Machine Learning for Authorship Attribution and Cyber Forensics


Book Description

The book first explores the cybersecurity’s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.




Authorship Attribution


Book Description

Authorship Attribution surveys the history and present state of the discipline, presenting some comparative results where available. It also provides a theoretical and empirically-tested basis for further work. Many modern techniques are described and evaluated, along with some insights for application for novices and experts alike.




Social Network Analytics for Contemporary Business Organizations


Book Description

Social technology is quickly becoming a vital tool in our personal, educational, and professional lives. Its use must be further examined in order to determine the role of social media technology in organizational settings to promote business development and growth. Social Network Analytics for Contemporary Business Organizations is a critical scholarly resource that analyzes the application of social media in business applications. Featuring coverage on a broad range of topics, such as business management, dynamic networks, and online interaction, this book is geared towards professionals, researchers, academics, students, managers, and practitioners actively involved in the business industry.




Intelligent Systems Technologies and Applications


Book Description

This book constitutes the thoroughly refereed post-conference proceedings of the third International Symposium on Intelligent Systems Technologies and Applications (ISTA’17), September 13-16, 2017, Manipal, Karnataka, India. All submissions were evaluated on the basis of their significance, novelty, and technical quality. This proceedings contains 34 papers selected for presentation at the Symposium.




Cyber Security Cryptography and Machine Learning


Book Description

This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held in Beer-Sheva, Israel, in June 2017. The 17 full and 4 short papers presented include cyber security; secure software development methodologies, formal methods semantics and verification of secure systems; fault tolerance, reliability, availability of distributed secure systems; game-theoretic approaches to secure computing; automatic recovery of self-stabilizing and self-organizing systems; communication, authentication and identification security; cyber security for mobile and Internet of things; cyber security of corporations; security and privacy for cloud, edge and fog computing; cryptography; cryptographic implementation analysis and construction; secure multi-party computation; privacy-enhancing technologies and anonymity; post-quantum cryptography and security; machine learning and big data; anomaly detection and malware identification; business intelligence and security; digital forensics; digital rights management; trust management and reputation systems; information retrieval, risk analysis, DoS.




Applications of Data Mining in Computer Security


Book Description

Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.




Deep Learning for Security and Privacy Preservation in IoT


Book Description

This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.




Scalability Issues in Authorship Attribution


Book Description

Provides an in-depth and systematic study of the so-called scalability issues in authorship attribution -- the task that aims to identify the author of a text, given a model of authorial style based on texts of known authorship. Computational authorship attribution does not rely on in-depth reading, but rather automates the process. This book investigates the behavior of a text categorization approach to the task when confronted with scalability issues. By addressing the issues of experimental design, data size, and author set size, the dissertation demonstrates whether the approach taken is valid in experiments with limited or sufficient data, and with small or large sets of authors.




Computer Vision


Book Description

Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.




Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications


Book Description

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.