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Titlebook: Data Analytics and Decision Support for Cybersecurity; Trends, Methodologie Iván Palomares Carrascosa,Harsha Kumara Kalutarage Book 2017 Sp

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書目名稱Data Analytics and Decision Support for Cybersecurity
副標(biāo)題Trends, Methodologie
編輯Iván Palomares Carrascosa,Harsha Kumara Kalutarage
視頻videohttp://file.papertrans.cn/263/262684/262684.mp4
概述Brings together and disseminates important lessons learned within a young, but surprisingly rapid field.Emphasizes the significant role of Data Science and Artificial Intelligence in overcoming crucia
叢書名稱Data Analytics
圖書封面Titlebook: Data Analytics and Decision Support for Cybersecurity; Trends, Methodologie Iván Palomares Carrascosa,Harsha Kumara Kalutarage Book 2017 Sp
描述.The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. ..?This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detecti
出版日期Book 2017
關(guān)鍵詞Risk assessment approaches to cybersecurity; Network traffic analysis; Network attack monitoring; Insid
版次1
doihttps://doi.org/10.1007/978-3-319-59439-2
isbn_softcover978-3-319-86624-6
isbn_ebook978-3-319-59439-2Series ISSN 2520-1859 Series E-ISSN 2520-1867
issn_series 2520-1859
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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https://doi.org/10.1057/9780230623781ork that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be e
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https://doi.org/10.1057/9780230623781hone platforms. “Total mobile malware has grown 151% over the past year”, according to McAfee?’s quarterly treat report in September 2016. By design, Android. is “open” to download apps from different sources. Its security depends on restricting apps by combining digital signatures, sandboxing, and
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