標(biāo)題: Titlebook: Data Science in Cybersecurity and Cyberthreat Intelligence; Leslie F. Sikos,Kim-Kwang Raymond Choo Book 2020 Springer Nature Switzerland A [打印本頁(yè)] 作者: 矜持 時(shí)間: 2025-3-21 18:50
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence影響因子(影響力)
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence影響因子(影響力)學(xué)科排名
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence被引頻次
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書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence年度引用學(xué)科排名
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence讀者反饋
書(shū)目名稱Data Science in Cybersecurity and Cyberthreat Intelligence讀者反饋學(xué)科排名
作者: 改變立場(chǎng) 時(shí)間: 2025-3-21 22:21 作者: 教義 時(shí)間: 2025-3-22 00:34 作者: 水獺 時(shí)間: 2025-3-22 06:18 作者: Compass 時(shí)間: 2025-3-22 08:49
Amrina Ferdous,Md. Abu Shahin,Md. Ayub Alicurity threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demo作者: Androgen 時(shí)間: 2025-3-22 16:25
Md. Jahanur Rahman,Md. Al Mehedi Hasans for network attack and anomaly detection. The approach is characterized by several layers of data processing, including extraction and decomposition of datasets, compression of feature vectors, training, and classification. To reduce the dimension of the analyzed feature vectors, principal compone作者: Androgen 時(shí)間: 2025-3-22 17:49
A. Zahiri,H. Md. Azamathulla,Kh. Ghorbanis smart phones and wearables that have been adopted for personal use in everyday life, has produced a demand for utilities that can assist people with achieving goals for a successful lifestyle, i.e., to live healthier and more productive lives. With continued research and development into technolog作者: 來(lái)這真柔軟 時(shí)間: 2025-3-23 00:33 作者: 并入 時(shí)間: 2025-3-23 04:33
Data Science in Cybersecurity and Cyberthreat Intelligence978-3-030-38788-4Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 男生如果明白 時(shí)間: 2025-3-23 08:23
https://doi.org/10.1007/978-3-030-38788-4Cybersecurity; Cybersituational Awareness; Cyberthreat Intelligence; Data Science; Artificial Intelligen作者: 急急忙忙 時(shí)間: 2025-3-23 09:58
978-3-030-38790-7Springer Nature Switzerland AG 2020作者: RAG 時(shí)間: 2025-3-23 15:10 作者: GEM 時(shí)間: 2025-3-23 18:42 作者: 巧辦法 時(shí)間: 2025-3-23 22:30
The Formal Representation of Cyberthreats for Automated Reasoning,ncident response, and comprehensive and automated data analysis. This chapter reviews the most influential and widely deployed cyberthreat classification models, machine-readable taxonomies, and machine-interpretable ontologies that are well-utilized in cyberthreat intelligence applications.作者: SLUMP 時(shí)間: 2025-3-24 04:11
Seven Pitfalls of Using Data Science in Cybersecurity,results provided by a machine learning algorithm. There is some evidence to suggest that algorithm choice is not a discriminator. In particular, we explore the importance of feature set selection and evaluate the inherent problems in relying on synthetic data.作者: febrile 時(shí)間: 2025-3-24 06:55
Book 2020oning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details h作者: Homocystinuria 時(shí)間: 2025-3-24 12:45
Discovering Malicious URLs Using Machine Learning Techniques,urce Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.作者: 確定無(wú)疑 時(shí)間: 2025-3-24 17:25 作者: 領(lǐng)帶 時(shí)間: 2025-3-24 19:55 作者: LINE 時(shí)間: 2025-3-25 02:12 作者: 興奮過(guò)度 時(shí)間: 2025-3-25 06:27
A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks,ctively identifying and systematically understanding . and . those events are likely to occur is still challenging. It has earlier been shown that monitoring malicious hacker discussions about software vulnerabilities in the Dark web and Deep web platforms (D2web) is indicative of future cyberattack作者: 束以馬具 時(shí)間: 2025-3-25 09:50
Discovering Malicious URLs Using Machine Learning Techniques,curity threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demo作者: 故意 時(shí)間: 2025-3-25 14:14
Machine Learning and Big Data Processing for Cybersecurity Data Analysis,s for network attack and anomaly detection. The approach is characterized by several layers of data processing, including extraction and decomposition of datasets, compression of feature vectors, training, and classification. To reduce the dimension of the analyzed feature vectors, principal compone作者: 小卷發(fā) 時(shí)間: 2025-3-25 17:59
Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Netwos smart phones and wearables that have been adopted for personal use in everyday life, has produced a demand for utilities that can assist people with achieving goals for a successful lifestyle, i.e., to live healthier and more productive lives. With continued research and development into technolog作者: Brain-Imaging 時(shí)間: 2025-3-25 23:36 作者: 兩種語(yǔ)言 時(shí)間: 2025-3-26 03:49 作者: 觀察 時(shí)間: 2025-3-26 04:44
Mohamed Lahby,Ala Al-Fuqaha,Yassine Malehtrends identified from the context of hacker discussions across multiple hacker community websites. Our approach is evaluated on real-world, enterprise-targeted attack events of malicious emails. Compared to a baseline statistical prediction model, our approach provides better precision-recall trade作者: 600 時(shí)間: 2025-3-26 10:33
Md. Jahanur Rahman,Md. Al Mehedi Hasannd majority voting. Two different architectures of the distributed intrusion detection system based on Big Data technologies are used. In the first, parallel data processing is achieved by splitting data into several non-intersecting subsets, and a separate parallel thread is assigned to each of the作者: Mindfulness 時(shí)間: 2025-3-26 15:23 作者: 神秘 時(shí)間: 2025-3-26 17:24
A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks,trends identified from the context of hacker discussions across multiple hacker community websites. Our approach is evaluated on real-world, enterprise-targeted attack events of malicious emails. Compared to a baseline statistical prediction model, our approach provides better precision-recall trade作者: 善于 時(shí)間: 2025-3-26 23:00
Machine Learning and Big Data Processing for Cybersecurity Data Analysis,nd majority voting. Two different architectures of the distributed intrusion detection system based on Big Data technologies are used. In the first, parallel data processing is achieved by splitting data into several non-intersecting subsets, and a separate parallel thread is assigned to each of the作者: Conclave 時(shí)間: 2025-3-27 04:24
Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Netwosurance companies and hospitals or doctors. Data collected by these sensors are transmitted by the devices to a centralized server, which can be accessed and retrieved by those service providers for further processing, analysis, and use. Devices used for this purpose through the IoT network can be r作者: 肥料 時(shí)間: 2025-3-27 06:33
Data Science in Cybersecurity and Cyberthreat Intelligence作者: 新星 時(shí)間: 2025-3-27 12:40 作者: 高歌 時(shí)間: 2025-3-27 14:07 作者: Interstellar 時(shí)間: 2025-3-27 19:30
1613-5113 , multidimensional, collective and well-coordinated responses..The European Union, Organization for Security and Co-operation in Europe, United Nations, as well as other international organisations are able to 978-3-030-62730-0978-3-030-62728-7Series ISSN 1613-5113 Series E-ISSN 2363-9466 作者: Countermand 時(shí)間: 2025-3-27 22:20 作者: Abominate 時(shí)間: 2025-3-28 02:26
Alte Strafnehmerdenzen bemerkbar, die alten, abgelegten Strafmuster wieder aufzunehmen. Kollektive Verantwortung wird statuiert, Tote und Tiere werden bestraft, leblose Gegenst?nde werden der Vernichtung anheimgegeben, ?bestraft“. Diese Ans?tze werden nicht gebilligt, erfahren aber die Art der Approbation, die sich
