Kirjahaku

Cyberspace is a critical part of our lives. Although we all use cyberspace for work, entertainment, and social life, much of its infrastructure and operation is invisible to us. We spend a big part of our lives in an environment that is almost an essential service but is full of potential dangers: a place where criminals can commit new kinds of crimes, where governments can exert political pressure, and where we can be hurt by the unthinking actions of the bored and careless.Making cyberspace more secure is one of the challenges of our times. This is not only (or perhaps even primarily) a technical challenge. It requires actions by governments and businesses to encourage security whenever possible, and to make sure that their own actions do not undermine it. Unfortunately, many of those in a position to do something about cybersecurity do not have the background to understand the issues fully. Cybersecurity for Everyone will help by describing the issues in a way that is accessible to anyone, but especially those from non-technical backgrounds.

Cyberspace is a critical part of our lives. Although we all use cyberspace for work, entertainment, and social life, much of its infrastructure and operation is invisible to us. We spend a big part of our lives in an environment that is almost an essential service but is full of potential dangers: a place where criminals can commit new kinds of crimes, where governments can exert political pressure, and where we can be hurt by the unthinking actions of the bored and careless.Making cyberspace more secure is one of the challenges of our times. This is not only (or perhaps even primarily) a technical challenge. It requires actions by governments and businesses to encourage security whenever possible, and to make sure that their own actions do not undermine it. Unfortunately, many of those in a position to do something about cybersecurity do not have the background to understand the issues fully. Cybersecurity for Everyone will help by describing the issues in a way that is accessible to anyone, but especially those from non-technical backgrounds.

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets arelarge and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. The book will be of value to practitioners, graduate students and researchers.

Community detection in social networks is an important but challenging problem. This book develops a new technique for finding communities that uses both structural similarity and attribute similarity simultaneously, weighting them in a principled way. The results outperform existing techniques across a wide range of measures, and so advance the state of the art in community detection. Many existing community detection techniques base similarity on either the structural connections among social-network users, or on the overlap among the attributes of each user. Either way loses useful information. There have been some attempts to use both structure and attribute similarity but success has been limited. We first build a large real-world dataset by crawling Instagram, producing a large set of user profiles. We then compute the similarity between pairs of users based on four qualitatively different profile properties: similarity of language used in posts, similarity of hashtags used (which requires extraction of content from them), similarity of images displayed (which requires extraction of what each image is 'about'), and the explicit connections when one user follows another. These single modality similarities are converted into graphs. These graphs have a common node set (the users) but different sets a weighted edges. These graphs are then connected into a single larger graph by connecting the multiple nodes representing the same user by a clique, with edge weights derived from a lazy random walk view of the single graphs. This larger graph can then be embedded in a geometry using spectral techniques. In the embedding, distance corresponds to dissimilarity so geometric clustering techniques can be used to find communities. The resulting communities are evaluated using the entire range of current techniques, outperforming all of them. Topic modelling is also applied to clusters to show that they genuinely represent users with similar interests. This can form the basis for applications such as online marketing, or key influence selection.