8,738 research outputs found

    How to Hide a Clique?

    Get PDF
    In the well known planted clique problem, a clique (or alternatively, an independent set) of size k is planted at random in an Erdos-Renyi random G(n, p) graph, and the goal is to design an algorithm that finds the maximum clique (or independent set) in the resulting graph. We introduce a variation on this problem, where instead of planting the clique at random, the clique is planted by an adversary who attempts to make it difficult to find the maximum clique in the resulting graph. We show that for the standard setting of the parameters of the problem, namely, a clique of size k = ?n planted in a random G(n, 1/2) graph, the known polynomial time algorithms can be extended (in a non-trivial way) to work also in the adversarial setting. In contrast, we show that for other natural settings of the parameters, such as planting an independent set of size k = n/2 in a G(n, p) graph with p = n^{-1/2}, there is no polynomial time algorithm that finds an independent set of size k, unless NP has randomized polynomial time algorithms

    Measuring, Characterizing, and Detecting Facebook Like Farms

    Get PDF
    Social networks offer convenient ways to seamlessly reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, there is little work that systematically analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics, and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms which spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99% and 93% recall.Comment: To appear in ACM Transactions on Privacy and Security (TOPS

    The best practice for preparation of samples from FTA®cards for diagnosis of blood borne infections using African trypanosomes as a model system

    Get PDF
    Background: Diagnosis of blood borne infectious diseases relies primarily on the detection of the causative agent in the blood sample. Molecular techniques offer sensitive and specific tools for this although considerable difficulties exist when using these approaches in the field environment. In large scale epidemiological studies, FTA®cards are becoming increasingly popular for the rapid collection and archiving of a large number of samples. However, there are some difficulties in the downstream processing of these cards which is essential for the accurate diagnosis of infection. Here we describe recommendations for the best practice approach for sample processing from FTA®cards for the molecular diagnosis of trypanosomiasis using PCR. Results: A comparison of five techniques was made. Detection from directly applied whole blood was less sensitive (35.6%) than whole blood which was subsequently eluted from the cards using Chelex®100 (56.4%). Better apparent sensitivity was achieved when blood was lysed prior to application on the FTA cards (73.3%) although this was not significant. This did not improve with subsequent elution using Chelex®100 (73.3%) and was not significantly different from direct DNA extraction from blood in the field (68.3%). Conclusions: Based on these results, the degree of effort required for each of these techniques and the difficulty of DNA extraction under field conditions, we recommend that blood is transferred onto FTA cards whole followed by elution in Chelex®100 as the best approach

    Hawk\u27s Herald - April 2, 2005

    Get PDF
    • …
    corecore