AI-Assisted Digital Forensics for Securing Industry 4.0 Assets
A. Shahela, Gandu Suchitra, B. Beulah Aswini, Prince Prashant Kumar Jha · 2024
Industry 4.0 provided a fillip to the manufacturing industry by introducing advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data. However, it led to increases in cybersecurity challenges in securing organizations’ digital assets. Cyberattacks result in significant financial losses, reputation damage, and legal turmoil for organizations. Organizations are turning to AI-powered digital forensics solutions to identify, analyze, and respond to security breaches. These solutions use machine learning (ML) algorithms to sift through large volumes of data and detect anomalies. Digital forensics helps by connecting the gap from the source of attack to the point of attack. It plays a crucial role in securing digital assets by identifying the extent of the attack, assisting in incident response, ensuring compliance with regulations, and protecting intellectual property. The use of machine learning allows analysts to conduct inquiry processes to be successful and fruitful. It is a subfield of science that focuses on creating algorithms and computer models that can carry out particular activities, such as dataset training and validation, and its potential to support investigations. This chapter discusses several machine learning methods that look at and evaluate digital evidence while an inquiry is underway. Based on the features, each machine learning algorithm targets a particular area of digital forensics and solves challenges such as complexity, data volume, timing, correlation, and consistency. As the industry continues to evolve, organizations must remain vigilant and prioritize digital forensics as a crucial component of their cybersecurity strategy. This chapter explores the significance of AI-assisted digital forensics.