Dark Web Pattern Recognition And Crime Analysis Using Machine Intelligence

Data stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome, regardless of whether countermeasures are taken. The Dark Web especially presents challenges to information privacy and security due to anonymous behaviors and the unavailability of data. To better understand and prevent cyberattacks, it is vital to have a forecast of cyberattacks, proper safety measures, and viable use of cyber-intelligence that empowers these activities. Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence discusses cyberattacks, security, and safety measures to protect data and presents the shortcomings faced by researchers and practitioners due to the unavailability of information about the Dark Web. Attacker techniques in these Dark Web environments are highlighted, along with intrusion detection practices and crawling of hidden content. Covering a range of topics such as malware and fog computing, this reference work is ideal for researchers, academicians, practitioners, industry professionals, computer scientists, scholars, instructors, and students.

The Dark Web is a known hub that hosts myriad illegal activities behind the veil of anonymity for its users. For years now, law enforcement has been struggling to track these illicit activities and put them to an end. However, the depth and anonymity of the Dark Web has made these efforts difficult, and as cyber criminals have more advanced technologies available to them, the struggle appears to only have the potential to worsen. Law enforcement and government organizations also have emerging technologies on their side, however. It is essential for these organizations to stay up to date on these emerging technologies, such as computational intelligence, in order to put a stop to the illicit activities and behaviors presented in the Dark Web. Using Computational Intelligence for the Dark Web and Illicit Behavior Detection presents the emerging technologies and applications of computational intelligence for the law enforcement of the Dark Web. It features analysis into cybercrime data, examples of the application of computational intelligence in the Dark Web, and provides future opportunities for growth in this field. Covering topics such as cyber threat detection, crime prediction, and keyword extraction, this premier reference source is an essential resource for government organizations, law enforcement agencies, non-profit organizations, politicians, computer scientists, researchers, students, and academicians.

AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today’s society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.

ROBOTIC PROCESS AUTOMATION Presenting the latest technologies and practices in this ever-changing field, this groundbreaking new volume covers the theoretical challenges and practical solutions for using robotics across a variety of industries, encompassing many disciplines, including mathematics, computer science, electrical engineering, information technology, mechatronics, electronics, bioengineering, and command and software engineering. Robotics is the study of creating devices that can take the place of people and mimic their behaviors. Mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, mathematics, and other subjects are all included in robotics. Robots can be employed in a variety of scenarios and for a variety of objectives, but many are now being used in hazardous areas (such as radioactive material inspection, bomb detection, and deactivation), manufacturing operations, or in conditions where humans are unable to live (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). Walking, lifting, speaking, cognition, and any other human activity are all attempted by robots. Many of today’s robots are influenced by nature, making bio-inspired robotics a growing area. Defusing explosives, seeking survivors in unstable ruins, and investigating mines and shipwrecks are just a few of the activities that robots are designed to undertake. This groundbreaking new volume presents a Robotic Process Automation (RPA) software technique that makes it simple to create, deploy, and manage software robots that mimic human movements while dealing with digital systems and software. Software robots can interpret what’s on a screen, type the correct keystrokes, traverse systems, locate and extract data, and do a wide variety of predetermined operations, much like people. Software robots can do it quicker and more reliably than humans, without having to stand up and stretch or take a coffee break.

It is crucial that forensic science meets challenges such as identifying hidden patterns in data, validating results for accuracy, and understanding varying criminal activities in order to be authoritative so as to hold up justice and public safety. Artificial intelligence, with its potential subsets of machine learning and deep learning, has the potential to transform the domain of forensic science by handling diverse data, recognizing patterns, and analyzing, interpreting, and presenting results. Machine Learning and deep learning frameworks, with developed mathematical and computational tools, facilitate the investigators to provide reliable results. Further study on the potential uses of these technologies is required to better understand their benefits. Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks provides an outline of deep learning and machine learning frameworks and methods for use in forensic science to produce accurate and reliable results to aid investigation processes. The book also considers the challenges, developments, advancements, and emerging approaches of deep learning and machine learning. Covering key topics such as biometrics, augmented reality, and fraud investigation, this reference work is crucial for forensic scientists, law enforcement, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.

In recent years, industries have shifted into the digital domain, as businesses and organizations have used various forms of technology to aid information storage and efficient production methods. Because of these advances, the risk of cybercrime and data security breaches has skyrocketed. Fortunately, cyber security and data privacy research are thriving; however, industry experts must keep themselves updated in this field. Exploring Cyber Criminals and Data Privacy Measures collects cutting-edge research on information security, cybercriminals, and data privacy. It proposes unique strategies for safeguarding and preserving digital information using realistic examples and case studies. Covering key topics such as crime detection, surveillance technologies, and organizational privacy, this major reference work is ideal for cybersecurity professionals, researchers, developers, practitioners, programmers, computer scientists, academicians, security analysts, educators, and students.

Cybersecurity has emerged to address the need for connectivity and seamless integration with other devices and vulnerability assessment to find loopholes. However, there are potential challenges ahead in meeting the growing need for cybersecurity. This includes design and implementation challenges, application connectivity, data gathering, cyber-attacks, and cyberspace analysis. Perspectives on Ethical Hacking and Penetration Testing familiarizes readers with in-depth and professional hacking and vulnerability scanning subjects. The book discusses each of the processes and tools systematically and logically so that the reader can see how the data from each tool may be fully exploited in the penetration test’s succeeding stages. This procedure enables readers to observe how the research instruments and phases interact. This book provides a high level of understanding of the emerging technologies in penetration testing, cyber-attacks, and ethical hacking and offers the potential of acquiring and processing a tremendous amount of data from the physical world. Covering topics such as cybercrimes, digital forensics, and wireless hacking, this premier reference source is an excellent resource for cybersecurity professionals, IT managers, students and educators of higher education, librarians, researchers, and academicians.

The constant threat of terror leads to the destabilization of the political, economic, and social situation in the state. Lack of confidence in personal safety contributes to the growth of anxiety, fears, and mental stress, which negatively affects psychological health, leading to the development of various psychosomatic disorders among the population. Global Perspectives on the Psychology of Terrorism discusses the psychological aspects of terrorism, including the determination of the main types of terrorism and the psychological characteristics of terrorists and terrorist groups. It further speaks on the negative impact of terrorism on the mass consciousness, as well as the ways to deal with stress in people exposed to the impact of terrorist attacks, features of human behavior in extreme situations, and methods of psychological support in times of crisis. Covering topics such as state terrorism, international security, and cyberterrorism, this premier reference source is an excellent resource for government officials, sociologists, representatives of mass media, non-governmental organizations, politicians, psychologists, students and faculty of higher education, librarians, researchers, and academicians.