77968 - Big Data and Cybersecurity

Academic Year 2015/2016

Learning outcomes

By the end of the course, studemts will be alble to: 1) Understand the foundations of big data, including it’s foundations in computing technology and statistics. 2) Understand the social implications of increased knowledge, surveillance, and behavioral prediction made possible by big data, and the ethical tradeoffs faced. 3) Demonstrate the ability to formulate specific study questions concerning cybersecurity. 4) Understand accepted tools and practices concerning cyberterrorism and cyberwarfare. 5) Demonstrate the ability to communicate complex concepts to multidisciplinary teams including students from computing and international affairs backgrounds.

 

Course contents

The course is organized in two parts. The first secion aims to introducing the basic concpets and methodological challenges connected with the availability of Big Data. In addition, in this first theoretical part, key issues in cybersecurity, social engeneering and business intelligence will be addressed. The second part of the course will be devoted to the study of methods, techniques and tools for big data analyses. In particular, basic elements of data mining (descriptive statistics, analysis of relations and dimension reduction techniques) will be discussed. Finally, students will be introduced, trough examples, to the use of KNIME: a software for Big Data analysis.

Readings/Bibliography

- Altshuler Y. et al. (2013) “Detecting Anomalous behaviours using structural properties of social networks” in Greenberg A.M., Kennedy, W.G. & Bos N.D. (eds.) Social computing, behavioural-cultural modeling and prediction. Berlin: Springer. Pp. 433-440.

- Arun Vishwanath, et al. (2011) “Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model”, Decision Support Systems, 51, pp. 576–586 doi:10.1016/j.dss.2011.03.002.

- Barfar A. & Padmanabhan B. (2015) “Does television viewership predict presidential election outcomes?”, in Big Data, 3(3): 138-147.

- Berthold M.R., Borgelt C., Hoeppner F., Klawonn F. (2010) Guide to intelligent data analysis. Springer.

- Bogomolov A. et al. (2015) “Moves on the street: classifying crome hotspots using agrgegated anonymized data on people dynamics”, in Big Data, 3(3): 148-158.

- Boyd D. and Crawford K. (2012) “Critical questions for big data”. Information, Communication & Society, 15(5): 662-679.

- de Brujine, M. and van Eeten, M. (2007), ‘Systems That Should Have Failed: Critical Infrastructure Protection in an Institutionally Fragmented Environment’, Journal of Contingencies and Crisis Managment, 15(1), pp. 18-29.

- Burrows, R. & Savage, M. (2014) “After the crisis? Big data and the methodological challenges of empirical sociology”, Big Data & Society, April-June: 1-6.

- Cardullo P. 2015) “’Haking multitude’ and big data: Some insights from the Turkish digital coup”, Big Data & Society. Jan-June: 1-24.

-Carin, L., Cybenko, G., & Hughes, J. (2008) “Cybersecurity Strategies: The QUERIES Methodology”, Computer, 41(8), 20-26.

- Cohen, J., et al. (2009), “MAD Skills: New Analysis Practices for Big Data”, Proceedings of the VLDB Endowment, 2(2), pp.1481-1492.

- Croft, C. (2014) “The Limits of Big Data”, The SAIS Review of International Affairs, 34(1), 117-120, available from <http://search.proquest.com/docview/1552151758?accountid=9652>.

- Finney, M. N. K. (2014) “Cybersecurity and Cyberwar: What Everyone Needs to Know”, Parameters, 44(3), 149-150.

- de Fortuny E.J., Martens D. & Provost F. (2013) “Predictive modelling with big data: Is bigger really better?”, in Big Data, 1(4): 215-226.

- Giacomello, G. (2004) “Bangs for the Buck: A Cost-Benefit Analysis of Cyberterrorism”, Studies in Conflict and Terrorism, 27(5): 195—212.

- Giacomello G. (2014) “Introduction: Security in Cyberspace”, in G. Giacomello (ed.) Security in Cyberspace: Targeting Nations, Infrastructures, Individuals, New York: Bloomsbury, pp. 1-19.

- Gold, S., (2009) “The SCADA Challenge: Securing Critical Infrastructure”, Network Security, 2009(8), pp.18-20.

- He, W., et al. (2015) “Gaining Competitive Intelligence from Social Media Data”, Industrial Management & Data Systems, 115(9), 1622-1636, available from <http://search.proquest.com/docview/1748939460?accountid=9652>.

- Leetaru, K., Wang, S., Cao, G., Padmanabhan, A. and Shook, E., (2013) “Mapping the Global Twitter Heartbeat: The Geography of Twitter”, First Monday, 18(5), available from <www.firstmonday.org>.

- Leventhal B. (2010) “An introduction to data mining and other techniques for advanced analytics”, Journal of Direct, Data and Digital Marketing Practice, 12(2): 137-153.

- Kitchin R. (2014) “Big data, new epistemologies and paradigm shifts”, Big Data & Society, April-June: 1-12.

- Magdy, W., K. Darwish & I. Weber (2016) “#FailedRevolutions: Using Twitter to study the antecedents of ISIS support”, First Monday,January, available from <http://firstmonday.org/ojs/index.php/fm/article/view/6372>, doi:10.5210/fm.v21i2.6372.

- Mayer-Schonberger, V. & Cukier, K. (2013) Big Data: A revolution that will transform how we live, work and think". Boston: Houghton Mifflin Harcourt.

- Pentland A (2012) Reinventing society in the wake of big data. Edge, 30 August 2012. Available at: https://edge.org/conversation/alex_sandy_pentland-reinventing-society-in-the-wake-of-big-data

- Rutherford, A. et al. (2013) “Targeted social mobilization in a global manhunt”, PLOSone. Doi: 10.1371/journal.pone.0074628

-Tehan, R. (2013, October). Cybersecurity: Authoritative Reports and Resources. LIBRARY OF CONGRESS WASHINGTON DC CONGRESSIONAL RESEARCH SERVICE, available from <http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA588715>

-Ten, C. W., Liu, C. C., & Manimaran, G. (2008) “Vulnerability Assessment of Cybersecurity for SCADA systems”, Power Systems, IEEE Transactions on, 23(4), 1836-1846.

- Tene, O. and Polonetsky, J., (2012) “Big Data for All: Privacy and User Control in the Age of Analytics”, Nw. J. Tech. & Intell. Prop., 11, p.xxvii, available from <http://scholarlycommons.law.northwestern.edu/cgi/viewcontent.cgi?article=1191&context=njtip>

- Vie L. L. et al. (2015) “The U.S. Army Person-Event data environment: A military-civilian Big Data enterprise”, Big Data, 3(2): 67-79.

- Wang, H., & Wang, S. (2008) “A Knowledge Management Approach to Data Mining Process for Business Intelligence”, Industrial Management & Data Systems, 108(5), 622-634. doi:http://dx.doi.org/10.1108/02635570810876750

- Zhu, B., Joseph, A. and Sastry, S., (2011), ‘A Taxonomy of Cyber Attacks on SCADA Systems, in Internet of things (iThings/CPSCom), 2011 international conference on and 4th international conference on cyber, physical and social computing, October, pp. 380-388. IEEE.

-Zwitter, A. (2015) “Big Data and International Relations”, Ethics & International Affairs, 29(4), 377-389 doi:http://dx.doi.org/10.1017/S0892679415000362.

Additional references and readings will be communicated during the classes. 

Teaching methods

Lectures, lab exercises.

Assessment methods

For students attending at least 80% of class

60% final written exam (in English)

40% final paper on a topic to be agreed with the instructors due within 8 weeks after passing the written exam.

For students who fail to attend at least 80% of class

Please contact the instructors by e.mail.

Teaching tools

This course is also supported by a dedicated e-learning module available at https://elearning-cds.unibo.it/

Office hours

See the website of Marco Albertini

See the website of Giampiero Giacomello