標(biāo)題: Titlebook: Explainable AI for Cybersecurity; Zhixin Pan,Prabhat Mishra Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic [打印本頁] 作者: Ensign 時間: 2025-3-21 17:35
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書目名稱Explainable AI for Cybersecurity讀者反饋學(xué)科排名
作者: 招待 時間: 2025-3-21 23:02
AI Trojan Attacks and Countermeasures145–1153); Pan and Mishra (Automated detection of Spectre and Meltdown attacks using explainable machine learning. In: 2021 IEEE international symposium on hardware oriented security and trust (HOST). IEEE, 2021, pp 24–34); Witharana and Mishra (Speculative load forwarding attack on modern processor作者: Picks-Disease 時間: 2025-3-22 02:51 作者: Crohns-disease 時間: 2025-3-22 05:26
Santino D. Carnevale,Roberto C. Myersn technique for spectral normalization based on Fourier transform and layer separation. The proposed method provides DNNs with promising security protection while maintaining minimized time cost, which turns SN from a theoretically feasible approach to a practically useful framework. Experimental ev作者: 債務(wù) 時間: 2025-3-22 12:03 作者: sacrum 時間: 2025-3-22 13:03
Perspectives on Individual Differencesre events in sequential timestamps rather than relying on overall statistics. We utilize long-short term memory as well as ensemble boosting to accelerate the training speed. We investigate both model distillation and the Shapley value analysis to improve the model’s explainability.作者: sacrum 時間: 2025-3-22 17:10
Spectre and Meltdown Detection Using Explainable AIre events in sequential timestamps rather than relying on overall statistics. We utilize long-short term memory as well as ensemble boosting to accelerate the training speed. We investigate both model distillation and the Shapley value analysis to improve the model’s explainability.作者: 橡子 時間: 2025-3-22 22:58 作者: 俗艷 時間: 2025-3-23 02:36 作者: synovial-joint 時間: 2025-3-23 05:56 作者: 原告 時間: 2025-3-23 13:34 作者: 較早 時間: 2025-3-23 15:16 作者: Adjourn 時間: 2025-3-23 20:47 作者: 發(fā)酵 時間: 2025-3-24 01:15 作者: 哄騙 時間: 2025-3-24 05:44
The Future of AI-Enabled Cybersecurity of vulnerability detection and mitigation methods using explainable AI. This chapter concludes the book with a summary of ideas presented in the previous chapters and outlines the road map of future cybersecurity challenges and opportunities.作者: optional 時間: 2025-3-24 07:59 作者: Flatter 時間: 2025-3-24 11:20
Siamak Talatahari,Hadi Bayzidi,Mehdi Bayzidiate Arrays (FPGA) and Graphic Processing Units (GPU). Hardware acceleration enables fast and efficient explainable AI models that provide explainability as well as applicability across diverse domains, including real-time and safety-critical systems.作者: output 時間: 2025-3-24 18:03
Mary M. Eshaghian-Wilner,Lili Haital evaluation demonstrates that the proposed approach deployed on TPU can provide drastic improvement in interpretation time (39x on average) as well as energy efficiency (69x on average) compared to existing acceleration techniques.作者: HAIL 時間: 2025-3-24 22:14 作者: WITH 時間: 2025-3-25 02:30
Hardware Acceleration of Explainable AIate Arrays (FPGA) and Graphic Processing Units (GPU). Hardware acceleration enables fast and efficient explainable AI models that provide explainability as well as applicability across diverse domains, including real-time and safety-critical systems.作者: Intellectual 時間: 2025-3-25 06:15 作者: cushion 時間: 2025-3-25 08:19 作者: Provenance 時間: 2025-3-25 13:53 作者: irreducible 時間: 2025-3-25 18:11
Characteristics of Electrolytes,eness of signals to significantly improve the trigger coverage. Experimental results demonstrate that reinforcement learning can drastically improve both trigger coverage (14.5% on average) and test generation time (6.5 times on average) compared to state-of-the-art techniques.作者: inventory 時間: 2025-3-25 21:26
Conducting-Polymer-Based Supercapacitors,f lightweight models that significantly reduces the training time while providing robustness against adversarial attacks. Experimental results demonstrate that this framework can drastically improve both detection accuracy (up to 24.6%) and time efficiency (up to 5.1x) compared to state-of-the-art HT detection techniques.作者: Initiative 時間: 2025-3-26 01:48 作者: CLEFT 時間: 2025-3-26 04:52 作者: 同步左右 時間: 2025-3-26 11:27
Hardware Trojan Detection Using Shapley Ensemble Boostingf lightweight models that significantly reduces the training time while providing robustness against adversarial attacks. Experimental results demonstrate that this framework can drastically improve both detection accuracy (up to 24.6%) and time efficiency (up to 5.1x) compared to state-of-the-art HT detection techniques.作者: 體貼 時間: 2025-3-26 15:27 作者: 相符 時間: 2025-3-26 18:50
http://image.papertrans.cn/e/image/319281.jpg作者: chronicle 時間: 2025-3-26 23:59 作者: decode 時間: 2025-3-27 03:02 作者: 熔巖 時間: 2025-3-27 07:27
Shouchuan Hu,Nikolas S. Papageorgiou as anti-virus software, are not effective since they rely on matching patterns that can be easily fooled by carefully crafted malware with obfuscation or other deviation capabilities. While recent malware detection methods provide promising results through an effective utilization of hardware featu作者: Protein 時間: 2025-3-27 12:58 作者: 胰臟 時間: 2025-3-27 15:58
Characteristics of Electrolytes,own as hardware Trojans. Unfortunately, traditional simulation-based validation using millions of test vectors is unsuitable for detecting stealthy Trojans with extremely rare trigger conditions due to exponential input space complexity of modern SoCs. There is a critical need to develop efficient T作者: 過于光澤 時間: 2025-3-27 21:02
Applications of Supercapacitors,ods that rely on the delay difference of a few gates, this framework utilizes critical path analysis to generate test vectors that can maximize the side-channel sensitivity. The experimental results demonstrate that this framework can significantly improve both side-channel sensitivity (59% on avera作者: 吹牛者 時間: 2025-3-28 00:40
Conducting-Polymer-Based Supercapacitors,e promising machine learning-based HT detection techniques, they have three major limitations: ad hoc feature selection, lack of explainability, and vulnerability toward adversarial attacks. In this chapter, we describe an efficient HT detection approach using an effective combination of Shapley val作者: 小淡水魚 時間: 2025-3-28 02:45 作者: 我沒有強(qiáng)迫 時間: 2025-3-28 06:20 作者: Cholesterol 時間: 2025-3-28 12:12 作者: 脾氣暴躁的人 時間: 2025-3-28 18:25
Mary M. Eshaghian-Wilner,Lili Haicifically, we explore the effectiveness of Tensor Processing Unit (TPU) in accelerating explainable AI by exploiting the synergy between matrix convolution and Fourier transform, and therefore, it takes full advantage of TPU’s inherent ability in accelerating matrix computations. Extensive experimen作者: zonules 時間: 2025-3-28 18:52
Basics of Computer Architecturen trustworthy computing systems, it is crucial to identify and mitigate both hardware and software vulnerabilities. This book presented a wide variety of vulnerability detection and mitigation methods using explainable AI. This chapter concludes the book with a summary of ideas presented in the prev作者: Urgency 時間: 2025-3-29 01:19 作者: MORT 時間: 2025-3-29 05:52 作者: 剛毅 時間: 2025-3-29 09:07 作者: obviate 時間: 2025-3-29 15:19
Explainable Artificial Intelligencehms are powerful at carrying out their tasks, they often do not provide any human-understandable explanation as to how the ML model reached its conclusion for a specific input. Explainable AI aims to explain an ML model’s decision-making in a human-comprehensible form. These explanations can be util
