会议简介

Rapid proliferation of computing and communication systems with increasing computational power and connectivity into every sphere of modern life has brought security to the forefront of system design, test, and validation processes. The emergence of new application spaces for these systems in the internet-of-things (IoT) regime is creating new attack surfaces as well as new requirements for secure and trusted system operation. Additionally, the design, manufacturing and the distribution of microchip, PCB, as well as other electronic components are becoming more sophisticated and globally distributed with a number of potential security vulnerabilities. Therefore, hardware plays an increasingly important and integral role in system security with many emerging system and application vulnerabilities and defense mechanisms relating to hardware.

征稿信息

重要日期

初稿截稿日期:2018-10-15

征稿简介

IEEE International Symposium on Hardware Oriented Security and Trust (HOST) aims to facilitate the rapid growth of hardware-based security research and development, and to highlight new results in the area of hardware security. HOST 2019 invites original contributions in all areas of overlap between hardware and security. This includes but is not limited to the following:

Hardware

  • Security primitives
  • Computer-aided design (CAD) tools
  • Emerging and nanoscale devices
  • Trojans and backdoors
  • Side-channel attacks and mitigation
  • Fault injection and mitigation
  • (Anti-)Reverse engineering and physical attacks
  • Anti-tamper
  • Anti-counterfeit

Architecture

  • Trusted execution environments
  • Cache-side channel attacks and mitigation
  • Privacy-preserving computation
  • System-on-chip (SoC)/platform security
  • FPGA and reconfigurable fabric security
  • Cloud computing
  • Smart phones and smart devices

System

  • Internet-of-things (IoT) security
  • Sensors and sensor network security
  • Smart grid security
  • Automotive/autonomous vehicle security
  • Cyber-physical system security
  • (Adversarial) Machine learning and cyber deception

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