標(biāo)題: Titlebook: Combating Security Challenges in the Age of Big Data; Powered by State-of- Zubair Md. Fadlullah,Al-Sakib Khan Pathan Book 2020 Springer Nat [打印本頁(yè)] 作者: CYNIC 時(shí)間: 2025-3-21 19:42
書目名稱Combating Security Challenges in the Age of Big Data影響因子(影響力)
書目名稱Combating Security Challenges in the Age of Big Data影響因子(影響力)學(xué)科排名
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書目名稱Combating Security Challenges in the Age of Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Combating Security Challenges in the Age of Big Data被引頻次
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書目名稱Combating Security Challenges in the Age of Big Data讀者反饋
書目名稱Combating Security Challenges in the Age of Big Data讀者反饋學(xué)科排名
作者: Density 時(shí)間: 2025-3-21 21:57
Wissen, Kommunikation und Gesellschafttion exits which reasons that the data is a random variable which is being generated independently from an underlying stationary distribution. In this chapter we present discussions on concept drifts that are inherent in the context big data. We discuss different forms of concept drifts that are evi作者: 完成才會(huì)征服 時(shí)間: 2025-3-22 03:59 作者: coddle 時(shí)間: 2025-3-22 07:11
https://doi.org/10.1007/978-3-531-90761-1utilization will increase the value of information and on the other hand also induces an increase in the number of attacks on such systems. Side Channel Attack (SCA) is an attack model that could disrupt the information security when hardware implements a cryptographic algorithm. Differential Power 作者: 同步左右 時(shí)間: 2025-3-22 10:28 作者: Tortuous 時(shí)間: 2025-3-22 16:02 作者: Tortuous 時(shí)間: 2025-3-22 17:49
https://doi.org/10.1007/978-3-658-33768-1g. It is considered as one of the prime issues regarding cyber world security. Damage caused by the malware programs ranges from system failure to financial loss. Traditional approach for malware classification approach are not very suitable for advance malware programs. For the continuously evolvin作者: Anthrp 時(shí)間: 2025-3-23 00:11 作者: Adrenaline 時(shí)間: 2025-3-23 02:23 作者: 相互影響 時(shí)間: 2025-3-23 06:44
https://doi.org/10.1007/978-3-322-82663-3e of the inter-machine communication, which is popularly referred to as the Machine to Machine (M2M) communications whereby the deployed “things” such as smart meters and numerous sensors require none/minimal human intervention to characterize power requirements and energy distribution. The plethora作者: 使絕緣 時(shí)間: 2025-3-23 11:16
,Zusammenwirken — Praxis und Theorie,structure (AMI) is one of the key components in smart grids that enables two-way communication between end users and the utility using smart meters installed at end users. Cyber security plays a fundamental role to secure communications in the AMI. To ensure confidentiality and integrity, key manage作者: entreat 時(shí)間: 2025-3-23 16:03
https://doi.org/10.1007/978-3-030-35642-2Big Data Analytics and Security; Cloud Computing Resiliency; Internet of Things Security; Blockchain Ba作者: 罵人有污點(diǎn) 時(shí)間: 2025-3-23 18:50
978-3-030-35644-6Springer Nature Switzerland AG 2020作者: 欲望 時(shí)間: 2025-3-23 22:21
Combating Security Challenges in the Age of Big Data978-3-030-35642-2Series ISSN 1613-5113 Series E-ISSN 2363-9466 作者: 以煙熏消毒 時(shí)間: 2025-3-24 02:21
Zubair Md. Fadlullah,Al-Sakib Khan PathanOffers a comprehensive coverage of the main topic, ranging from big data analytics to protection.Provides an up-to-date source on computing/network security and resilience contributed by international作者: 斗爭(zhēng) 時(shí)間: 2025-3-24 09:00 作者: Factual 時(shí)間: 2025-3-24 12:29 作者: 影響深遠(yuǎn) 時(shí)間: 2025-3-24 15:50 作者: Pathogen 時(shí)間: 2025-3-24 21:35
Book 2020f conventional and state-of-the-art techniques.?The incentive for joining big data and advanced analytics is no longer in doubt for businesses and ordinary users alike. Technology giants like Google, Microsoft, Amazon, Facebook, Apple, and companies like Uber, Airbnb, NVIDIA, Expedia, and so forth a作者: 半身雕像 時(shí)間: 2025-3-25 00:30
,Classification of Outlier’s Detection Methods Based on Quantitative or?Semantic Learning,iers, there is a conceptual meaning behind the outlier based on the context of the dataset, shifting the focus to finding the anomalous class of data. We also discuss the use of the proposed definition of semantic learning in detecting credit card frauds..CCS CONCEPTS.作者: 偽書 時(shí)間: 2025-3-25 04:43 作者: abnegate 時(shí)間: 2025-3-25 11:01
Wissen, Kommunikation und Gesellschaft continuously causing existing learned models to lose their predictive accuracy. This chapter will serve as a reference to academicians and industry practitioners who are interested in the niche area of handling concept drift for big data applications.作者: Colonnade 時(shí)間: 2025-3-25 12:04
https://doi.org/10.1007/978-3-658-33768-1 the Internet of Things more intelligent than mere monitoring devices. Big data and IoT works well conjointly to offer analysis and insights. With the conjunction of the Internet of things, big data analytics shift the computing paradigm to the edges for real-time decision making.作者: 走路左晃右晃 時(shí)間: 2025-3-25 18:00
Concept Drift for Big Data, continuously causing existing learned models to lose their predictive accuracy. This chapter will serve as a reference to academicians and industry practitioners who are interested in the niche area of handling concept drift for big data applications.作者: Exploit 時(shí)間: 2025-3-25 21:26 作者: Graves’-disease 時(shí)間: 2025-3-26 01:41 作者: MANIA 時(shí)間: 2025-3-26 05:26 作者: 使顯得不重要 時(shí)間: 2025-3-26 09:56 作者: 暫時(shí)別動(dòng) 時(shí)間: 2025-3-26 14:28
Combating Security Challenges in the Age of Big DataPowered by State-of-作者: 不易燃 時(shí)間: 2025-3-26 19:35 作者: 沙漠 時(shí)間: 2025-3-26 21:42
Cognitive Artificial Intelligence Countermeasure for Enhancing the Security of Big Data Hardware fr power analysis attack. The attacking aspect is reviewed as a form of identification of the correct countermeasure method against power analysis attack using Cognitive Artificial Intelligence (CAI)‘s method called cognitive countermeasure approach in an AES encryption device. Our main contribution i作者: 無(wú)彈性 時(shí)間: 2025-3-27 02:54 作者: mortuary 時(shí)間: 2025-3-27 06:40 作者: 挖掘 時(shí)間: 2025-3-27 11:12 作者: 聲明 時(shí)間: 2025-3-27 17:10 作者: Incommensurate 時(shí)間: 2025-3-27 18:46
Book 2020ns are inherently different at different stages of the big data centric system, namely at the point of big data sensing and collection, delivery over existing networks, and analytics at the data centers. Thus, the book sheds light on how conventional security provisioning techniques like authenticat作者: jaunty 時(shí)間: 2025-3-27 22:42
Die Sinnhaftigkeit der Kategorialen Formen,easures are not applied in big data storage, it will cause some vital consequences. Trust management can be considered as a critical factor which operates seamlessly behind the scenes in IoT big data era to provide a reliable communication between devices. This chapter aims to disclose the trust man作者: companion 時(shí)間: 2025-3-28 05:23
https://doi.org/10.1007/978-3-531-90761-1 power analysis attack. The attacking aspect is reviewed as a form of identification of the correct countermeasure method against power analysis attack using Cognitive Artificial Intelligence (CAI)‘s method called cognitive countermeasure approach in an AES encryption device. Our main contribution i作者: DRILL 時(shí)間: 2025-3-28 10:10
https://doi.org/10.1007/978-3-531-19769-2 prospective solution. Based on our study, we found that Blockchain can enhance the security of Big Data by strengthening the security of the data storage, enhancing the data integrity using digital certificate and chaining the block using hash of previous block, and enhancing data availability usin作者: Prognosis 時(shí)間: 2025-3-28 11:10
https://doi.org/10.1007/978-3-531-19769-2 the smart grid communication should be carefully taken into consideration and adequate authentication methodology should be developed tailored for the smart grid context. In this vein, in this chapter, we first overview the M2M communication framework in the smart grid system and highlight its shor作者: osculate 時(shí)間: 2025-3-28 15:44 作者: Melatonin 時(shí)間: 2025-3-28 20:45 作者: 我吃花盤旋 時(shí)間: 2025-3-29 01:51
1613-5113 the point of big data sensing and collection, delivery over existing networks, and analytics at the data centers. Thus, the book sheds light on how conventional security provisioning techniques like authenticat978-3-030-35644-6978-3-030-35642-2Series ISSN 1613-5113 Series E-ISSN 2363-9466 作者: Generic-Drug 時(shí)間: 2025-3-29 06:08
Secure Big Data Transmission with Trust Management for the Internet of Things (IoT),abeled and stored in the typical database system. Generating intelligent decisions from enormously increasing data in a real-time system is of major concern. Although big data seems to change our lives, it tries to make a burden in the computing environment due to the proliferation of data. In such 作者: GUEER 時(shí)間: 2025-3-29 08:39 作者: Accessible 時(shí)間: 2025-3-29 14:34 作者: 浸軟 時(shí)間: 2025-3-29 17:40
Cognitive Artificial Intelligence Countermeasure for Enhancing the Security of Big Data Hardware frutilization will increase the value of information and on the other hand also induces an increase in the number of attacks on such systems. Side Channel Attack (SCA) is an attack model that could disrupt the information security when hardware implements a cryptographic algorithm. Differential Power
