자연주의적 탐구조사(Naturalistic Inquiry)

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.In this article, we show using two simple attacks that a k-anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called e-diversity that can defend against such attacks. In addition to building a formal foundation for e-diversity, we show in an experimental evaluation that e-diversity is practical and can be implemented efficiently.

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L-diversity: Privacy beyond k-anonymity

Remote access to distributed hyperlinked information proves to be one of the killer applications for computer networks. More and more content in current inter and intranets is available as hyperdata, a form easing its distribution and semantic organization. In the framework of the Internet's Web-portals and pay-sites, mechanisms for login based on username and password enable the dynamic customization as well as partial protection of the content. In other applications (e.g. commercial intranets) various similar schemes of authentication are deployed. Nevertheless, stolen passwords are an easy avenue to identity theft, in both public and commercial data networks. Once a perpetrator enters a system, assuming an authorized user's identity, the task of actually detecting this intrusion becomes non-trivial and is often ignored completely. Thus, in addition to the initial authentication step we propose a runtime intrusion detection mechanism, required to maintain a virtually continuous user authentication process and detect identity theft and password misuses. The current paper focuses on designing a pervasive intrusion detection method for hyperdata systems, based on training on and analyzing of access patterns to hyperlinked data, aiming at detecting intruders and raising a red flag at the content provider's side. Our solution is based on a new technique, on-the-fly adaptive training for normality on streams of data access patterns. This enables runtime intrusion detection through analysis of correlations between current patterns and the adaptive past-knowledge. Such a method is to be used in conjunction with current username-password protection schemes. We introduce the motivation behind our solution, discuss the novel detection and training metrics and propose a real-life deployment design. We implement the main algorithm and perform experiments for assessing its intrusion detection ability, with very encouraging results. We also discuss the deployment of our method for detecting automatic spam-bot accesses.

On-the-fly intrusion detection for Web portals

There is a large and growing body of literature concerning the solutions of geometric problems on mesh-connected arrays of processors. Most of these algorithms are optimal (i.e., run in timeO(n1/d) on ad-dimensionaln-processor array), and they all assume that the parallel machine is trying to solve a problem of sizen on ann-processor array. Here we investigate the situation where we have a mesh of sizep and we are interested in using it to solve a problem of sizen >p. The goal we seek is to achieve, when solving a problem of sizen >p, the same speed up as when solving a problem of sizep. We show that for many geometric problems, the same speedup can be achieved when solving a problem of sizen >p as when solving a problem of sizep.

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On the parallel-decomposability of geometric problems

A Block-Based Mode Selection Model for SIMD/SPMD Parallel Environments

Watermarking is a frequently used tool for digital rights management. An example of this is using watermarks to place ownership information into an object. There are many instances where placing multiple watermarks into the same object is desired. One mechanism that has been proposed for doing this is segmenting the data into a grid and placing watermarks into different regions of the grid. This is particularly suited for images and geographic information systems (GIS) databases as they already consist of a fine granularity grid (of pixels, geographic regions, etc.); a grid cell for watermarking is an aggregation of the original fine granularity cells. An attacker may be interested in only a subset of the watermarked data, and it is crucial that the watermarks survive in the subset selected by the attacker. In the kind of data mentioned above (images, GIS, etc.) such an attack typically consists of cropping, e.g. selecting a geographic region between two latitudes and longitudes (in the GIS case) or a rectangular region of pixels (in an image). The contribution of this paper is a set of schemes and their analysis for multiple watermark placement that maximizes resilience to the above mentioned cropping attack. This involves the definition of various performance metrics and their use in evaluating and comparing various placement schemes.

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Cropping-Resilient Segmented Multiple Watermarking

We present a parallel algorithm for computing the visible portion of a simple polygonal chain with n vertices from a point in the plane The algorithm runs in O(log n) time using O(n/ log n) processors in the CREW-PRAM computational model, and hence is asymptomatically optimal

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Mikhail J. Atallah

Papers

On-the-fly intrusion detection for Web portals

On the parallel-decomposability of geometric problems

A Block-Based Mode Selection Model for SIMD/SPMD Parallel Environments

Cropping-Resilient Segmented Multiple Watermarking

Optimal parallel algorithm for visibility of a simple polygon from a point