International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This paper presents the application of Python in computer forensics, proposing various approaches for file system analysis, memory forensics, network monitoring, and malware detection. It examines Python’s integration with forensic tools such as Autopsy, The Sleuth Kit (TSK), Volatility, and Scapy, highlighting its role in automating forensic investigations and enhancing efficiency. The research discusses the advantages of Python in forensic analysis, emphasizing its role in forensic automation, script-based evidence collection, and rapid data processing within various case studies.

This paper discusses key challenges and methods in computer forensics and presents a comparative analysis of forensic software tools FTK, Autopsy, The Sleuth Kit TSK, TestDisk and Foremost. The instruments considered in the study are applied in different case studies to collect, analyze and preserve digital evidence available for computer forensics, cybersecurity and cloud-based investigations. The research discusses their effectiveness and limitations in handling cloud-based evidence collection.

The main goal of this paper is to present methods and tools for secure software development. The process of creating secure software involves analysis, design and implementation based on multi criteria decision making risk assessment. The results of this study give readers some proposals how to produce secure software systems and conduct cost-benefit analysis.

The main purpose of this paper is to offer a data-driven approach to assess cyber risk and to ensure appropriate confidentiality, integrity, and availability. A study presents the decision hierarchical model of cyber security risk assessment based on AHP methodology and describes a quantitative measure for evaluating and ranking security incidents. It can be used to conduct cost-benefit analysis, design and optimize cybersecurity in the systems.