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%

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.Comment: 14 pages, 5 figure

PFrauDetector: a parallelized graph mining approach for efficient fraudulent phone call detection

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly- efficient parallelized graph-miningbased fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a 'fraud' tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable subnetworks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods

Age and sex differences in the association between APOE genotype and Alzheimer’s disease in a Taiwan Chinese population

IntroductionThe Apolipoprotein E (APOE) epsilon (ε) 4 allele is a well-established risk factor for late-onset Alzheimer’s disease (AD). Reports on white ancestry populations have showed that age, sex, and ethnicity have different effects on the association between APOE genotype and AD. However, studies on Asian populations such as Taiwan Chinese populations are limited. This study aimed to evaluate the association between APOE genotype and AD in a Taiwan Chinese population, and to explore if the association varies by age and sex.MethodsWe conducted a case-control study in 725 patients with AD and 1,067 age- and sex- matched controls without dementia from a Taiwan Chinese population. Logistic regression models were used to test the association between AD and APOE genotypes. Secondary analyses considered age (<75 or ≥75 years old), and sex stratified models.ResultsThe risk of AD was significantly increased for people with at least one copy of APOE ε4 (OR = 2.52, 95% CI = 2.01–3.17, p < 0.001) and in a dose-dependent manner. Our results did not show an statistically significance different in AD risk when women and men carrying APOEε4 were compared. Despite not reaching statistical significance, the risk of APOE ε4 for AD was higher among younger participants (OR = 3.21, 95% CI = 2.26–4.56, p < 0.001) compared to older ones (OR = 2.13, 95% CI = 1.53–2.97, p < 0.001). When considering both sex and age, the risk of AD was higher among older men carrying APOE ε4 (OR = 2.64, 95% CI = 1.51–4.60 in men; OR = 1.90, 95% CI = 1.26–2.86 in women), while women carrying APOE ε4 appeared to have an increased risk at a younger age (OR = 3.29, 95% CI = 2.20–4.93 in women; OR = 2.91, 95% CI = 1.40–6.05 in men).DiscussionThe APOE ε4 allele represents a major risk factor for AD in the Taiwanese population. The effect of APOE ε4 allele on AD risk appeared to be stronger among men aged 75 years or more and among younger women