https://www.digitalguardian.com/resources/knowledge-base/data-forensics
Data forensics, also know as computer forensics, refers to the study or investigation of digital data and how it is created and used. Data forensics is a broad term, as data forensics encompasses identifying, preserving, recovering, analyzing, and presenting attributes of digital information. In regards to data recovery, data forensics can be conducted on mobile devices, computers, servers ...
https://dev.rwu.edu/graduate/programs/graduate-programs/digital-forensics-graduate-certificate
The study of digital forensics is a growing field for both law enforcement as well as corporate employees. Within this three-course certificate, students will understand NTFS and FAT Operating Systems, be able to develop sound evidence for presentation in court, and be able to manage evidence safely and acceptably.The Digital Forensics certificate program is taught fully online and can be ...
https://www.udemy.com/course/computer-forensics-and-incident-response/
Description. Unlock the secrets of computer forensics and digital investigations with this advanced, hands-on course designed to empower you with the skills needed to excel in the Digital Forensics and Incident Response (DFIR) field. Whether you're aiming to master on-scene investigations, digital evidence acquisition, memory analysis, or dive ...
https://ieeexplore.ieee.org/document/10745632
The proliferation of the Internet of Things (IoT) systems has fueled a surge in cybercrime, particularly through advanced persistent threats, such as botnets and ransomware, posing challenges for centralized Digital Forensics (DF) solutions in tracking decentralized attacks and ensuring data privacy. Despite these challenges, existing research has primarily focused on traditional DF methods ...
https://www.forensicfocus.com/articles/understanding-digital-forensics-mental-health-stressors-introduction/
This piece explores the mental health effects faced by digital forensic investigators (DFIs). One study carried out by Wilson-Kovacs et al. in 2021 suggested that 80% of work undertaken by digital forensic investigators (DFIs) consists of child sexual abuse material. Digital forensic investigators (DFIs) work in an environment that demands both ...
https://www.linkedin.com/learning/web-forensics-recovering-digital-evidence
It’s more important than ever for digital forensics professionals to acquire expertise to solve cybercrimes occurring on the web. This course provides the theory and practice of web forensics ...
https://westoahu.hawaii.edu/cyber/forensics-weekly-executive-summmaries/importance-of-digital-forensics-process-models-some-examples/
Proposed digital forensic readiness models take a proactive stance, with a focus on enabling organizations to gather digital evidence in a cost effective manner to produce actionable threat intelligence that can be utilized by security personnel [2, 4]. Bankole et al. and Englbrecht et al. propose maturity models; models of this kind focus on ...
https://www.coursera.org/learn/ibm-incident-response-digital-forensics
There are 3 modules in this course. This IBM course will teach you the critical skills needed to manage and investigate cybersecurity incidents. You will learn about key topics, including incident response frameworks (NIST and SANS), digital forensics methodologies, and best practices for handling digital evidence.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11157519/
Digital forensic methods, which involve collecting and analyzing digital evidence, have been adapted to tackle the challenge of deepfakes (Khan et al., 2022). These methods range from pixel-level analysis to more complex techniques that involve analyzing the physical properties of digital content.
Feedbackdata:image/jpeg;base64,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
data:image/jpeg;base64,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
data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDABwTFRgVERwYFhgfHRwhKUUtKSYmKVQ8QDJFZFhpZ2JYYF9ufJ6GbnWWd19giruLlqOpsbOxa4TC0MGszp6usar/2wBDAR0fHykkKVEtLVGqcmByqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqr/wAARCABuAMUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDK0nrN/uVa3ZHWqmlnAn/3Kk3100Xoc1ZXZozzW86mV3lExXG0Abc4xnPpUjXkL3NwWLiOVAuQORjHasrfRvq7IjUuSzq0MCLnKKQc/Umot9Qb6N9UnYlxuT76N9Qb6N9O4uQn30b6g30b6LhyE++jfUG+jfRcOQn30b6g30b6LhyE++jfUG+jfRcOQn30b6g30b6LhyE++jfUG+jfRcOQn30b6g30b6LhyExfg81va7/yBrj6D+dc0X4NdLrv/IGuPoP51z1nsdFFWucRRRRWBuaOkHDyn0Wr/nj0H5VT0FFkujG4yrYBHtmum/sex/55H/vtv8a1hNRRnKHMzNP2fPFzHjOOV5oAhZwq3EZycD5a0v7Hsf8Ankf++2/xo/sex/54n/vtv8aftBezMYTIZApIC5wWx2qQNCf+WyL04xn9a1f7Hsf+eR/77b/Gj+x7H/nkf++2/wAaftEL2Zlu0CuQsysNuc4xznp+VL+43Y+0x4x121p/2PY/88j/AN9t/jR/Y9j/AM8j/wB9t/jS9og9mY/nJ5gUldu7BbHbPWpFMJAzcRqfTbnFan9j2P8AzyP/AH23+NH9j2P/ADyP/fbf40/aIPZmcFgdwqXMeT6rUEcqNIoZlRT1bHStj+x7H/nif++2/wAaP7Hsf+eR/wC+2/xo9og9mZeYMZ+0x/TYaX9weFuIyc4+71rT/sex/wCeR/77b/Gj+x7H/nkf++2/xpe0D2ZkSSKkpTKkA43AVIfs+44uY8e61p/2PY/88T/323+NH9j2P/PI/wDfbf40/aIPZmZ+43Y+0x4zjOw1FJLGpAV1fIySB09q2P7Hsf8Ankf++2/xo/sex/55H/vtv8aPaIPZmWrQGNSZ0VsDIK5waMw/8/MfXH3D+dan9j2P/PI/99t/jR/Y9j/zyP8A323+NL2gezMvdB5mPPQrtJzt79hQjW5jBaYBsD5dvetT+x7H/nkf++2/xo/sex/55H/vtv8AGj2g/ZmTI8QRyJUYjoNvXmtPXf8AkDXH0H86f/Y1if8Alif++2/xpmu/8ga4+g/nUTlzFRjynEUUtFQWavh3/j/H1X+ddkSAMk4ArjfDv/H+Pqv866q//wCPOT8P51SV7ImTsmw+3W3/AD0/8dNH262/56f+OmssRoQP3oHTOffrS+VHkjzhx3/CtvZROP6xU7I0/t1t/wA9P/HTR9utv+en/jprLMacYlU5OPpSmFAM+ep47UeygH1ip2Rp/brb/np/46aPt1t/z0/8dNZDAAgA54FRQkSZ3ShPTPem6UUONepLZI3Pt1t/z0/8dNH262/56f8AjprGCqTzMAN2Ooz/ADpAEP8Ay2xjr09/8P1qeSBpz1eyNxLuB2CrJyemQRU1c5G3+lbA24BuD6810MpZYmKfeA4qJxUdjSlOUrqQ6lrON1OOD8px3HSoLnULiNGKMvAHJX3rNam/KzYorCj1O6bOXX/vmr8NzK8SszDJ9qbVhWZepKgWRiOTUd3PJFavIh+YDjjNIOVlyisFNR1BzhVz/wBs6kkutTi2Fyq78hQUHYZp2Cxs0VmQXFyyKzzDGwM3yjg46frUVxqDIzbbzYeytCD/AFpXQcrNmsbU0X+zLk7RkJ/UVNYXN1Ikn2h0LK2AUAwRiotT/wCQXdf9c/6igGrHH0UUUCNXw7/x/j6r/Ouqv/8Ajyk/D+dcr4d/4/x9V/nXVX//AB5Sfh/OrjuiJ/CzLW2kdQVIOaT7NJvK5GR15qMMRyCRQMnpk/SuzXueZePYcYmAzkdcH88UGI7Sc9DjnimZozT1JuiQQMT1FRtbqoBKoQfQUZozRYObsM8pP7i/lR5Sf3F/Kn0U7IOZ9wiRVkUqoHzDoPet+cgQvnpisFP9Yv8AvD+dbl2xW1lYDJCnFc1fdHbhHvcpMVP3WPIIOarXUWbd9g3NwAB35qmZL5vmEpA9hir9sHYAuWYbeeRjPeuZ+7qelYpRRSAHMbD6itK14hAPBqtLO8TBSFJ2/NkdDSLdO3IiUjp6Vd3JXJSsacdOz+9jHqf6VnLcFgCEI/GlW6IIzkc4zmpG0bFU76QBlQYz1qP7Qx+7Nn6Gql7dPG8bE7mOQMilLYmKs7shm815/nYqucBe1VpIFSRj94+lTvMpX94VZe2DzVWOYSXDbeB0rLUtmvp/Eb+7Z/QUmp/8gu6/65/1FN09/wDR2HG/r/Snan/yCrr/AK5/1FXEzmcfRRRVkGr4d/4/x9V/nXVah/x5Sfh/OuV8O/8AH+Pqv866nUf+PCX8P51cd0RP4WUYUmkjXZHEw/2m54x/gKRGcOdr22XAGBKB0P8A9eiFo0t0EiTEnn5ADzxz/KrLX0LKR9nl59EFW3K+hilTsrsqnT7onOxf++qP7Puf7i/991NHNbpKJFhnBUE4IAA45qT+1oP7kn5D/Gq5qhLp0Sr/AGdc/wBxf++qP7Ouv7i/99Vb/taD+5J+Q/xo/taD+5J+Q/xo5qouSh3Kn9nXP9xf++qP7Ouf7i/99Vb/ALWg/uSfkP8AGj+1oP7kn5D/ABo5qoclDuUntJoNryBQN6gYOc81rXYzayj/AGazru+juUjRFcESKea0rj/UP9Kibk7cxrSUU3y7GGqOhbG0qeSDT1csN0SgemD+dTSovkyZUE7TgHpmo7ErFui28Z3A46Z7VLsdClKxHtk6FUz6kZNLiQNy43H0HpVyRVJG3NVLw+UFkzgDPHqcUaBzNjXeZfuksaYrTuw81cKOlSR7pgjJ2xuyPWp/Jb0o0E5O1iv5YPOahusN9n8xsKC3P4CrDwyMQQ7bCO3aqOpOEihzyBux79KmUr6FRjy6la6lVAUQgjPX1qOCVUydyl26AA8VVdi7Fj3qewgNxeRRg43NyfQVFtB3uzftAEgL5B34XPp7Y/GpdT/5BV1/1z/qKUHyFZUTKgk88k0mp/8AILuv+uf9RREJo4+iiiqINXw7/wAf4+q/zrqdR/48Jfw/nXLeHf8Aj/H1X+ddRqf/ACD5fw/mKuO6In8LM+3VGQFr4RuQAUPOPQfyp4ERAxqY+YZ6den/AMUPzqGDUpIIVjWOMhe5zmn/ANry/wDPKL8jWrjO5gpUrFlbB5EDLeFlZeCF4INN/sf/AKb/APjn/wBeof7Ym/55xfrR/bE//POP9f8AGly1EDlRe5N/Y/8A03/8c/8Ar0f2P/03/wDHP/r1D/bE/wDzzj/Wj+2J/wDnnH+tO1UL0exN/Y//AE3/APHP/r0f2P8A9N//ABz/AOvUP9sT/wDPOP8AWj+2J/8AnnH+v+NFqoXo9hbmwFqiP5hcmRRjGK0p5UCumctjoBmsibUJLry0dUAEinjPrWpCVS38x2CjG5mY/qTXPiJyglfc0o8rb5dilNloWVUck5H3arbW5jFmyjZw+O9bSsrZ2kHBwcdjS1y+3n2OlaFLICLkMTwDwahuk84qNrEZyflrTxRS9vIFoZSqySAqrgFst8uc1MXJzg9OPpV/8Kq3gCsjdCTg1cKz5kmKWpnySMR5fTD9j1+tYV9cefNwfkThf8avXc+03TKRkvtH4isnHNblSeiSADJwK3dFhSOB5nyG3kH2CjP9aybZ/KnSQYyh3DPrWjZX8duCJcbGfd0yeRg1Qoo0vLSWcySIW4yAe54xTtT/AOQXdf8AXP8AqKzYtVZSf9HReg6kd60tT/5Bd1/1z/qKlCkcfRRRTJNXw7/x/j6r/Ouo1T/kHS/QfzFcv4d/4/x9V/nXUap/yDpfoP5irjuiZbMzrO2jktFlaIv8zbgM5IA46VLFbQO6K1jKu8kZJI2jGef5VnQvMWWNJnQH0Y4FT7LvtcvyePnPrWrhK+5zxmkvhLYtYN0amxl+dsZDHCj1NPgsbeRir2rIQAc7mx+tUSl2MD7S2Tj+Nu9M/wBK+f8A0h/kOD85pcku4+eP8psf2Xaf88j/AN9Gj+y7T/nkf++jWEbicEjz5eDj75qeMXEkav8AapBkZxuP+NTNcivKQKcXtE0pLC2jZAtsjAn5i0hG0evvWNOAs7gDAB6CrBhnZwv2hiSM5JP09aYLVn+YyZJ9RRGrCOrl+ZM/eVkiCM/vU/3h/Ot5ovPsPKyBvXGSM1hlPKuUQnPzLzW4VZ7DahIYpxg4/WscW1Lka/rY0w6tcrnTmL8TLkSFgdvKAkEY98DGamgszDMHMm5VzhcdOw/JQBUDWNycskqxu0YXKs2B8v8Aj3605La5DIQ6hSeV3sdoDZwPXg47VzO76nSXg6nbhgd33eev0qrc2xmuC24ovlEbs/xHgH8AT+dQW1jPFcWzSMjLCm3IY/3AuAPTIJ/GtCVS8TqOrKQM/Soa5XoBTjsXURbZEChtxVVO0DcGwvPHT9aZrhxZDnA3cn8KEs7lRABJtVH5USE4+73I56Hj/aqPxH/yDf8AgY/kab+JB0OYkkMjE84Jzim9KTtWrotg0s0V0+wxK/3TySe1dgifS7CO3uVFzLG0zoc24XcQOvzeh9q000+NZfMgaSFv9lRgfpVa0uPN1dIyMMN53YGWBzwT14xWyOO9IexjvoaMdwnJOc8rU2qf8gu6/wCuf9RWicYrO1T/AJBd1/1z/qKYm7nHUUUUCP/Z
data:image/jpeg;base64,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