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.

This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend’s saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author’s Github website.

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.

This book constitutes the refereed post-conference proceedings of the Second International Conference on Cyber Security and Computer Science, ICONCS 2020, held in Dhaka, Bangladesh, in February 2020. The 58 full papers were carefully reviewed and selected from 133 submissions. The papers detail new ideas, inventions, and application experiences to cyber security systems. They are organized in topical sections on optimization problems; image steganography and risk analysis on web applications; machine learning in disease diagnosis and monitoring; computer vision and image processing in health care; text and speech processing; machine learning in health care; blockchain applications; computer vision and image processing in health care; malware analysis; computer vision; future technology applications; computer networks; machine learning on imbalanced data; computer security; Bangla language processing.

The First International Conference on Digital Forensics and Cyber Crime (ICDF2C) was held in Albany from September 30 to October 2, 2009. The field of digital for- sics is growing rapidly with implications for several fields including law enforcement, network security, disaster recovery and accounting. This is a multidisciplinary area that requires expertise in several areas including, law, computer science, finance, networking, data mining, and criminal justice. This conference brought together pr- titioners and researchers from diverse fields providing opportunities for business and intellectual engagement among attendees. All the conference sessions were very well attended with vigorous discussions and strong audience interest. The conference featured an excellent program comprising high-quality paper pr- entations and invited speakers from all around the world. The first day featured a plenary session including George Philip, President of University at Albany, Harry Corbit, Suprintendent of New York State Police, and William Pelgrin, Director of New York State Office of Cyber Security and Critical Infrastructure Coordination. An outstanding keynote was provided by Miklos Vasarhelyi on continuous auditing. This was followed by two parallel sessions on accounting fraud /financial crime, and m- timedia and handheld forensics. The second day of the conference featured a mesm- izing keynote talk by Nitesh Dhanjani from Ernst and Young that focused on psyc- logical profiling based on open source intelligence from social network analysis. The third day of the conference featured both basic and advanced tutorials on open source forensics.

Machine learning approaches are widely used in natural language analysis. Previous research has shown that similar techniques can be applied in the analysis of computer programming (artificial) languages. In this thesis, we focus on identifying the authors of computer programs by using machine learning techniques. We extend these techniques to determine which features capture the writing style of authors in the classification of a computer program according to the author's identity. We then propose a novel approach for computer program author identification. In this method, program features from the text documents are combined with authors' sociological features (gender and region) to develop the classification model. Several experiments have been conducted on two datasets composed of computer programs written in C++, and the results are encouraging. According to the experimental results, the author's identity can be predicted with a $75\%$ accuracy rate.

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.

This book constitutes the refereed proceedings of the 9th International Conference on Future Data and Security Engineering, FDSE 2022, held in Ho Chi Minh City, Vietnam, during November 23–25, 2022. The 41 full papers(including 4 invited keynotes) and 12 short papers included in this book were carefully reviewed and selected from 170 submissions. They were organized in topical sections as follows: ​invited keynotes; big data analytics and distributed systems; security and privacy engineering; machine learning and artificial intelligence for security and privacy; smart city and industry 4.0 applications; data analytics and healthcare systems; and security and data engineering.

As computer and internet technologies continue to advance at a fast pace, the rate of cybercrimes is increasing. Crimes employing mobile devices, data embedding/mining systems, computers, network communications, or any malware impose a huge threat to data security, while cyberbullying, cyberstalking, child pornography, and trafficking crimes are made easier through the anonymity of the internet. New developments in digital forensics tools and an understanding of current criminal activities can greatly assist in minimizing attacks on individuals, organizations, and society as a whole. Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice addresses current challenges and issues emerging in cyber forensics and new investigative tools and methods that can be adopted and implemented to address these issues and counter security breaches within various organizations. It also examines a variety of topics such as advanced techniques for forensic developments in computer and communication-link environments and legal perspectives including procedures for cyber investigations, standards, and policies. Highlighting a range of topics such as cybercrime, threat detection, and forensic science, this publication is an ideal reference source for security analysts, law enforcement, lawmakers, government officials, IT professionals, researchers, practitioners, academicians, and students currently investigating the up-and-coming aspects surrounding network security, computer science, and security engineering.