Messaging Forensics In Perspective

This chapter presents the central theme and a big picture of the methods and technologies covered in this book (see Fig. 2.2). For the readers to comprehend presented security and forensics issues, and associated solutions, the content is organized as components of a forensics analysis framework. The framework is employed to analyze online messages by integrating machine learning algorithms, natural language processing techniques, and social networking analysis techniques in order to help cybercrime investigation

Cybersecurity And Cybercrime Investigation

Society\u27s increasing reliance on technology, fueled by a growing desire for increased connectivity (given the increased productivity, efficiency, and availability to name a few motivations) has helped give rise to the compounded growth of electronic data. The increasing adoption of various technologies has driven the need to protect said technologies as well as the massive amount of electronic data produced by them. Almost every type of new technology created today, from homes and cars to fridges, toys, and stoves, is designed as a smart device, generating data as an auxiliary function. These devices are all now part of the Internet of Things (IoT), which is comprised of devices that have embedded sensors, networking capabilities, and features that can generate significant amounts of data. Not only has society seen a dramatic rise in the use of IoT devices, but there has also been a marked evolution in the way that businesses use these technologies to deliver goods and services. These include banking, shopping, and procedure-driven processes. These enhanced approaches to delivering added value create avenues for misuse and increase the potential for criminal activities by utilizing the digital information generated for malicious purposes. This threat requires protecting this information from unauthorized access, as this data (ranging from sensitive personal data, demographic data, business data, to system data and context data) can be monetized by criminals

Dynamical regimes and hydrodynamic lift of viscous vesicles under shear

The dynamics of two-dimensional viscous vesicles in shear flow, with different fluid viscosities $\eta_{\rm in}$ and $\eta_{\rm out}$ inside and outside, respectively, is studied using mesoscale simulation techniques. Besides the well-known tank-treading and tumbling motions, an oscillatory swinging motion is observed in the simulations for large shear rate. The existence of this swinging motion requires the excitation of higher-order undulation modes (beyond elliptical deformations) in two dimensions. Keller-Skalak theory is extended to deformable two-dimensional vesicles, such that a dynamical phase diagram can be predicted for the reduced shear rate and the viscosity contrast $\eta_{\rm in}/\eta_{\rm out}$. The simulation results are found to be in good agreement with the theoretical predictions, when thermal fluctuations are incorporated in the theory. Moreover, the hydrodynamic lift force, acting on vesicles under shear close to a wall, is determined from simulations for various viscosity contrasts. For comparison, the lift force is calculated numerically in the absence of thermal fluctuations using the boundary-integral method for equal inside and outside viscosities. Both methods show that the dependence of the lift force on the distance $y_{\rm {cm}}$ of the vesicle center of mass from the wall is well described by an effective power law $y_{\rm {cm}}^{-2}$ for intermediate distances $0.8 R_{\rm p} \lesssim y_{\rm {cm}} \lesssim 3 R_{\rm p}$ with vesicle radius $R_{\rm p}$. The boundary-integral calculation indicates that the lift force decays asymptotically as $1/[y_{\rm {cm}}\ln(y_{\rm {cm}})]$ far from the wall.Comment: 13 pages, 13 figure

Automatic Fall Risk Detection based on Imbalanced Data

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion

ER-AE: Differentially-private Text Generation for Authorship Anonymization

Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers. However, personal writing style, as a strong indicator of the authorship, is often neglected. Recent studies on writing style anonymization can only output numeric vectors which are difficult for the recipients to interpret. We propose a novel text generation model with the exponential mechanism for authorship anonymization. By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially-private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style. It does not assume any conditioned labels or paralleled text data for training. We evaluate the performance of the proposed model on the real-life peer reviews dataset and the Yelp review dataset. The result suggests that our model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation