Intrusion Detection and Threat Intelligence for IoT and Cloud

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 June 2019) | Viewed by 559

Special Issue Editors


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Guest Editor
School of Engineering and Information technology, University of New South Wales Canberra, Northcott Drive, Canberra, ACT 2610, Australia
Interests: biometrics; security; cybersecurity; bio-cryptography
Special Issues, Collections and Topics in MDPI journals
School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, ACT 2612, Australia
Interests: intrusion detection; threat intelligence; privacy preservation; digital forensics; machine/deep learning; network systems; IoT; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IoT devices connected to cloud and edge paradigms offer many new opportunities and benefits for manufacturers and consumers. The ubiquitous norm of IoT technology allows new use cases in manufacturing, smart cities, smart factories and other market verticals. However, the security of IoT has not kept pace with the innovation and deployment of solutions that create economic and political issues. The growing number of IoT devices and services increases the attack landscape such as Distributed Denial of Services (DDoS) and ransomware, making the solutions more vulnerable to cyber-attacks. Consequently, assuring the security of each component within an IoT system is crucial in keeping cyber hackers from using it in an unauthorized manner.

This Special Issue will focus on cutting-edge of intrusion detection and threat intelligence techniques for IoT, cloud, mobile, and fog computing paradigms. The topics could include one of the following (but not limited):

  • Collaborative Intrusion Detection for Cloud and IoT
  • Anomaly Detection for DDoS on Cloud and Fog Paradigms
  • Anomaly Detection for Ransomware in IoT Networks
  • Detecting Abnormal Behaviors based on Telemetry Data
  • Threat Intelligence for Advanced Persistent Threats (APT)
  • Adversarial Deep Learning for Threat Intelligence in IoT Systems
  • Privacy-Preserving Anomaly Detection in Computing Paradigms
  • Threat Detection for Computing Resources of Cloud Servers
  • Protection and Mitigation of Sensitive Information of Users and Organizations

Prof. Dr. Jiankun Hu
Dr. Nour Moustafa
Guest Editors

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