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Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.About This BookUse a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is ForIf you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.What You Will LearnApply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetIn DetailThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of

Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.About This BookUse a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is ForIf you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.What You Will LearnApply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetIn DetailThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of

Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.About This BookUse a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is ForIf you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.What You Will LearnApply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetIn DetailThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINTFocuses on algorithms and applications allowing the practitioner to get up and running quicklyIncludes fully developed case studies on the digital underground and predicting crime through OSINTDiscusses the ethical considerations when using publicly available online data

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINTFocuses on algorithms and applications allowing the practitioner to get up and running quicklyIncludes fully developed case studies on the digital underground and predicting crime through OSINTDiscusses the ethical considerations when using publicly available online data

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINTFocuses on algorithms and applications allowing the practitioner to get up and running quicklyIncludes fully developed case studies on the digital underground and predicting crime through OSINTDiscusses the ethical considerations when using publicly available online data

Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.About This BookUse a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is ForIf you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.What You Will LearnApply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetIn DetailThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINTFocuses on algorithms and applications allowing the practitioner to get up and running quicklyIncludes fully developed case studies on the digital underground and predicting crime through OSINTDiscusses the ethical considerations when using publicly available online data

Team Awesome AOC (Aircraft Operations Challenge) is developing a Sense-and-Avoid system that will take in ADS-B information (978Mhz UAT (Universal Access Transceiver)) and information from the autopilot to will predict if a conflict with another plane will occur, and autonomously change the flight plan in order to avoid the conflict. A conflict is defined as violating the minimum separation distances between aircraft. The team is working to develop a system which includes a small UAS platform, a Commercial off the shelf autopilot, a company sponsored ADS-B solution, and custom Sense-and-Avoid hardware and software. The system must be able to fly several 40 minute long scenarios with multiple possible conflicts while flying a pre-set flight plan. This solution is being developed to help students understand the problems limiting the use of UAS in the national airspace and for the UASAOC competition.

The Internet is a decentralized structure that offers speedy communication, has a global reach and provides anonymity, a characteristic invaluable for committing illegal activities. In parallel with the spread of the Internet, cybercrime has rapidly evolved from a relatively low volume crime to a common high volume crime. A typical example of such a crime is the spreading of spam emails, where the content of the email tries to entice the recipient to click a URL linking to a malicious Web site or downloading a malicious attachment. Analysts attempting to provide intelligence on spam activities quickly find that the volume of spam circulating daily is overwhelming; therefore, any intelligence gathered is representative of only a small sample, not of the global picture. While past studies have looked at automating some of these analyses using topic-based models, i.e. separating email clusters into groups with similar topics, our preliminary research investigates the usefulness of applying authorship-based models for this purpose. In the first phase, we clustered a set of spam emails using an authorship-based clustering algorithm. In the second phase, we analysed those clusters using a set of linguistic, structural and syntactic features. These analyses reveal that emails within each cluster were likely written by the same author, but that it is unlikely we have managed to group together all spam produced by each group. This problem of high purity with low recall, has been faced in past authorship research. While it is also a limitation of our research, the clusters themselves are still useful for the purposes of automating analysis, because they reduce the work needing to be performed. Our second phase revealed useful information on the group that can be utilized in future research for further analysis of such groups, for example, identifying further linkages behind spam campaigns.

Online Grooming is a growing phenomenon within online environments. One of the major problems encountered in qualitative internet research of chat communication is the issue of anonymity which is being exploited and greatly enjoyed by chatters. An importa

Voice over IP (VoIP) technologies such as Skype are becoming increasingly popular and widely used in different organisations, and therefore identifying the usage of this service at the network level becomes very important. Reasons for this include applying Quality of Service (QoS), network planning, prohibiting its use in some networks and lawful interception of communications. Researchers have addressed VoIP traffic classification from different viewpoints, such as classifier accuracy, building time, classification time and online classification. This previous research tested their models using the same version of a VoIP product they used for training the model, giving generalizability only to that version of the product. This means that as new VoIP versions are released, these classifiers become obsolete. In this paper, we address if this approach is applicable to detecting new, untrained, versions of Skype. We suggest that using cost-sensitive classifiers can help to improve the accuracy of detecting untrained versions, by testing compared to other algorithms. Our experiment demonstrates promising preliminary results to detect Skype version 4, by building a cost sensitive classifier on Skype version 3, achieving an F-measure score of 0.57. This is a drastic improvement from not using cost sensitivity, which scores an F-measure of 0. This approach may be enhanced to improve the detection results and extended to improve detection for other applications that change protocols from version to version.

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.

Purpose Ethnographic studies of cyber attacks typically aim to explain a particular profile of attackers in qualitative terms. The purpose of this paper is to formalise some of the approaches to build a Cyber Attacker Model Profile (CAMP) that can be used to characterise and predict cyber attacks. Design/methodology/approach The paper builds a model using social and economic independent or predictive variables from several eastern European countries and benchmarks indicators of cybercrime within the Australian financial services system. Findings The paper found a very strong link between perceived corruption and GDP in two distinct groups of countries – corruption in Russia was closely linked to the GDP of Belarus, Moldova and Russia, while corruption in Lithuania was linked to GDP in Estonia, Latvia, Lithuania and Ukraine. At the same time corruption in Russia and Ukraine were also closely linked. These results support previous research that indicates a strong link between been legitimate economy and the black economy in many countries of Eastern Europe and the Baltic states. The results of the regression analysis suggest that a highly skilled workforce which is mobile and working in an environment of high perceived corruption in the target countries is related to increases in cybercrime even within Australia. It is important to note that the data used for the dependent and independent variables were gathered over a seven year time period, which included large economic shocks such as the global financial crisis. Originality/value This is the first paper to use a modelling approach to directly show the relationship between various social, economic and demographic factors in the Baltic states and Eastern Europe, and the level of card skimming and card not present fraud in Australia.

In this paper, we propose a five-step approach to detect obfuscated malware by investigating the structural and behavioural features of API calls. We have developed a fully automated system to disassemble and extract API call features effectively from executables. Using n-gram statistical analysis of binary content, we are able to classify if an executable file is malicious or benign. Our experimental results with a dataset of 242 malwares and 72 benign files have shown a promising accuracy of 96.5% for the unigram model. We also provide a preliminary analysis by our approach using support vector machine (SVM) and by varying n-values from 1 to 5, we have analysed the performance that include accuracy, false positives and false negatives. By applying SVM, we propose to train the classifier and derive an optimum n-gram model for detecting both known and unknown malware efficiently.

Renewed interest in fission-fusion dynamics is due to the recognition that such dynamics may create unique challenges for social interaction and distinctive selective pressures acting on underlying communicative and cognitive abilities. New frameworks for integrating current knowledge on fission-fusion dynamics emerge from a fundamental rethinking of the term "fission-fusion" away from its current general use as a label for a particular modal type of social system (i.e., "fission-fusion societies"). Specifically, because the degree of spatial and temporal cohesion of group members varies both within and across taxa, any social system can be described in terms of the extent to which it expresses fission-fusion dynamics. This perspective has implications for socioecology, communication, cognitive demands, and human social evolution.

This manual is provided as a resource to help Oregon transportation professionals quantify the expected benefits of various access management applications. As a result, this manual includes recommendations for how to evaluate potential access management applications including expected performance metrics and recommended data collection information.