stream Hal, to step Curation: 004303
- to stream Hal, to step Checkpoint: 002190
- to stream Main, to step Merge: 002A17
- to stream Main, to step Curation: 002954
Le document en format XML
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sort="Panayiotis, Alefragkis" uniqKey="Panayiotis A" first="Alefragkis" last="Panayiotis">Alefragkis Panayiotis</name></author><author><name sortKey="George, Polyzos" sort="George, Polyzos" uniqKey="George P" first="Polyzos" last="George">Polyzos George</name></author><author><name sortKey="Christos, Gogos" sort="Christos, Gogos" uniqKey="Christos G" first="Gogos" last="Christos">Gogos Christos</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Fraud</term><term>Fraud detection</term><term>Security</term><term>VoIP</term><term>fraud data collection</term><term>neural networks</term><term>rule-based detection</term><term>service misuse</term><term>user profiling</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Fraude</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Based on the results achieved in the previous deliverable D2.1 where the fraud problem was discussed in details and an overview of the existing anti-fraud techniques was provided, the SCAMSTOP architecture is investigated and described in this document. The SCAMSTOP architecture has adopted a modular vision which allows the proposed solutions to be updated, upgraded and adjusted to the needs of the VoIP providers without any difficulty. One of these modules reflects the specification of a user signature where the behavior of this user is summarized through some statistical parameters that allow tracking the user “normal” behavior. Related issues such as signature initialization, definition, update and comparison were investigated. The integration of Intrusion Detection Systems with Fraud exploration is also one the main milestones of the SCAMSTOP architecture. The SCAMSTOP framework also took into account the user context when building the signature, for instance travels and holidays. This was done to have signatures close as much as possible to real live scenarios. Another aspect that was also considered is the correlation of the different fraud alerts that might be generated due to the use of various detection modules either in sequence or in parallel. As users „ data is mined at some extend, this deliverable also discussed the related privacy issues.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><noCountry><name sortKey="Christos, Gogos" sort="Christos, Gogos" uniqKey="Christos G" first="Gogos" last="Christos">Gogos Christos</name><name sortKey="George, Polyzos" sort="George, Polyzos" uniqKey="George P" first="Polyzos" last="George">Polyzos George</name><name sortKey="Panayiotis, Alefragkis" sort="Panayiotis, Alefragkis" uniqKey="Panayiotis A" first="Alefragkis" last="Panayiotis">Alefragkis Panayiotis</name><name sortKey="Tasos, Dagiuklas" sort="Tasos, Dagiuklas" uniqKey="Tasos D" first="Dagiuklas" last="Tasos">Dagiuklas Tasos</name><name sortKey="Yacine, Rebahi" sort="Yacine, Rebahi" uniqKey="Yacine R" first="Rebahi" last="Yacine">Rebahi Yacine</name></noCountry><country name="France"><region name="Grand Est"><name sortKey="Nassar, Mohamed" sort="Nassar, Mohamed" uniqKey="Nassar M" first="Mohamed" last="Nassar">Mohamed Nassar</name></region><name sortKey="Festor, Olivier" sort="Festor, Olivier" uniqKey="Festor O" first="Olivier" last="Festor">Olivier Festor</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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