A Scalable High-Performance Virus Detection Processor Against a Large Pattern Set for Embedded Network Security

Cheng, Chieh-Jen; Wang, Chao-Ching; Ku, Wei-Chun; Chen, Tien-Fu; Wang, Jinn‐Shyan

doi:10.1109/tvlsi.2011.2119382

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…This coding scheme can produce a secrecy capacity region for the channel in several scenarios. The works in [22] and [23] focused on the virus detection and hardware security problems of embedded systems, respectively. In addition, there have been works that focus on the security of the control networks of various industrial systems [32], [33], [34].…”

Section: Security Technologies For Embedded Systemsmentioning

confidence: 99%

“…Currently, increasing threats are capable of disturbing the normal function of an industrial automation system because many sophisticated attacks can masquerade as legitimate applications or services to destroy the resources of RTAI-Linux, such as user processes, the kernel, and industrial I/O. There have been some works that focused on the security of embedded systems [18], [19] [20], [21], [22], [23]. However, these works just studied single points of the security in embedded systems, including the security of the code, memory, processor, etc.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Secure Management Against APT Attacks for Intelligent Embedded-Enabled Smart Manufacturing

Dong

Ota

et al. 2020

IEEE Trans. Sustain. Comput.

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Intelligent embedded-enable smart manufacturing is an important infrastructure for future industries. Increasing security threats are disturbing the normal operations of smart manufacturing. As a novel type of threat, an advanced persistent threat (APT) has the novel features of strong concealment, latency and long-term entanglement, which can penetrate the core systems of smart manufacturing, especially for intelligent embedded systems, and cause great destruction from the cyber side to physical side. However, the existing security schemes cannot provide sustainable resource management, which causes the core system in smart manufacturing not to perform sustainable secure detection and defense against APTs. To address this challenge, this paper proposes a sustainable secure management mechanism for smart manufacturing against APTs. The proposed mechanism includes two parts: sustainable threat intelligence analysis and sustainable secure resource management. Sustainable threat intelligence analysis provides sustainable discovery of the indications of potential APTs , which has features of a weak signal, low correlation and slow time variation. The sustainable secure resource management provides deep and continuous protection for intelligent embedded systems in smart manufacturing. The evaluations show the defense capabilities and the feasibility of the proposed mechanism.

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