• Title/Summary/Keyword: 하드웨어 트로이 목마

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Trends of Hardware-based Trojan Detection Technologies (하드웨어 트로이목마 탐지기술 동향)

  • Choi, Y.S.;Lee, S.S.;Choi, Y.J.;Kim, D.W.;Choi, B.C.
    • Electronics and Telecommunications Trends
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    • v.36 no.6
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    • pp.78-87
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    • 2021
  • Information technology (IT) has been applied to various fields, and currently, IT devices and systems are used in very important areas, such as aviation, industry, and national defense. Such devices and systems are subject to various types of malicious attacks, which can be software or hardware based. Compared to software-based attacks, hardware-based attacks are known to be much more difficult to detect. A hardware Trojan horse is a representative example of hardware-based attacks. A hardware Trojan horse attack inserts a circuit into an IC chip. The inserted circuit performs malicious actions, such as causing a system malfunction or leaking important information. This has increased the potential for attack in the current supply chain environment, which is jointly developed by various companies. In this paper, we discuss the future direction of research by introducing attack cases, the characteristics of hardware Trojan horses, and countermeasure trends.

Run-Time Hardware Trojans Detection Using On-Chip Bus for System-on-Chip Design (온칩버스를 이용한 런타임 하드웨어 트로이 목마 검출 SoC 설계)

  • Kanda, Guard;Park, Seungyong;Ryoo, Kwangki
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.2
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    • pp.343-350
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    • 2016
  • A secure and effective on-chip bus for detecting and preventing malicious attacks by infected IPs is presented in this paper. Most system inter-connects (on-chip bus) are vulnerable to hardware Trojan (Malware) attack because all data and control signals are routed. A proposed secure bus with modifications in arbitration, address decoding, and wrapping for bus master and slaves is designed using the Advanced High-Performance and Advance Peripheral Bus (AHB and APB Bus). It is implemented with the concept that arbiter checks share of masters and manage infected masters and slaves in every transaction. The proposed hardware is designed with the Xilinx 14.7 ISE and verified using the HBE-SoC-IPD test board equipped with Virtex4 XC4VLX80 FPGA device. The design has a total gate count of 39K at an operating frequency of 313MHz using the $0.13{\mu}m$ TSMC process.

A Study on Implementation and Countermeasure for Undefined Instruction Hardware Trojan evitable from exception handling (예외 처리를 피하는 정의되지 않은 명령에 의한 하드웨어 트로이 목마의 구현 및 대응책 연구)

  • Kong, Sunhee;Kim, Hanyee;Lee, Bosun;Suh, Taeweon;Yu, Heon Chang
    • Proceedings of the Korea Information Processing Society Conference
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    • 2013.05a
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    • pp.24-26
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    • 2013
  • Undefined Instruction 하드웨어 Trojan 은 정의되지 않은 명령어가 명령어 버스를 통해 CPU 에 유입될 경우 발현되어 CPU 의 전반적인 기능을 마비시킬 수 있는 하드웨어 Trojan 이다. 일반적으로 대부분의 상용화된 CPU 는 Undefined Instruction 에 대한 예외 처리를 지원하는데, ARM 의 경우 파이프 라인의 실행 단계에서 Undefined Instruction 임을 판별한다. 본 연구에서는 파이프 라인의 명령어 추출단계에서 발현되어서 명령어 해독단계에는 다른 명령어를 전달 시킴으로써 Undefined Instruction 예외처리를 피할 수 있는 하드웨어 Trojan 을 설계하고, 이를 방지하는 대응책을 제안한다.

A Study of Machine Learning based Hardware Trojans Detection Mechanisms for FPGAs (FPGA의 Hardware Trojan 대응을 위한 기계학습 기반 탐지 기술 연구)

  • Jang, Jaedong;Cho, Mingi;Seo, Yezee;Jeong, Seyeon;Kwon, Taekyoung
    • Journal of Internet Computing and Services
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    • v.21 no.2
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    • pp.109-119
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    • 2020
  • The FPGAs are semiconductors that can be redesigned after initial fabrication. It is used in various embedded systems such as signal processing, automotive industry, defense and military systems. However, as the complexity of hardware design increases and the design and manufacturing process globalizes, there is a growing concern about hardware trojan inserted into hardware. Many detection methods have been proposed to mitigate this threat. However, existing methods are mostly targeted at IC chips, therefore it is difficult to apply to FPGAs that have different components from IC chips, and there are few detection studies targeting FPGA chips. In this paper, we propose a method to detect hardware trojan by learning the static features of hardware trojan in LUT-level netlist of FPGA using machine learning.