The goal of this class is to expose you to computer security research, and the best way to learn about computer security research is to do it. In this class, you will undertake your own research project, which you will present at the end of the course.
We prefer group of two people. You may come up with your own project or you may choose from one of the project ideas below.
- Jan 16: Form project groups.
- Feb 4: Submit project proposal (1 page max).
- Mar 22 (9 am): Preset project (15 mins) in room 4109.
- Mar 24 (9 pm): Submit Project report (6 pages max).
Your project proposal should describe what you plan to do, why it is interesting, how you plan to do it, and what you are not sure about. Also describe what resources you think you will need to carry out the project. The proposal should be no longer than one page.
Your report should be written like a research paper on par with the papers read in class. The report should be no longer than 6 pages.
- Sound and Vision
- Automated extraction of latent fingerprints (e.g. via UV photography)
- Video-conferencing filter that obscures identity but looks natural (autotune for faces)
- Identifying individuals in crowd scenes via non-traditional cues (e.g. bad of the head recognition)
- Watermarking 3D models
- Meaningful visualization of security data (e.g., spam, net, etc.)
- Seeing through privacy glass or 3M privacy screens?
- Fingerprint capture at a distance via live video
- Automated mapping of spy satellites
- Skype keystroke capture (side channel) and cancellation (privacy protection)
- How well can you reconstruct Web logs from Netflow data?
- Study of exploitable PII exposure on flickr, myspace, etc (e.g. credit cards, check routing numbers, SSNs, name/address)
- Check which sites you’ve visited recently via DNS lookup timing (was entry in cache?)
- Build a Flash “scrubber” that integrates with Tor to prevent flash-based information leakage
- Privacy analysis of Chatroulette (i.e., can you tell who these people are?)
- Tie FAA flight database with network log data to infer which users are travelling and where they came from
- What does your TiVO box know about you?
- Malware phylogeny: Fingerprint malware “family trees” based on the data structures they use, i.e., to determine which variants derive from which earlier specimens
- Build system to identify the kinds of information being targeted by different kinds of malware
- Evaluate malware delivery vectors: P2P malware vs web sites vs attachments, etc ... are they all carrying the same malware or different?
- Evaluate time-to-detect for commercial malware
- Build IDA plug-in to locate particular “kind” of code in binary (e.g., AES code, CRC code, etc.)
- Spam and E-Crime
- How serious is the problem of blog spam? What might be done to detect/characterize it?
- Automatically extract pricing data from UCSD Trajectory database to track price of spam-advertised goods
- Use NLP to track good/service pricing on underground forums/IRC
- Relate use of domain names in various scams to price of domain offered by registrar
- Machine Learning
- Predict which code changes will produce software vulnerabilities
- Repeat Ma’s on-line URL classification study using Web page content
- Build classifier to predict machine compromise based on what sites you visit
- Apply receiver-reputation idea to Web visits (reputation of sites depends on who visits them)
- Can we predict which Torrents are malicious?
- Classify spam senders (which program used)
- Clustering of malware families based on behavioral features
- Detection of Bots in MMPORGs
- Automation for “attack surface” estimation
- Analysis of Taser authentication
- Fuzz testing of embedded devices (e.g. Cameras)
- Location verification via “audio-print” (indeed, any way of proving location)
- Analysis of on-line poker (fair deal or not?)
- AppEngine Cartography (repeat our Amazon study on AppEngine)
- Automated safe cracker
- Use new Intel security features for something interesting (e.g., NotABot)
- Hardware support for self-destructing data
- Hardware support for information flow tracking
- Detecting pirated hardware IP (e.g., mp3 or PCI blocks) via unique side-effects
- How does a cloud provider prove that they’ve replicated your data?
- How hard to reliably erase flash storage?
- Security analysis of campus power grid
- Security vulnerabilities the Kindle?
- Repeat Ozment/Schechter’s Milk/Wine study on vulnerability generation w/another system
- Evaluate how people respond to different kinds of warning messages
- Explore use of ABE for real systems problem
- Difficulty in spoofing consumer GPS
- Are there vulnerabilities in Digital FM radio?
- Identify “anomalous” file contents to mitigate file format vulnerabilities (esp Flash, QuickTime and PDF)
- Attacks against smart batteries (drain beyond ability to recharge or make explode)
- Driver detection (infer identity of driver via driving behavior)
- Explore use of differential privacy to protect data for interesting network or security trace analysis problem (e.g., pick any of George Varghese’s recent papers and see if it can be done with DP)
- Build an interactive biometric system (e.g., proof of presence via eye-tracking) to prevent simple replay attacks
- Design a CAPTCHA that is difficult to outsource to low minimum-wage solvers
- Build a system to fingerprint physical luxury goods (e.g., leather, etc) ala the Princeton work on paper
- SMART disks will move data from failing sectors to spare sectors. Consequently the data on these failing sectors may not be erased when the associated data is erased. Explore if this actually happens and the correct way to erase a disk
- Analyze data breach incidents (sources: datalossdb.org, CA DoJ, Privacy Rights Clearinghouse)