SAINS DATA, BIG DATA, DAN ANALISIS PREDIKTIF: SEBUAH LANDASAN UNTUK KECERDASAN KEAMANAN SIBER
DOI:
https://doi.org/10.33172/jpbh.v7i2.187Abstract
Abstrak – Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal.
Kata Kunci: analisis prediktif, big data, intelijen, keamanan siber, sains data
Abstract – Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data.
Keywords: big data, cyber security, data science, intelligence, predictive analytics
References
References
Books
P. Zikopoulos, & C. Eaton, “Understanding big data: Analytics for enterprise class hadoop and streaming dataâ€, McGraw-Hill Osborne Media, 2011.
R. Schutt, and C. O’Neil, “Doing data science: Straight talk from the frontlineâ€, O’Reilly Media, Inc.â€, 2013.
Journals
C. P. Chen & C. Y. Zhang. 2014. “Data-intensive applications, challenges, techniques and technologies: A survey on Big Dataâ€, Information Sciences Vol.275.
L Xu, C. Jiang, J. Wang, J. Yuan, & Y. Ren. 2014. “Information security in big data: privacy and data miningâ€. IEEE Access. 2.
Vashant Dhar. 2013. “Data science and predictionâ€, Communications of the ACM, Vol. 56. No.12.
Wu, X., X. Zhu, X., G. Q. Wu, and W. Ding. 2014. “Data Mining With Big Data.†IEEE Transactions On Knowledge And Data Engineering. Vol. 26. No.1.
Publications
Anonymous. 2013. “Big Data Analytics in Cyber Defenseâ€. Ponemon Institute Research Report.
Curry, S, E Kirda, E Schwartz, WH Stewart, and A Yoran. 2013. A. Big data fuels intelligence-driven security. RSA Security Brief Report.
Cackett, Doug. 2013. â€Information Management and Big data: A Reference Architectureâ€, Oracle: Redwood City, CA, USA.
Falessi, N, R Gavrila, MR Klejnstrup, K Moulinos. 2012. “National cyber security strategies: practical guide on development and executionâ€. European Network and Information Security Agency (ENISA ) Publication.
Manyika, J, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, & A. H. Byers. 2011. “Big data: The next frontier for innovation, competition, and productivityâ€. McKinsey Publication.
Siewert, S. B. 2013. “Big data in the cloud: Data velocity, volume, variety, veracityâ€. IBM Developerworks.
Truvé, Staffan. 2017. â€Machine Learning in Cyber Security: Age of the Centaursâ€. Threat Intelligence Whitepaper, Recorded Future.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. 1996. “From data mining to knowledge discovery in databases†AI magazine.
U. Ganugula, & A. Saxena. 2012. “High Performance Cryptography: Need of the Hourâ€. CSI Communications Magazine.
Conferences
Agrawal, R, A. Imran, C. Seay, & J. Walker. 2014. “A layer based architecture for provenance in big dataâ€, In Big Data (Big Data), 2014 IEEE International Conference on Big Data.
Alguliyev, R. & Y. Imamverdiyev. 2014. “Big data: big promises for information security, In Application of Information and Communication Technologies (AICT), IEEE 8th International Conference on Oct 15.
Hayashi, C, K. Yajima, H. Bock, N, Ohsumi, Y, Tanaka, and Y, Baba, (Eds.). 2013. “Data Science, Classification, and Related Methodsâ€. Proceedings of the Fifth Conference of the International Federation of Classification Societies (IFCS-96), Kobe, Japan, March 27–30, 1996, Springer Science & Business Media,
Websites
Imanuel,â€What Is Predictive Analytics ?†Predictive Analytics Today2017, http://www.predictiveanalyticstoday.com/what-is-predictive-analytics, retrieved June 6, 2017.
Kim, D, ì œ3íšŒì‚¬ë‚´ê¸°ìˆ ì„¸ë¯¸ë‚˜-hadoop (ë°°í¬ìš©)-dh kim-2014-10-1, https://www.slideshare.net/jihanie/3-hadoopdh-kim2014101, retrieved May 28, 2017.
Lockwood, G, “Conceptual Overview of Map-Reduce and Hadoopâ€, 2015, http://www.glennklockwood.com/data-intensive/hadoop/overview.html, retrieved June 5, 2017.
Press, G, “A very short history of big dataâ€, Forbes, 2013, https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/, retrieved 20 May 2017.
Solanki, A. “Big data–Back to the Basicsâ€, 2013, http://trinityordestiny.blogspot.co.id/2013/07/big-databack-to-basics.html, retrieved June 8, 2017.
Solnik,R, “The Time Has Come: Analytics Delivers for IT Operationsâ€, 2013, http://www.datacenterjournal.com/time-analytics-delivers-operations/, retrieved June 3, 2017.
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