Computer Forensics
Computer Forensics: The application of investigation and analysis techniques to gather and preserve evidence from a particular computing device in a way that is suitable for presentation in a court of law. The goal of computer forensics is to perform a structured investigation while maintaining a documented chain of evidence to find out exactly what […]
Computer Sequence Checking
Computer Sequence Checking: This refers to methods used in computing to verify the sequence and integrity of data transmitted or stored. Techniques such as checksums, sequence numbers, and cyclic redundancy checks (CRC) can detect and correct errors to ensure the data received is in the correct order and unaltered.
Computer-Aided Software Engineering (CASE)
Computer-Aided Software Engineering (CASE): The use of computer-based tools to support the development, testing, and maintenance of software. It is used to automate and streamline the software development process and to improve the quality and reliability of software. Examples of CASE tools include modeling tools, code generation tools, and testing tools.
Computer-Assisted Audit Technique (CAAT)
Computer-Assisted Audit Technique (CAAT): A set of tools and techniques used by auditors to analyze an organization’s data with software, improving efficiency and accuracy in audit processes. CAATs include data extraction and analysis tools, which can automate procedures to identify anomalies or patterns in data related to financial statements or compliance.
Concealment cipher
Concealment cipher: Also known as a steganographic cipher, concealment cipher hides the existence of a message within another innocent-looking message. Unlike traditional ciphers, which make it apparent that a message has been encrypted, a concealment cipher’s goal is to prevent an observer from even suspecting that a hidden message exists. This is achieved by embedding […]
Clustering of Pseudorandom Numbers
Clustering of Pseudorandom Numbers: This refers to the undesirable pattern in pseudorandom number generation where values are not evenly distributed but rather appear grouped or “clustered” together. Such patterns can compromise the security of cryptographic systems that rely on pseudo-randomness, making them vulnerable to predictability and potential attacks.