Introduction

Cyber forensics is a specialized area of digital forensics that focuses on the investigation, analysis, and preservation of digital evidence concerning cybercrimes, cybersecurity incidents, and illicit activities carried out through digital means. It is also known as computer forensics or cyber forensic science. Network forensics, virus analysis, memory forensics, data recovery, digital forensics and cybercrime investigations are just a few of the many forensic specialties that are included in the broad field of cyber forensics. 

Digital forensic science also referred to as digital forensics, is a multidisciplinary area that includes the methodical identification, preservation, extraction, analysis, and presentation of digital evidence from a range of digital media, digital environments, and electronic devices. Digital forensics extends beyond conventional computer systems to include a wide range of technologies, such as digital networks, mobile devices, cloud forensics, storage devices analysis, and analysis of Internet of Things (IoT) devices. Investigators in this field look into a variety of criminal activity, legal disputes, cybersecurity events, corporate frauds, thefts of intellectual property, and regulatory compliance issues using a multitude of forensic technologies, methodologies, and best practices. 

The field of multimedia forensics employs a range of approaches and procedures to examine, verify, and comprehend multimedia information for investigative, legal, and security objectives. Its main objective is to identify content for digital media that has been altered, manipulated, or forged. Steganography and watermarking, as well as picture, video, and audio forensics, are important areas of focus. To solve the issues raised by multimedia manipulation and forgery, these solutions combine conventional forensic procedures, machine learning, digital signal processing algorithms, and data analysis tools.

MATLAB:

MathWorks created the high-level programming and numerical computing platform known as MATLAB (Matrix Laboratory). For matrix manipulation, algorithm implementation, data visualization, and numerical computations, it offers an interactive environment and a range of built-in tools. MATLAB's extensive capability and versatility make it widely used in a wide range of areas, including engineering, mathematics, science, finance, and research. MATLAB software has a bright future in signal processing, image processing, machine learning, and data analysis because of its extensive ecosystem of features. MATLAB enables users to push the boundaries of AI research and applications with advances in deep learning and AI. It encourages the creation of innovative optimization methods, interpretability tools, and algorithms. Another important factor driving MATLAB innovation is interdisciplinary collaboration, which gives academics access to the software's capabilities in areas like financial modeling, medical imaging, and geographic analysis. Given that big data is growing at an exponential rate and dataset complexity is rising, MATLAB's future in data analytics and visualization looks very promising. But there are issues that need to be resolved, like scalability, performance optimization, and computational resources.