Generative Ai And Digital Forensics
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Generative AI and Digital Forensics
In today’s world, cybersecurity attacks and security breaches are becoming the norm. Unfortunately, we are not immune to it, and any individual and entity is at dire risk. The best and only thing that we can do is to mitigate the risks as much as much as possible so that they do not happen at all. But even when a security breach does indeed happen, the immediate reaction is to contain it so that it does not penetrate further into the information technology/network infrastructure. From there, mission-critical processes need to be restored, until the business can resume a normal state of operations, like it was before the security breach. But another key step here is to investigate how and why the security breach happened in the first place. The best way to do this is through what is known as “digital forensics”. This is where specially trained digital forensics investigators collect and comb through every piece of evidence to determine this. Eventually, the goal is then to use this evidence in a court of law so the cyberattacker can be made to answer for their crime and eventually be brought to justice. However, the area of digital forensics is a large one, and many topics around it can be covered. Also, generative AI is being used to not only help in the analysis of the evidence but also to help automate the digital forensics software packages that are available today. Therefore, in this book, the following is covered: Examples of security breaches and overview of digital forensics. How digital forensics can be used to investigate the loss or theft of data. An introduction to the SQL Server Database. A review of the SQL Injection Attack. How generative AI can be used in digital forensics.
Artificial Intelligence and Digital Forensics
Artificial Intelligence (AI) has revolutionized several sectors, with digital forensics being one that has been notably affected by its rapid advancement. AI brings previously unheard-of powers to this field, helping authorities examine large datasets, identify developments, and uncover digital evidence that is essential to cracking cybercrimes. Despite these promising developments, using AI in digital forensics presents problems. The complexity and dynamic nature of cyber-attacks are a significant challenge, demanding the ongoing adaptation of AI models to new attack strategies. This changing environment makes it difficult to create reliable and future-proof solutions. This book explores the advancements, applications, challenges, and solutions that AI brings to the realm of digital forensics. Artificial Intelligence and Digital Forensics: Advancements, Applications, Challenges, and Solutions includes the latest applications and examples with real data making the book meaningful for readers. It is written in very simple language with each technology having its dedicated chapter that explains how it works and provides an example of a real-world application. Key points and a summary are provided at the end of each chapter to enable the readers to quickly review the major concepts as it presents a practical understanding, so the readers can be better equipped to handle AI-based tools with ease and efficiency. The book provides unconditional support to those who are making decisions by using massive data from their organization and applying the findings to real-world current business scenarios addressing the challenges associated with integrating AI into digital forensics and offering practical solutions. It also offers insights into the ethical use of AI technologies and guidance on navigating legal requirements and ensuring responsible and compliant practices. This book caters to industry professionals from diverse backgrounds, including engineering, data science, computer science, law enforcement, and legal experts, fostering a holistic understanding of the subject along with providing practical insights and real-world examples.
Combating Cyberbullying With Generative AI
Cyberbullying is a pervasive global issue that threatens mental health, particularly among young people, with alarming rates of victimization. Emerging technologies like generative AI offer powerful tools to detect, prevent, and mitigate harmful online behavior, addressing this critical societal challenge. The use of machine learning and natural language processing enables early identification of offensive content, though ethical considerations and privacy concerns remain vital. Advancing AI-driven solutions to combat cyberbullying has the potential to create safer digital environments, fostering well-being and inclusivity online. These efforts not only bridge a significant research gap but also empower stakeholders to develop responsible, effective strategies against online abuse. Combating Cyberbullying with Generative AI emphasizes the creative ability of generative AI to generate innovative solutions that are tailored to the issues faced in cyberbully detection. It presents case studies and practical examples that demonstrate effective merging of generative AI in cyberbullying space, providing readers with applicable knowledge and motivation to implement these technologies in their situations. Covering topics such as creativity, intelligent systems, and social platforms, this book is an excellent resource for scholars, researchers, professionals, practitioners, and more.