Artificial Intelligence Driven Forensics
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Beschreibung
This book explores the transformative role of artificial intelligence (AI) in the field of digital forensics, offering a comprehensive overview of how AI technologies are reshaping crime investigation, cybersecurity, and evidence analysis. The book bridges traditional forensic methodologies with cutting-edge AI techniques, such as machine learning, deep learning, natural language processing, and graph analytics, to improve the accuracy, speed, and scalability of forensic investigations. Structured around real-world applications and case studies, the book covers critical areas including AI-enhanced malware detection, user behavior profiling, deep fake identification, social media profile cloning detection, canine forensics and hybrid approaches to analyzing digital artifacts. It also delves into the integration of AI with forensic tools for automating incident response, identifying anomalies in massive log files, and supporting decision-making in complex cybercrime scenarios. A key feature of the book is its interdisciplinary approach, blending insights from computer science, cybersecurity, law, and ethics. It addresses not only the technical mechanisms behind AI-driven forensics but also the legal, ethical, and societal implications, particularly regarding privacy, accountability, and the admissibility of AI-generated evidence in courts. Geared toward researchers, practitioners, students, and policymakers, Artificial Intelligence Driven Forensics serves as both a foundational text and an advanced guide to understanding the future of forensic science in the age of AI.
- Presents research presented at the AI-Enabled Forensic Investigations Network in Digital Sciences Conference;
- Includes innovation at the intersection of AI and digital forensics, focusing on inclusivity and workforce development;
- Brings together industry leaders to share advancements in forensics, cybersecurity, and investigative methodologies.
This book explores the transformative role of artificial intelligence (AI) in the field of digital forensics, offering a comprehensive overview of how AI technologies are reshaping crime investigation, cybersecurity, and evidence analysis. The book bridges traditional forensic methodologies with cutting-edge AI techniques, such as machine learning, deep learning, natural language processing, and graph analytics, to improve the accuracy, speed, and scalability of forensic investigations. Structured around real-world applications and case studies, the book covers critical areas including AI-enhanced malware detection, user behavior profiling, deep fake identification, social media profile cloning detection, canine forensics and hybrid approaches to analyzing digital artifacts. It also delves into the integration of AI with forensic tools for automating incident response, identifying anomalies in massive log files, and supporting decision-making in complex cybercrime scenarios. A key feature of the book is its interdisciplinary approach, blending insights from computer science, cybersecurity, law, and ethics. It addresses not only the technical mechanisms behind AI-driven forensics but also the legal, ethical, and societal implications, particularly regarding privacy, accountability, and the admissibility of AI-generated evidence in courts. Geared toward researchers, practitioners, students, and policymakers, Artificial Intelligence Driven Forensics serves as both a foundational text and an advanced guide to understanding the future of forensic science in the age of AI.
Presents research presented at the AI-Enabled Forensic Investigations Network in Digital Sciences Conference Includes innovation at the intersection of AI and digital forensics, focusing on inclusivity and workforce development Brings together industry leaders to share advancements in forensics, cybersecurity, and investigative methodologies
Autor*in
S. S. Iyengar
Themen in »Artificial Intelligence Driven Forensics«
AI applications in cybercrime investigation Digital forensics Canine-Assisted Forensic Investigations Machine learning for forensic data analysis Malware, ransomware, and cyber intrusions Blockchain and distributed ledger technologies Profile cloning detection and analysis Identifying and preventing digital fraud