VSO: Self-organizing Spatial Publish Subscribe

Abstract-Spatial publish subscribe (SPS) is a basic primitive underlying many real-time, interactive applications such as online games or discrete-time simulations. Supporting SPS on a large-scale, however, requires sufficient resources and proper load distribution among the simulation units. For load distribution, existing mechanisms either use a static partitioning, such that over-provisioning or overloading are bound to occur, or require manual adjustments unsuitable for massive workloads. We describe Voronoi Self-organizing Overlay (VSO) [1], which extends a Voronoi-based Overlay network (VON) to automatically partition and manage a logical space to support SPS. Efficient resource usage thus is possible as only the units necessary to maintain the system are used. Load is also balanced among the resource units so that overloading or overprovisioning can be avoided. We use simulations to verify our design and describe some preliminary results

Counteracting Phishing Page Polymorphism: An Image Layout Analysis Approach

Abstract. Many visual similarity-based phishing page detectors have been developed to detect phishing webpages, however, scammers now cre-ate polymorphic phishing pages to breach the defense of those detectors. We call this kind of countermeasure phishing page polymorphism. Poly-morphic pages are visually similar to genuine pages they try to mimic, but they use different representation techniques. It increases the level of difficulty to detect phishing pages. In this paper, we propose an effective detection mechanism to detect polymorphic phishing pages. In contrast to existing approaches, we analyze the layout of webpages rather than the HTML codes, colors, or content. Specifically, we compute the sim-ilarity degree of a suspect page and an authentic page through image processing techniques. Then, the degrees of similarity are ranked by a classifier trained to detect phishing pages. To verify the efficacy of our phishing detection mechanism, we collected 6, 750 phishing pages and 312 mimicked targets for the performance evaluation. The results show that our method achieves an excellent detection rate of 99.6%.

Fast-Flux Bot Detection in Real Time

Abstract. The fast-flux service network architecture has been widely adopted by bot herders to increase the productivity and extend the lifes-pan of botnets ’ domain names. A fast-flux botnet is unique in that each of its domain names is normally mapped to different sets of IP addresses over time and legitimate users ’ requests are handled by machines other than those contacted by users directly. Most existing methods for de-tecting fast-flux botnets rely on the former property. This approach is effective, but it requires a certain period of time, maybe a few days, before a conclusion can be drawn. In this paper, we propose a novel way to detect whether a web service is hosted by a fast-flux botnet in real time. The scheme is unique because it relies on certain intrinsic and invariant characteristics of fast-flux bot-nets, namely, 1) the request delegation model, 2) bots are not dedicated to malicious services, and 3) the hardware used by bots is normally infe-rior to that of dedicated servers. Our empirical evaluation results show that, using a passive measurement approach, the proposed scheme can detect fast-flux bots in a few seconds with more than 96 % accuracy, while the false positive/negative rates are both lower than 5%

Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

Background: To examine whether the modulating evoked cortical oscillations could be brain signatures among patients with chronic migraine, we investigated cortical modulation using an electroencephalogram with machine learning techniques. Methods: We directly record evoked electroencephalogram activity during nonpainful, painful, and repetitive painful electrical stimulation tasks. Cortical modulation for experimental pain and habituation processing was analyzed and used to differentiate patients with chronic migraine from healthy controls using a validated machine-learning model. Results: This study included 80 participants: 40 healthy controls and 40 patients with chronic migraine. Evoked somatosensory oscillations were dominant in the alpha band. Longer latency (nonpainful and repetitive painful) and augmented power (nonpainful and repetitive painful) were present among patients with chronic migraine. However, for painful tasks, alpha increases were observed among healthy controls. The oscillatory activity ratios between repetitive painful and painful tasks represented the frequency modulation and power habituation among healthy controls, respectively, but not among patients with chronic migraine. The classification models with oscillatory features exhibited high performance in differentiating patients with chronic migraine from healthy controls. Conclusion: Altered oscillatory characteristics of sensory processing and cortical modulation reflected the neuropathology of patients with chronic migraine. These characteristics can be reliably used to identify patients with chronic migraine using a machine-learning approach

Understanding the performance of thin-client gaming

Abstract—The thin-client model is considered a perfect fit for online gaming. As modern games normally require tremendous computing and rendering power at the game client, deploying games with such models can transfer the burden of hardware upgrades from players to game operators. As a result, there are a variety of solutions proposed for thin-client gaming today. However, little is known about the performance of such thin-client systems in different scenarios, and there is no systematic means yet to conduct such analysis. In this paper, we propose a methodology for quantifying the performance of thin-clients on gaming, even for thin-clients which are close-sourced. Taking a classic game, Ms. Pac-Man, and three popular thin-clients, LogMeIn, TeamViewer, and UltraVNC, as examples, we perform a demonstration study and determine that 1) display frame rate and frame distortion are both critical to gaming; and 2) different thin-client implementations may have very different levels of robustness against network impairments. Generally, LogMeIn performs best when network conditions are reasonably good, while TeamViewer and UltraVNC are the better choices under certain network conditions. I

What Can the Temporal Social Behavior Tell Us? An Estimation of Vertex-Betweenness Using Dynamic Social Information

Abstract—The vertex-betweenness centrality index is an es-sential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V ||E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V |) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks. Index Terms—Betweenness, MMORPG, Text-Conversation I

Pain sensitivities predict prophylactic treatment outcomes of flunarizine in chronic migraine patients: A prospective study

Abstract Background We aimed to assess the differences in quantitative sensory testing between chronic migraine and healthy controls and to explore the association between pain sensitivities and outcomes in chronic migraine following preventive treatment. Methods In this prospective open-label study, preventive-naïve chronic migraine and healthy controls were recruited, and cold, heat, mechanical punctate, and pressure pain thresholds over the dermatomes of first branch of trigeminal nerve and first thoracic nerve were measured by quantitative sensory testing at baseline. Chronic migraines were treated with flunarizine and treatment response was defined as ≥50% reduction in the number of monthly headache days over the 12-week treatment period. Results Eighty-four chronic migraines and fifty age-and-sex-matched healthy controls were included in the analysis. The chronic migraine had higher cold pain thresholds over the dermatomes of the first branch of trigeminal nerve and the first thoracic nerve (p  158 g (p = 0.020) or heat pain threshold over the dermatome of the first branch of the trigeminal nerve > 44.9°C (p = 0.002) were more likely to be responders. Conclusions Chronic migraine were generally more sensitive compared to healthy controls. Preventive treatment with flunarizine should be recommended particularly for chronic migraine who have relatively normal sensitivity to mechanical punctate or heat pain. Trial registration: This study was registered on ClinicalTrials.gov (Identifier: NCT02747940)