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Mobile security threats have recently emerged because of the fast growth in mobile technologies and the essential role that mobile devices play in our daily lives. For that, and to particularly address threats associated with malware, various techniques are developed in the literature, including ones that utilize static, dynamic, on-device, off-device, and hybrid approaches for identifying, classifying, and defend against mobile threats. Those techniques fail at times, and succeed at other times, while creating a trade-off of performance and operation. In this paper, we contribute to the mobile security defense posture by introducing Andro-AutoPsy, an anti-malware system based on similarity matching of malware-centric and malware creator-centric information. Using Andro-AutoPsy, we detect and classify malware samples into similar subgroups by exploiting the profiles extracted from integrated footprints, which are implicitly equivalent to distinct characteristics. The experimental results demonstrate that Andro-AutoPsy is scalable, performs precisely in detecting and classifying malware with low false positives and false negatives, and is capable of identifying zero-day mobile malware. [ABSTRACT FROM AUTHOR]

In this study, we propose the web forum analysis model that can support corporate foresight activities. The medical industry can utilize the rich, objective decision-making data contained within web forums through which participants who have common interests disseminate and receive information and form self-contained communities. We collect and analyze the contents of the web forum using Web, text, and data mining techniques. We identify the major needs of Alzheimer disease patients and their families. We also show how to track the time-series patterns of major topics providing insight to the medical industry. Furthermore, we study the dynamic mechanisms of major needs using the epidemic model and describe how users in a web forum collectively participate in topic discussions. Using the proposed model, the medical industry can predict the future market by estimating how long a topic will persist and how strongly a topic attracts attention. [ABSTRACT FROM AUTHOR]

Abstract: As online games become popular and the boundary between virtual and real economies blurs, cheating in games has proliferated in volume and method. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play which reflects the social activities among gamers: in a Massively Multi-user Online Role Playing Game (MMORPG), party play is a major activity that game bots exploit to keep their characters safe and facilitate the acquisition of cyber assets in a fashion very different from that of normal humans. Through a comprehensive statistical analysis of user behaviors in game activity logs, we establish threshold levels for the activities that allow us to identify game bots. Based on this, we also build a knowledge base of detection rules, which are generic. We apply our rule reasoner to AION, a popular online game serviced by NCsoft, Inc., a leading online game company based in Korea. [Copyright &y& Elsevier]

Abstract: A previous genome-wide association study (GWAS) failed to discover any nucleotide sequence variant associated with susceptibility to vascular dementia (VaD) and remained a problem of false negatives produced by a low statistical power. The current study was conducted to identify such potential false negatives and to provide comprehensive evidence for the most plausible predisposing genetic factor using large-scale Korean cohorts. We identified the gene encoding retinitis pigmentosa GTPase regulator-interacting protein 1-like (RPGRIP1L) with multiple nucleotide variants associated with susceptibility to VaD by a modest significant threshold (P<10−4). Genetic associations were intensively examined with its sequence variants using 207 VaD patients and 207 age- and gender-matched control subjects. Genetic association analysis with dense variants in the region associated with VaD revealed 3 variants (P<0.0017) in strong linkage. Further analysis with VaD-related phenotypes using Korean Association REsource (KARE) cohort data showed that the region of the gene was associated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), and blood pressure (BP) (P<7.6×10−4). The current study provided the first evidence of the association between RPGRIP1L gene and susceptibility of VaD. Functional studies are needed to understand underlying biological mechanism of the genetic association. [Copyright &y& Elsevier]

White blood cells (WBC) are vital elements of the immune system, and their number and differential count are crucial for diagnosing blood-related disorders. While existing research has primarily focused on classifying easily distinguishable major WBC types, our study delves into a model encompassing up to 19 WBC classes, some of which exhibit irregular shapes and are challenging to differentiate manually. Convolutional Neural Networks (CNNs) have shown remarkable progress in accurately classifying these intricate WBC classes. However, the accuracy of these models depends mainly on the availability of enough appropriate datasets, which can be challenging to obtain for rare WBC classes. To address this, we introduce a generative model, the diffusion-based Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This model innovatively combines the Denoising Diffusion Probabilistic Model (DDPM) forward diffusion process with the WGAN-GP, leveraging DDPM's noisy vectors as inputs for WGAN-GP's generator. This fusion accelerates the generative process and significantly enhances the output's fidelity, particularly for complex WBC images. Our model demonstrated its effectiveness on a dataset comprising 4,503 images across 19 WBC classes from Soonchunhyang University Bucheon Hospital, Korea, showing significant improvement in generating high-quality images for rare WBC classes and addressing data imbalance. We further combined pre-trained CNNs with Support Vector Machines (SVM) for classification, where our augmentation strategy led to the ResNet50-SVM model achieving an average accuracy of 95% in classifying the 19 WBC classes. This study not only addresses the data imbalance but also sets a new benchmark in WBC image analysis, demonstrating our model's efficacy in generating high-quality data for rare classes. [ABSTRACT FROM AUTHOR]

In indoor environments, such as smart homes, the number of occupants within the space and their moving directions can provide a rich set of contextual information about the surroundings and occupants themselves, which can enable systems to adapt their services according to the occupants' situation. Therefore, significant effort has been devoted to the development of variable sensing systems and learning methods. In this study, we introduce a pyroelectric infrared (PIR) sensor-based sensing system for counting moving people and detecting directions using convolutional neural networks (CNNs) and generative adversarial networks (GANs). PIR output signals were collected from four multiple-subject scenarios: single-, two-, three-, and four-subject groups in the experiments. We propose a novel time sequence sensor data augmentation algorithm, namely, auxiliary-classifier conditional GAN. This algorithm embeds the input data to reflect the condition to which the generated data should be transformed and its class information to which the generated data should be classified. The algorithm aims to build a model that works well in cases where multiple people move together (like to occur less than the cases when a single person moves alone). The experimental results show that when compared with the original model without augmentation, our multitask learning model combined with the proposed sample augmentation method increases the precision of counting moving people by 7.9%, 9.7%, 26%, and 37.5% for the one-, two-, three-, and four-subject groups, respectively, when compared with the original model without augmentation. [ABSTRACT FROM AUTHOR]