No abstract

Abstract. In this paper, we report that we have solved the SVP Challenge over a 128-dimensional lattice in Ideal Lattice Challenge from TU Darmstadt, which is currently the highest dimension in the challenge that has ever been solved. The security of lattice-based cryptography is based on the hardness of solving the shortest vector problem (SVP) in lattices. In 2010, Micciancio and Voulgaris proposed a Gauss Sieve algorithm for heuristically solving the SVP using a list L of Gaussreduced vectors. Milde and Schneider proposed a parallel implementation method for the Gauss Sieve algorithm. However, the efficiency of the more than 10 threads in their implementation decreased due to the large number of non-Gauss-reduced vectors appearing in the distributed list of each thread. In this paper, we propose a more practical parallelized Gauss Sieve algorithm. Our algorithm deploys an additional Gauss-reduced list V of sample vectors assigned to each thread, and all vectors in list L remain Gauss-reduced by mutually reducing them using all sample vectors in V . Therefore, our algorithm allows the Gauss Sieve algorithm to run for large dimensions with a small communication overhead. Finally, we succeeded in solving the SVP Challenge over a 128-dimensional ideal lattice generated by the cyclotomic polynomial x 128 + 1 using about 30,000 CPU hours.

No abstract

Phishing attacks, which steal users' account information by fake websites, have become a serious problem on the Internet. There are two major approaches in phishing detection: the blacklist-and the heuristics-based approach. Heuristicsbased approaches employ common characteristics of phishing sites such as distinctive keywords used in web pages or URLs in order to detect new phishing sites that are not yet listed in blacklists. However, these kinds of heuristics can be easily circumvented by phishers once their mechanism is revealed. In order to overcome this weakness, visual similarity-based detection techniques have been proposed. Because phishing sites have to mimic victim sites, visual similarity between phishing sites and their victim sites is supposed to be an inherent and not easily concealable characteristic. However, these techniques require images of real victim sites for detection.In this paper, we propose a phishing detection mechanism based on visual similarity among phishing sites that mimic the same victim site. Surprisingly, just by analyzing visual similarity among web pages without a priori knowledge, our method automatically extracts 224 distinct web page layouts mimicked by 2,262 phishing sites and achieves a detection rate of over 80 % while keeping the false-positive rate to 17.5 %. We also find that the false-positive rate can be reduced.

No abstract