Security intelligenc的問題,透過圖書和論文來找解法和答案更準確安心。 我們找到下列評價、門市、特惠價和推薦等優惠

Security intelligenc的問題,我們搜遍了碩博士論文和台灣出版的書籍,推薦Maleh, Yassine,Sahid, Abdelkebir,Belaissaoui, Mustapha寫的 It Governance and Information Security: Guides, Standards and Frameworks 可以從中找到所需的評價。

逢甲大學 資訊工程學系 劉宗杰所指導 林則勳的 基於集成式惡意流量偵測演算法之研究 (2019),提出Security intelligenc關鍵因素是什麼,來自於殭屍網路、機器學習、分類、集成模型。

而第二篇論文國立臺中科技大學 資訊工程系碩士班 陳同孝、陳民枝所指導 許正成的 應用於大數據運算之虛擬化高效能電腦架構設計與研究 (2017),提出因為有 大數據、虛擬化的重點而找出了 Security intelligenc的解答。

接下來讓我們看這些論文和書籍都說些什麼吧:

除了Security intelligenc,大家也想知道這些:

It Governance and Information Security: Guides, Standards and Frameworks

為了解決Security intelligenc的問題,作者Maleh, Yassine,Sahid, Abdelkebir,Belaissaoui, Mustapha 這樣論述:

Yassine Maleh (http: //orcid.org/0000-0003-4704-5364) is a PhD of the University Hassan 1st in Morocco in the field of Internet of Things Security and privacy, since 2013. He is Senior Member of IEEE, Member of the International Association of Engineers IAENG and The Machine Intelligence Research La

bs. Dr Maleh has made contributions in the fields of information security and privacy, Internet of Things Security, Wireless and Constrained Networks Security. His research interests include Information Security and Privacy, Internet of Things, Networks Security, Information system and IT Governance

. He has published over than 70 papers (Book chapters, international journals and conferences/workshops), and 8 edited books and 3 authored books. He is the editor in chief of the International Journal of Smart Security Technologies (IJSST). He serves as an Associate Editor for IEEE Access (2019 Imp

act Factor 4.098), the International Journal of Digital Crime and Forensics (IJDCF) and the International Journal of Information Security and Privacy (IJISP). He was also a Guest Editor of a special issue on Recent Advances on Cyber Security and Privacy for Cloud-of-Things of the International Journ

al of Digital Crime and Forensics (IJDCF), Volume 10, Issue 3, July-September 2019. He has served and continues to serve on executive and technical program committees and as a reviewer of numerous international conference and journals such as Elsevier Ad Hoc Networks, IEEE Network Magazine, IEEE Sen

sor Journal, ICT Express, and Springer Cluster Computing. He was the Publicity chair of BCCA 2019 and the General Chair of the MLBDACP 19 symposium and ICI2C’21 conference. He received Publon Top 1% reviewer award for the years 2018 and 2019Mamoun Alazab (https: //orcid.org/0000-0002-1928-3704) is t

he Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. He received his Ph.D. degree is in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cyber security researc

her and practitioner with industry and academic experience. Dr Alazab’s research is multidisciplinary that focuses on cyber security and digital forensics of computer systems including current and emerging issues in the cyber environment like cyber-physical systems and the internet of things, by tak

ing into consideration the unique challenges present in these environments, with a focus on cybercrime detection and prevention. He looks into the intersection use of machine learning as an essential tool for cybersecurity, for example, for detecting attacks, analyzing malicious code or uncovering v

ulnerabilities in software. He has more than 100 research papers. He is the recipient of short fellowship from Japan Society for the Promotion of Science (JSPS) based on his nomination from the Australian Academy of Science. He delivered many invited and keynote speeches, 27 events in 2019 alone. He

convened and chaired more than 50 conferences and workshops. He is the founding chair of the IEEE Northern Territory Subsection: (Feb 2019 - current). He is a Senior Member of the IEEE, Cybersecurity Academic Ambassador for Oman’s Information Technology Authority (ITA), Member of the IEEE Computer

Society’s Technical Committee on Security and Privacy (TCSP) and has worked closely with government and industry on many projects, including IBM, Trend Micro, the Australian Federal Police (AFP), the Australian Communications and Media Authority (ACMA), Westpac, UNODC, and the Attorney General’s Dep

artment. Sahid Abdelkbir is from Morocco. He is a PhD Student at the University Hassan 1st in Settat Morocco, since 2014. He received his Master degree (2012) in Computer Sciences from the Faculty of Science and Technology Settat, Morocco, and his Bachelor in Networks and IT Systems (2009) from Hass

an 1st University Morocco. His research interests include Information Systems, IT Service Management, IT Security and IT Agility. He is the author of the book "Strategic Information System Agility: From Theory to Practices", by Emerald.Mustapha Belaissaoui is a Professor of Computer Science at Hassa

n 1st Univesity, Settat, Morocco, President of the Moroccan Association of Free Software (AMP2L), and Head of Master Management Information System and Communication. He obtained his PhD in Artificial Intelligenc from Mohammed V University in Rabat. His research interests are Combinatorial Optimizati

on, Artificial Intelligence and Information Systems. He is the author and co-author of more than 70 papers including journals, conferences, chapters, and books, which appeared in refereed specialized journals and symposia.

基於集成式惡意流量偵測演算法之研究

為了解決Security intelligenc的問題,作者林則勳 這樣論述:

隨著網際網路迅速發展,許多資訊竊取相關技術走向了網路攻擊,除了考驗駭客如何精進自己的攻擊技術,同時也考驗網路使用者在使用網路時如何保護自己的隱私資訊與設備。從20世紀末開始,殭屍網路之攻擊技術隨著網際網路的發展而變化,它由一組惡意程式碼所組成,並且透過修改參數來改變惡意行為,駭客可以藉此傳播惡意程式碼來感染網路使用者的設備做為傀儡,更能利用這些傀儡發動難以防禦的網路攻擊,如DDoS、Spam、Phishing…等多種攻擊方式,因此如何分類殭屍網路發出的惡意流量與正常流量是本論文研究的目標。在大部分的殭屍網路分類研究中,鮮少研究會考慮到殭屍網路訓練集中是否有雜訊 (Noise) 的問題,因此我

們研究了相關訓練集處理技術並思考如何用於殭屍網路分類任務中,以提升殭屍網路流量的分類性能。在本論文中,我們提出了一個基於過濾的集成模型,透過機器學習分類演算法來辨識訓練集中可能存在的雜訊並且進行過濾,從而使用乾淨的訓練集重新訓練模型來進行網路流量分類,除此之外,考慮到這些可能的雜訊也許是特殊的網路行為,我們善加利用這些雜訊進行另一種分類方法,嘗試提升整體網路流量的分類性能。

應用於大數據運算之虛擬化高效能電腦架構設計與研究

為了解決Security intelligenc的問題,作者許正成 這樣論述:

目前大數據分析處理運算盛行,因此高效能運算系統需求越來越迫切。然而建置高效能系統主機群,平台架構眾多,相對機房硬體所需基礎建置成本與能源消耗非常龐大。且若因作業任務需求增加時,加大硬體設備主機資源的方法,將造成基礎設備管理的困擾。如果將需求建置於商用公有雲端系統,所需的租賃費用與硬體資源不易掌控,尤其在中型的企業或組織,在資料安全與商業機密上,會審慎考量放置於公有雲上的風險。然而目前HPC(High Performance Computing)系統高效能的需求,因上述問題考慮建置於虛擬系統上,無從得知較佳的平台為何?且台灣大多為中小企業或組織,以需求低成本與高效能並存條件下,是否符合高速運行

的要求。因此本論文提出以INTEL X86-64 CPU為研究基礎,以具備高效能、多核心、單位成本低之優點,且為目前主流架構。分析現今主流虛擬化基礎平台,運行於Microsoft HPC Server與Microsoft R Server系統。選擇效能與管理最佳之虛擬化平台,並加入MSSQL2016/2017 In-Database R資料庫的應用分析,展現高速多CPU與多主機群的平行處理能力,也可依任務作業透過虛擬化系統基礎設備彈性增減CPU核心數或記憶體,達到更密集的處理與彈性的橫向擴充能力。論文針對此架構分析多套虛擬系統效能表現,並經調教與優化後測試數據結果顯示,VMware hyper

visor虛擬平台所建置的HPC系統的效能表現較優異,且具有系統核心資源使用少、效能高、相容性佳、管理容易等優點,符合高速運算的需求平台。較一般主機群建置之HPC架構更為精簡且成本低與管理容易,並可達到相同效能與低維護成本,符合目前環保需求之高彈性服務等優點,提供大數據處理分析需求的最佳選擇。