Advancements in AI Applications Across Cybersecurity, Archaeology, and IoMT

The recent publications in the fields of cybersecurity, digital forensics, archaeology, and the Internet of Medical Things (IoMT) highlight a significant trend towards the integration of machine learning (ML) and deep learning (DL) technologies to address complex challenges. In cybersecurity and digital forensics, ML and DL are increasingly utilized for intrusion detection, malware classification, and anomaly detection, aiming to enhance system resilience against cyber threats. Similarly, in archaeology, ML applications are expanding, with a focus on automatic structure detection, artefact classification, and predictive modeling, despite challenges related to methodology clarity and application goals. The IoMT sector is witnessing a surge in research aimed at mitigating security vulnerabilities, particularly against malware and DDoS attacks, through innovative solutions like ML algorithms, blockchain, and edge computing. These developments underscore a broader movement towards leveraging AI to solve domain-specific problems, improve operational efficiency, and secure digital infrastructures.

Noteworthy Papers

  • Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review: Offers a comprehensive overview of ML and DL applications in cybersecurity, highlighting their potential to revolutionize threat detection and analysis.
  • Machine learning applications in archaeological practices: a review: Provides an exhaustive analysis of ML's role in archaeology, emphasizing the need for structured methodologies and collaborative practices.
  • Am I Infected? Lessons from Operating a Large-Scale IoT Security Diagnostic Service: Presents insights from a large-scale IoT security service, demonstrating the effectiveness of user-engaged security diagnostics.
  • A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS: Explores critical vulnerabilities in IoMT devices and proposes emerging solutions to enhance security, emphasizing the importance of lightweight security measures.

Sources

Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review

Machine learning applications in archaeological practices: a review

Am I Infected? Lessons from Operating a Large-Scale IoT Security Diagnostic Service

A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS

Built with on top of