SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2017 can be found here.
Students pursuing opportunities at JPL must be U.S. citizens or U.S. permanent residents.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.
Announcements for external summer programs are listed here.
|Project:||Machine Learning for Cybersecurity: Botnet Detection|
|Disciplines:||Computer Science, EE|
|Mentor:||Julian Bunn, Principal Computational Scientist, (PMA), Julian.Bunn@caltech.edu|
|Mentor URL:||http://pcbunn.cd3.caltech.edu/jjb.html (opens in new window)|
|Background:||Botnets are malware infections of computers in a network. They carry out tasks under remote control - typically they are used to send spam, or mine Bitcoins, mount Distributed Denial of Service attacks, or extract valuable and personal data from the host machines and send it out over the network. New types of Botnet are continually being discovered in the wild. Detecting, mitigating, and effectively destroying Botnets is one of the biggest current challenges in Cybersecurity. In this project, we will investigate how machine learning tools may be used to detect the presence of different types of Botnet activity in a network of computers.|
|Description:||The project will make use of large datasets comprising network measurements made before and during the activity of several different types of Botnet. The main project goal will be to develop software to identify global patterns in the exchange of packets between computers that indicate anomalous activity, and trigger an alert. This will likely involve working in the graph space of the data, and using machine learning techniques such as clustering or neural nets. The pattern alerts that are developed in the project will need to optimize the True Positive Rate, at a False Positive Rate as low as possible, when applied to the different types of Botnet data.|
Overview of Botnets: https://us.norton.com/botnet/
Project host department: http://cd3.caltech.edu/
Example datasets: http://mcfp.weebly.com/
|Student Requirements:||This project will probably most interest CS students or those students having well honed computing skills. Must be an undergraduate level student.|
This AO can be done under the following programs:
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