The rise in phishing attacks via e-mail and short message service (SMS) has not slowed down at all. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases that reach end users. Without understanding these characteristics, anti-phishing countermeasures cannot evolve. In this study, we propose an approach using Twitter as a new observation point to immediately collect and characterize phishing cases via e-mail and SMS that evade countermeasures and reach users. Specifically, we propose CrowdCanary, a system capable of structurally and accurately extracting phishing information (e.g., URLs and domains) from tweets about phishing by users who have actually discovered or encountered it. In our three months of live operation, CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports, 31,960 (90.2%) of these phishing URLs were later detected by the anti-virus engine. We analyzed users who shared phishing threats by categorizing them into two groups: experts and non-experts. As a results, we discovered that CrowdCanary extracts non-expert report-specific information, like company brand name in tweets, phishing attack details from tweet images, and pre-redirect landing page information.
The paper describes a method for classifying the motions of human bodies in an image sequence. First, a set of templates is prepared in advance, which includes the spatio-temporal Gabor features of key motions. Next, processing is performed to obtain the Gabor features of all unknown motion. Correlation coefficients between the feature vectors of both the key motions and the unknown motions are then calculated by using dynamic programming (DP), and finally the unknown motion is classified as one of the key motions. This study also compares the effectiveness between Gabor features and principal component analysis (PCA) for sequences of postures. Experimental results using image sequences from a volleyball game show the effectiveness of the proposed method.
Domain squatting is a technique used by attackers to create domain names for phishing sites. In recent phishing attempts, we have observed many domain names that use multiple techniques to evade existing methods for domain squatting. These domain names, which we call generated squatting domains (GSDs), are quite different in appearance from legitimate domain names and do not contain brand names, making them difficult to associate with phishing. In this paper, we propose a system called PhishReplicant that detects GSDs by focusing on the linguistic similarity of domain names. We analyzed newly registered and observed domain names extracted from certificate transparency logs, passive DNS, and DNS zone files. We detected 3,498 domain names acquired by attackers in a four-week experiment, of which 2,821 were used for phishing sites within a month of detection. We also confirmed that our proposed system outperformed existing systems in both detection accuracy and number of domain names detected. As an in-depth analysis, we examined 205k GSDs collected over 150 days and found that phishing using GSDs was distributed globally. However, attackers intensively targeted brands in specific regions and industries. By analyzing GSDs in real time, we can block phishing sites before or immediately after they appear.
This paper describes OSCAR multigrain parallelizing compiler which has been developed in Japanese Millennium Project IT21 "Advanced Parallelizing Compiler" project and its performance on SMP machines. The compiler realizes multigrain parallelization for chip-multiprocessors to high-end servers. It hierarchically exploits coarse grain task parallelism among loops, subroutines and basic blocks and near fine grain parallelism among statements inside a basic block in addition to loop parallelism. Also, it globally optimizes cache use over different loops, or coarse grain tasks, based on the data localization technique to reduce memory access overhead. Current performance of OSCAR compiler for SPEC95fp is evaluated on different SMPs. For example, it gives us 3.7 times speedup for HYDRO2D, 1.8 times for SWIM, 1.7 times for SU2COR, 2.0 times for MGRID, 3.3 times for TURB3D on 8 processor IBM RS6000, against XL Fortran compiler ver 7.1 and 4.2 times speedup for SWIM and 2.2 times speedup for TURB3D on 4 processor Sun Ultra80 workstation against Forte6 update 2.
There has been much research on user activity assistance applications using the location of users and objects as context. However people's activities are described in terms of time sequence aspect in addition to location aspect. Therefore, it is important for enhanced user activity support systems to consider the user's context in terms of spatio-temporal constraints. In this paper, we propose a user activity assistance system that employs a state sequence description scheme to describe the user's contexts. In this scheme, each state is described as a spatio-temporal relationship between the user and objects in the real world. Typical sets of states are stored as models of tasks performed by a user. To try out this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Each state is detected by a decision tree constructed by a C4.5 algorithm using the output of the sensors and the RFID tags. The user's context is obtained by matching the detected state series to a task model. Having evaluated the performance of the proposed system in this experimental house, we conclude that our system is an effective way of acquiring the user's spatio-temporal context.
The rise in phishing attacks via e-mail and short message service (SMS) has not slowed down at all. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases that reach end users. Without understanding these characteristics, anti-phishing countermeasures cannot evolve. In this study, we propose an approach using Twitter as a new observation point to immediately collect and characterize phishing cases via e-mail and SMS that evade countermeasures and reach users. Specifically, we propose CrowdCanary, a system capable of structurally and accurately extracting phishing information (e.g., URLs and domains) from tweets about phishing by users who have actually discovered or encountered it. In our three months of live operation, CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports, 31,960 (90.2%) of these phishing URLs were later detected by the anti-virus engine. We analyzed users who shared phishing threats by categorizing them into two groups: experts and non-experts. As a results, we discovered that CrowdCanary extracts non-expert report-specific information, like company brand name in tweets, phishing attack details from tweet images, and pre-redirect landing page information.
