Difference between revisions of "Group01 Report"
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− | + | The app in its current iteration is not designed for real-time monitoring. Future work would include adapting the code to ingest real-time data and create a loop to refresh the analysis periodically. The time taken to refresh the analysis would be shorter than the interval in which network traffic is analysed for suspicious activity. The app could be deployed within Big Data Architecture that use Apache Spark for analysis, which is a common solution, as the Spark engine comes with APIs for R. In fact, with enough data points, the dashboard could even be expanded to include a predictive module that anticipates where and when the next cyber-attack will take place.<br><br> | |
+ | The interactivity of the app could also be enhanced. More control elements could be implemented for users to perform their own exploration of the data. For example, the current Sankey visualisation only allows users to examine the connections by source country e.g. Iran but does not allow users to specify specific timings to inspect.<br><br> | ||
+ | While the Sankey and Network visualisations currently perform specific and distinct functions within the app, they could potentially overlap in terms of the type of information that can be conveyed to users. Hence future work would include tweaking the coding to see if either one could be omitted for an even simpler App interface. | ||
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This project attempts to tackle the complexity of cybersecurity and visualise suspicious attacks that are highly likely to be actual attacks in a meaningful and intuitive manner. That is not an easy task given that cyber-attacks can take place at any time, from anywhere, at any intensity (e.g. number of connections) and in many different forms. Hence tools to aid cybersecurity experts in detecting and defending against cyber-attacks need to continually be refined and upgraded. This project is a first step in that direction. | This project attempts to tackle the complexity of cybersecurity and visualise suspicious attacks that are highly likely to be actual attacks in a meaningful and intuitive manner. That is not an easy task given that cyber-attacks can take place at any time, from anywhere, at any intensity (e.g. number of connections) and in many different forms. Hence tools to aid cybersecurity experts in detecting and defending against cyber-attacks need to continually be refined and upgraded. This project is a first step in that direction. | ||
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==References== | ==References== | ||
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[1] Shneiderman, B. (2005) “The eyes have it: A task by data type taxonomy for information visualization” IEEE Conference on Visual Languages (VL96), pp. 336-343<br><br> | [1] Shneiderman, B. (2005) “The eyes have it: A task by data type taxonomy for information visualization” IEEE Conference on Visual Languages (VL96), pp. 336-343<br><br> | ||
[2] About BP: https://en.wikipedia.org/wiki/BP<br><br> | [2] About BP: https://en.wikipedia.org/wiki/BP<br><br> | ||
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[6] https://github.com/timelyportfolio/sunburstR<br><br> | [6] https://github.com/timelyportfolio/sunburstR<br><br> | ||
[7] R Packages Description: https://cran.r-project.org<br><br> | [7] R Packages Description: https://cran.r-project.org<br><br> | ||
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Revision as of 15:04, 13 August 2018
LINK TO PROJECT GROUPS:
Please Click Here -> [1]
Cybersecurity
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Contents
Introduction
PUT YOUR CONTENT HERE |
Objective and Motivations
PUT YOUR CONTENT HERE |
Previous Works
PUT YOUR CONTENT HERE |
Dataset and Data Preparation
PUT YOUR CONTENT HERE |
Design Framework and Visualization Methodologies
PUT YOUR CONTENT HERE |
Insights and Implications
PUT YOUR CONTENT HERE |
Limitation and Future Work
The app in its current iteration is not designed for real-time monitoring. Future work would include adapting the code to ingest real-time data and create a loop to refresh the analysis periodically. The time taken to refresh the analysis would be shorter than the interval in which network traffic is analysed for suspicious activity. The app could be deployed within Big Data Architecture that use Apache Spark for analysis, which is a common solution, as the Spark engine comes with APIs for R. In fact, with enough data points, the dashboard could even be expanded to include a predictive module that anticipates where and when the next cyber-attack will take place. |
Conclusion
This project attempts to tackle the complexity of cybersecurity and visualise suspicious attacks that are highly likely to be actual attacks in a meaningful and intuitive manner. That is not an easy task given that cyber-attacks can take place at any time, from anywhere, at any intensity (e.g. number of connections) and in many different forms. Hence tools to aid cybersecurity experts in detecting and defending against cyber-attacks need to continually be refined and upgraded. This project is a first step in that direction. |
References
[1] Shneiderman, B. (2005) “The eyes have it: A task by data type taxonomy for information visualization” IEEE Conference on Visual Languages (VL96), pp. 336-343 |