標(biāo)題: Titlebook: Artificial Intelligence for Cyber-Physical Systems Hardening; Issa Traore,Isaac Woungang,Sherif Saad Book 2023 The Editor(s) (if applicabl [打印本頁] 作者: Orthosis 時間: 2025-3-21 17:43
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書目名稱Artificial Intelligence for Cyber-Physical Systems Hardening讀者反饋
書目名稱Artificial Intelligence for Cyber-Physical Systems Hardening讀者反饋學(xué)科排名
作者: 被告 時間: 2025-3-21 23:53 作者: entitle 時間: 2025-3-22 04:23 作者: 實現(xiàn) 時間: 2025-3-22 06:40
A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms,of defense platforms. At its inception, the standard was conceived with a focus only on reliability and fault tolerance, with no attention paid to security concerns. However, it has been shown in the last few years that modern defense platforms are increasingly the target of cyber-attacks from both 作者: mercenary 時間: 2025-3-22 09:21 作者: Proponent 時間: 2025-3-22 13:41
Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driill be deployed on radio frequency (RF)- based networks. As such, society will be heavily dependent on the ability to protect these new wireless networks as well as the radio spectrum. Solutions such as artificial intelligence (AI)-based transmitter fingerprinting to identify and track unintended in作者: installment 時間: 2025-3-22 19:48 作者: 音樂戲劇 時間: 2025-3-23 01:04
Security and Privacy of IoT Devices for Aging in Place,lve this issue using emerging technologies centered around smart IoT devices. To ensure security and privacy for a smart home for aging in place, different aspects of the IoT devices have to be considered. This chapter seeks to provide a categorical review and analysis of age-tech IoT device technol作者: ineffectual 時間: 2025-3-23 02:04
Detecting Malicious Attacks Using Principal Component Analysis in Medical Cyber-Physical Systems,lso paved a way for a large number of cybercriminal activities targeting these networked devices, raising serious security and privacy concerns when healthcare professionals deal with sensitive and life-critical medical information. Existing security solutions in this domain are mainly prevention-ba作者: 背信 時間: 2025-3-23 07:31
,Activity and?Event Network Graph and?Application to?Cyber-Physical Security,information about the operations of networked systems and data centers. The model allows identifying long-term and stealthy attack patterns, which may be difficult to capture using traditional approaches. The current chapter focuses on defining the model elements and the underlying graph constructio作者: Palliation 時間: 2025-3-23 09:57
Space: Exclusion and Engagement, cyber-attacks in CPSs, this chapter outlines the roles of DL and Deep Reinforcement Learning (DRL). Also, we present state-of-the-art solutions without sacrificing technical details. Additionally, we describe common datasets used for DL in CPSs. Finally, we express research opportunities and challenges in the CPSs with respect to DL.作者: 類人猿 時間: 2025-3-23 17:02 作者: magenta 時間: 2025-3-23 19:44
2731-5002 g software, hardware, firmware, infrastructure, and communic.This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to作者: 后天習(xí)得 時間: 2025-3-23 22:47 作者: 我不死扛 時間: 2025-3-24 03:43
Raymond D. Hill,Franco Modigliani be difficult to capture using traditional approaches. The current chapter focuses on defining the model elements and the underlying graph construction algorithms, and presents a case study based on a cyberphysical security dataset.作者: 懸崖 時間: 2025-3-24 06:30 作者: Orgasm 時間: 2025-3-24 12:47
,Activity and?Event Network Graph and?Application to?Cyber-Physical Security, be difficult to capture using traditional approaches. The current chapter focuses on defining the model elements and the underlying graph construction algorithms, and presents a case study based on a cyberphysical security dataset.作者: outskirts 時間: 2025-3-24 17:09 作者: 紀(jì)念 時間: 2025-3-24 22:48 作者: Junction 時間: 2025-3-25 01:37
https://doi.org/10.1007/978-94-015-7753-3We evaluate our approach with a publicly available dataset collected in a real-time medical cyber-physical system testbed network and the results show the proposed approach successfully detects malicious attacks with a high detection rate and an acceptable low false alarm rate.作者: tolerance 時間: 2025-3-25 06:29
A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms,ted by executing a selected attack scenarios in a testbed environment. It is expected that the proposed datasets can be used toward designing and evaluating intrusion detection systems for MIL-STD-153 avionic platforms.作者: insurgent 時間: 2025-3-25 09:04 作者: Thyroiditis 時間: 2025-3-25 14:58 作者: arterioles 時間: 2025-3-25 18:51 作者: Headstrong 時間: 2025-3-25 22:54
Qingyun Jiang,Lixian Qian,Min Dingn detection algorithms that have the capability of learning from security data to be able to hunt threats, achieve better monitoring, master the complexity of the threat intelligence feeds, and achieve timely remediation of security incidents. The field of ML can be decomposed into two basic subfiel作者: 復(fù)習(xí) 時間: 2025-3-26 00:22
Perspectives on Sustainable Growthcal framework for these methods that can estimate both the error rate (a one-sample statistic) and the AUC (a two-sample statistic). The resampling methods are usually computationally expensive, because they rely on repeating the training and testing of a ML algorithm after each resampling iteration作者: rectum 時間: 2025-3-26 06:42 作者: 小口啜飲 時間: 2025-3-26 10:51
Space: Exclusion and Engagement,cture (V2I), vehicle to vehicle (V2V), and other telecommunications capabilities by 2022. To ensure the safety of the public, new and automated techniques are needed to protect CAVs on the road from unintentional or malicious interference. Against these requirements, this chapter presents the state 作者: orient 時間: 2025-3-26 14:47
Artificial Intelligence for Cyber-Physical Systems Hardening作者: 胰島素 時間: 2025-3-26 19:14
Machine Learning Construction: Implications to Cybersecurity,n detection algorithms that have the capability of learning from security data to be able to hunt threats, achieve better monitoring, master the complexity of the threat intelligence feeds, and achieve timely remediation of security incidents. The field of ML can be decomposed into two basic subfiel作者: 開始發(fā)作 時間: 2025-3-26 22:38
,Machine Learning Assessment: Implications to?Cybersecurity,cal framework for these methods that can estimate both the error rate (a one-sample statistic) and the AUC (a two-sample statistic). The resampling methods are usually computationally expensive, because they rely on repeating the training and testing of a ML algorithm after each resampling iteration作者: Common-Migraine 時間: 2025-3-27 01:12
Unsupervised Anomaly Detection for MIL-STD-1553 Avionic Platforms Using CUSUM,ion bus to extract a set of relevant features that are fed to the CUSUM algorithm for detection. The experimental evaluation of the proposed detector using the dataset yielded promising results, which are very encouraging considering the unsupervised nature of the underlying algorithm.作者: 有效 時間: 2025-3-27 07:04 作者: 鎮(zhèn)壓 時間: 2025-3-27 12:39 作者: 男學(xué)院 時間: 2025-3-27 17:37
Qingyun Jiang,Lixian Qian,Min Dings. A statistical learning machine (SLM) is the algorithm, function, model, or rule, that learns such a process; and machine learning (ML) is the conventional name of this field. ML and its applications are ubiquitous in the modern world. Systems such as Automatic target recognition (ATR) in military作者: 紅潤 時間: 2025-3-27 18:53 作者: Adornment 時間: 2025-3-28 01:57
Qingyun Jiang,Lixian Qian,Min Dingof defense platforms. At its inception, the standard was conceived with a focus only on reliability and fault tolerance, with no attention paid to security concerns. However, it has been shown in the last few years that modern defense platforms are increasingly the target of cyber-attacks from both 作者: 沙漠 時間: 2025-3-28 02:48 作者: Efflorescent 時間: 2025-3-28 08:20
Space: Exclusion and Engagement,ill be deployed on radio frequency (RF)- based networks. As such, society will be heavily dependent on the ability to protect these new wireless networks as well as the radio spectrum. Solutions such as artificial intelligence (AI)-based transmitter fingerprinting to identify and track unintended in作者: creatine-kinase 時間: 2025-3-28 12:53 作者: 外觀 時間: 2025-3-28 17:54
https://doi.org/10.1007/978-3-319-33771-5lve this issue using emerging technologies centered around smart IoT devices. To ensure security and privacy for a smart home for aging in place, different aspects of the IoT devices have to be considered. This chapter seeks to provide a categorical review and analysis of age-tech IoT device technol作者: Yag-Capsulotomy 時間: 2025-3-28 22:43 作者: Override 時間: 2025-3-29 00:52
Raymond D. Hill,Franco Modiglianiinformation about the operations of networked systems and data centers. The model allows identifying long-term and stealthy attack patterns, which may be difficult to capture using traditional approaches. The current chapter focuses on defining the model elements and the underlying graph constructio作者: 情節(jié)劇 時間: 2025-3-29 04:20
https://doi.org/10.1007/978-3-031-16237-4Cyber-Physical Systems; Artificial Intelligence; Machine Learning; Security; Privacy; Trust作者: ATOPY 時間: 2025-3-29 10:59 作者: Iniquitous 時間: 2025-3-29 14:07 作者: Colonnade 時間: 2025-3-29 19:21
Engineering Cyber-Physical Systems and Critical Infrastructureshttp://image.papertrans.cn/b/image/162363.jpg
