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new workshop paper “Assessing Co-Locality of IP Blocks” in GI 2016

The paper “Assessing Co-Locality of IP Blocks” appeared in the 19th IEEE  Global Internet Symposium on April 11, 2016 in San Francisco, CA, USA and is available at (http://www.cs.colostate.edu/~manafgh/publications/Assessing-Co-Locality-of-IP-Block-GI2016.pdf). The datasets are available at (https://ant.isi.edu/datasets/geolocation/).

From the abstract:

isi_all_blocks_clustersCountMany IP Geolocation services and applications assume that all IP addresses within the same /24 IPv4 prefix (a /24 block) reside in close physical proximity. For blocks that contain addresses in very different locations (such as blocks identifying network backbones), this assumption can result in a large geolocation error. In this paper we evaluate the co-location assumption. We first develop and validate a hierarchical clustering method to find clusters of IP addresses with similar observed delay measurements within /24 blocks. We validate our methodology against two ground-truth datasets, confirming that 93% of the identified multi-cluster blocks are true positives with multiple physical locations and an upper bound for false positives of only about 5.4%. We then apply our methodology to a large dataset of 1.41M /24 blocks extracted from a delay-measurement study of the entire responsive IPv4 address space. We find that about 247K (17%) out of 1.41M blocks are not co-located, thus quantifying the error in the /24 block co-location assumption.

The work in this paper is by Manaf Gharaibeh, Han Zhang, Christos Papadopoulos (Colorado State University) and John Heidemann (USC/ISI).

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Papers Publications

new workshop paper “BotDigger: Detecting DGA Bots in a Single Network” in TMA 2016

The paper “BotDigger: Detecting DGA Bots in a Single Network” has appeared at the TMA Workshop on April 8, 2016 in Louvain La Neuve, Belgium (available at http://www.cs.colostate.edu/~hanzhang/papers/BotDigger-TMA16.pdf).

The code of BotDigger is available on GitHub at: https://github.com/hanzhang0116/BotDigger

From the abstract:

To improve the resiliency of communication between bots and C&C servers, bot masters began utilizing Domain Generation Algorithms (DGA) in recent years. Many systems have been introduced to detect DGA-based botnets. However, they suffer from several limitations, such as requiring DNS traffic collected across many networks, the presence of multiple bots from the same botnet, and so forth. These limitations mBotDiggerOverviewake it very hard to detect individual bots when using traffic collected from a single network. In this paper, we introduce BotDigger, a system that detects DGA-based bots using DNS traffic without a priori knowledge of the domain generation algorithm. BotDigger utilizes a chain of evidence, including quantity, temporal and linguistic evidence to detect an individual bot by only monitoring traffic at the DNS servers of a single network. We evaluate BotDigger’s performance using traces from two DGA-based botnets: Kraken and Conflicker. Our results show that BotDigger detects all the Kraken bots and 99.8% of Conficker bots. A one-week DNS trace captured from our university and three traces collected from our research lab are used to evaluate false positives. The results show that the false positive rates are 0.05% and 0.39% for these two groups of background traces, respectively.

The work in this paper is by Han Zhang, Manaf Gharaibeh, Spiros Thanasoulas, and Christos Papadopoulos (Colorado State University).