References of "Gueye, Bamba"
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See detailPath Similarity Evaluation using Bloom Filters
Donnet, Benoît ULg; Gueye, Bamba,; Kaafar, Mohamed Ali

in Computer Networks (2012), 56(2), 858-869

The performance of several Internet applications often relies on the measurability of path similarity between different participants. In particular, the performance of content distribution networks mainly ... [more ▼]

The performance of several Internet applications often relies on the measurability of path similarity between different participants. In particular, the performance of content distribution networks mainly relies on the awareness of content sources topology information. It is commonly admitted nowadays that, in order to ensure either path redundancy or efficient content replication, topological similarities between sources is evaluated by exchanging raw traceroute data, and by a hop by hop comparison of the IP topology observed from the sources to the several hundred or thousands of destinations. In this paper, based on real data we collected, we advocate that path similarity comparisons between different Internet entities can be much simplified using lossy coding techniques, such as Bloom filters, to exchange compressed topology information. The technique we introduce to evaluate path similarity enforces both scalability and data confidentiality while maintaining a high level of accuracy. In addition, we demonstrate that our technique is scalable as it requires a small amount of active probing and is not targets dependent. [less ▲]

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See detailIP Geolocation Databases: Unreliable?
Poese, Ingmar; Uhlig, Steve; Kaafar, Mohamed Ali et al

in Computer Communication Review (2011), 41(2), 53-56

The most widely used technique for IP geolocation con- sists in building a database to keep the mapping between IP blocks and a geographic location. Several databases are available and are frequently used ... [more ▼]

The most widely used technique for IP geolocation con- sists in building a database to keep the mapping between IP blocks and a geographic location. Several databases are available and are frequently used by many services and web sites in the Internet. Contrary to widespread belief, geolo- cation databases are far from being as reliable as they claim. In this paper, we conduct a comparison of several current geolocation databases -both commercial and free- to have an insight of the limitations in their usability. First, the vast majority of entries in the databases refer only to a few popular countries (e.g., U.S.). This creates an imbalance in the representation of countries across the IP blocks of the databases. Second, these entries do not re- flect the original allocation of IP blocks, nor BGP announce- ments. In addition, we quantify the accuracy of geolocation databases on a large European ISP based on ground truth information. This is the first study using a ground truth show- ing that the overly fine granularity of database entries makes their accuracy worse, not better. Geolocation databases can claim country-level accuracy, but certainly not city-level. [less ▲]

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See detailDetecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning
Liao, Yongjun ULg; Kaafar, Mohamed Ali; Gueye, Bamba et al

in Lecture Notes in Computer Science (2009, May 12), 5550

Internet Coordinates Systems (ICS) are used to predict Internet distances with limited measurements. However the precision of an ICS is degraded by the presence of Triangle Inequality Violations (TIVs ... [more ▼]

Internet Coordinates Systems (ICS) are used to predict Internet distances with limited measurements. However the precision of an ICS is degraded by the presence of Triangle Inequality Violations (TIVs). Simple methods have been proposed to detect TIVs, based e.g. on the empirical observation that a TIV is more likely when the distance is underestimated by the coordinates. In this paper, we apply supervised machine learning techniques to try and derive more powerful criteria to detect TIVs. We first show that (ensembles of) Decision Trees (DTs) learnt on our datasets are very good models for this problem. Moreover, our approach brings out a discriminative variable (called OREE), which combines the classical estimation error with the variance of the estimated distance. This variable alone is as good as an ensemble of DTs, and provides a much simpler criterion. If every node of the ICS sorts its neighbours according to OREE, we show that cutting these lists after a given number of neighbours, or when OREE crosses a given threshold value, achieves very good performance to detect TIVs. [less ▲]

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