HASLab - Indexed Articles in Journals

Permanent URI for this collection

http://localhost:4000/handle/123456789/167

Browse

Browsing HASLab - Indexed Articles in Journals by Author "3645"
  • Item
    Anomaly Detection and Modeling in 802.11 Wireless Networks
    ( 2019) Anisa Allahdadidastjerdi ; Ricardo Morla ; 3645 ; 5587
  • Item
    Does domain name encryption increase users' privacy?
    ( 2020) Soro,F ; Ricardo Morla ; Trevisan,M ; Drago,I ; Mellia,M ; 3645
  • Item
    Rapid detection of spammers through collaborative information sharing across multiple service providers
    ( 2019) Ricardo Morla ; Azad,MA ; 3645
    Spammers and telemarketers target a very large number of recipients usually dispersed across many Service Providers (SPs). Collaboration and Information sharing between SPs would increase the detection accuracy but detection effectiveness depends on the amount of information shared between SPs. Having service provider's exchange call detail records would arguably attain the best detection accuracy but would require significant network resources. Moreover, SPs are likely to feel uncomfortable in sharing their call records because call records contain user's private information as well as operational details of their networks. The challenge towards the design of collaborative Spam over Internet Telephony (SPIT) detection system is two-fold: it should attain high detection accuracy with a small false positive, and should fully protect the privacy of users and their service providers. In this paper, we propose a COllaborative Spit Detection System (COSDS)-a collaborative SPIT detection system for the Voice over IP (VoIP) network where service providers collaborate for the effective and early detection of SPIT callers without raising privacy concerns. To this extent, COSDS relies on a trusted Centralized Repository (CR) and exchange of non-sensitive reputation scores. The CR computes global reputation of users by aggregating the reputation scores provided by the respective collaborating SPs. The data exchanged to the CR is not sensitive regarding users privacy, and cannot be used to infer the relationship network of users. We evaluate the performance of our system using synthetic data that we have generated by simulating the realistic social behavior of spammers and non-spammers in a network. The results show that the COSDS approach has better detection accuracy as compared to the traditional stand-alone detection systems. For instances, in a setup where spammers are making calls to recipients of many SPs, COSDS successfully identifies spammers with the True Positive (TP) rate of around 80% and false positive (FP) rate of around 2% on a first day, which further increases to 100% TP rate and zero FP rate in three days. COSDS approach is fast, requires a small communication overhead, ensures privacy of users and collaborating SP, and requires only few iterations for the reputation convergence within the SP. © 2018 Elsevier B.V.