[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Trust: The Social Virtues and the Creation of Prosperity

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Heterogeneous networking (HetNet) is a promising solution to meet the ever increasing demands of wireless applications. However, it is a significant challenge for mobile network operators to deploy HetNet at a large scale. To address this issue, this article discusses a new paradigm in which potential partners are encouraged to participate in facilitating HetNet deployment. This idea is inspired by the emerging phenomenon of crowdsourcing; hence, for HetNet with the feature of crowdsourcing, we have coined the term CHetNet. In this article, we discuss the essential elements of CHetNet, and propose a basic working framework. Four possible application scenarios are discussed to demonstrate how MNOs and their partners collaborate with each other to deliver wireless services. A case study is provided to illustrate that a win-win situation can be achieved by the proposed CHetNet.

CHetNet: crowdsourcing to heterogeneous cellular networks

Defocus blur has been used as a cue in image splicing detection. At present, existing methods mainly rely on consistency checking of defocus kernels estimated along suspicious edges (and other reference edges if applicable). However, the texture, nearby edges, light fields as well as noises will influence the information of defocus blur at the natural edges in a certain range, resulting in inconsistent edge defocus blur estimation. As a result, it makes the splicing detection unreliable. In this paper, we analyze the feature of the defocus blur on both the spliced edges and the natural edges, and propose a novel difference-of-defocus-blur based natural image splicing detection method. Compared to the state-of-the-art methods, the proposed method can detect splicing more robustly.

Natural image splicing detection based on defocus blur at edges

Many cryptography techniques have been used to protect the privacy of vehicular ad hoc networks(VANETs). For example, the ring signature, one of the anonymous techniques by virtue of its unconditional anonymity, is widely used in VANETs for privacy protection. However, some of their cryptographic properties may have been weakened due to the characteristic of VANETs. In this paper, we study the impact of the influences of ring selection on the anonymity property of ring signature when applied to privacy protection in VANETs. Specifically, we introduce a novel ring selection attack aimed to compromise the anonymity of vehicles in VANETs. We then propose a ring selection algorithm based on the analysis of the ring selection attack. Furthermore, by taking the information entropy as the privacy metric, we develop a privacy analytic model to quantitatively investigate the privacy level achieved in the solution. Finally, the analytical results indicate that better privacy protection can be achieved when selecting ring members according to our ring selection algorithm.

Ring Selection for Ring Signature-Based Privacy Protection in VANETs

Malicious softwares (Malware) are able to quickly evolve into many different variants and evade existing detection mechanisms, rendering the ineffectiveness of traditional signature-based malware detection systems. Many researchers have proposed advanced malware detection techniques by using Machine Learning. Although the machine learning based techniques perform well in detecting a wide range of malware variants, there still remain some problems when meeting the real scene in the industry. Since the volume of new malware variants grows fast and labelling data is expensive and takes a lot of labor, companies cannot label every one of those samples. They tend to label a small part of the malware samples and treat the rest of the unlabeled samples as benign samples in which the original malware samples are treated as mislabeled. This causes a bias of decision boundary which severely limits the accuracy. To address such a problem, in this paper, we propose a cost-sensitive boosting method to train an unbiased detection model with the malicious-unlabeled executables to improve the accuracy. Along with that, in order to detect malware variants efficiently, we propose a byte co-occurrence matrix as a representation of byte streams of executables to detect malware variants directly. Experimental results show that the machine learning methods optimized by our approach can achieve 80% to 90% accuracy while the original machine learning methods can only achieve 50% to 85% accuracy when the unlabeled data contain different rates of mislabeled positive data.

An Optimized Positive-Unlabeled Learning Method for Detecting a Large Scale of Malware Variants

Mobile crowdsensing enables individuals to collect data from social events and phenomena for performing tasks released by customers using their mobile devices. It removes the necessity of sensors deployment and hence supports large-scale sensing applications efficiently. Nevertheless, incentive and privacy remain as the major obstacles that need additional attention. If privacy is not presered or no benefit obtains, no mobile user prefers to participate in crowdsensing activities. In this paper, we propose DARD, a dual-anonymous reward distribution scheme to achieve the incentive for mobile users and privacy protection for both customers and mobile users in mobile crowdsensing. Specifically, we design a reward sharing incentive mechanism to encourage mobile users to participate in tasks and employ randomizable techniques to protect the identities of customers and mobile users during reward claim, distribution and deposit. Our analysis further shows that DARD achieves reward balance and cheater detection with low computational and communication overhead.

Xiaodong Lin

Papers

CHetNet: crowdsourcing to heterogeneous cellular networks

Natural image splicing detection based on defocus blur at edges

Ring Selection for Ring Signature-Based Privacy Protection in VANETs

An Optimized Positive-Unlabeled Learning Method for Detecting a Large Scale of Malware Variants

Dual-anonymous reward distribution for mobile crowdsensing