Search results for ""Author Mark Last""

As became apparent after the tragic events of September 11, 2001, terrorist groups are increasingly using the Internet as a communication and propaganda tool where they can safely communicate with their affiliates, coordinate action plans, raise funds, and introduce new supporters to their networks. This is evident from the large number of web sites run by different terrorist organizations, though the URLs and geographical locations of these web sites are frequently moved around the globe. The wide use of the Internet by terrorists makes some people think that the risk of a major cyber-attack against the communication infrastructure is low. However, this situation may change abruptly once the terrorists decide that the Net does not serve their purposes anymore and, like any other invention of our civilization, deserves destruction.Fighting Terror in Cyberspace is a unique volume, which provides, for the first time, a comprehensive overview of terrorist threats in cyberspace along with state-of-the-art tools and technologies that can deal with these threats in the present and in the future. The book covers several key topics in cyber warfare such as terrorist use of the Internet, the Cyber Jihad, data mining tools and techniques of terrorist detection on the web, analysis and detection of terror financing, and automated identification of terrorist web sites in multiple languages. The contributors include leading researchers on international terrorism, as well as distinguished experts in information security and cyber intelligence. This book represents a valuable source of information for academic researchers, law enforcement and intelligence experts, and industry consultants who are involved in detection, analysis, and prevention of terrorist activities on the Internet.

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This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance — a relatively new approach for determining graph similarity — the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms.To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters.In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling.

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