- General Chair:
- Ting Yu,
- Program Chair:
- Mihai Christodorescu
- Sponsor:
- sigops
It is my great pleasure to welcome you to the 2012 ACM Workshop on Building Analysis Datasets and Gathering Experience Returns for Security -- BADGERS'12. The BADGERS workshop is intended to encourage the development of Internet-scale security-related data collection and analysis initiatives. As such, the workshop is positioned at the confluence of computer security and general purpose large-scale data processing and aims at bringing together researchers, practitioners, system administrators, and security analysts active in the emerging domain of security-related data collection and analysis for Internet-scale computer systems and networks. By giving visibility to existing solutions, the workshop promotes and encourages the better sharing of data and knowledge. I expect that the increasing availability of tools and techniques to process large-scale data (aka "Big Data") will benefit computer security.
The call for papers attracted 7 submissions from Asia, Europe, and the United States, each of which received at least three reviews from the program committee. The 4 accepted papers cover a variety of topics, including Internet-wide intrusion detection and vulnerability analysis, probing using darknets, and residential privacy in location-based social networks. In addition, the program includes keynote speeches by Samuel Weber on challenges for Big Data privacy and security and by Peter Mell on Big Data technologies for security research, and two invited talks describing realworld experiences with Big Data in industrial research lab settings. I hope that these proceedings will serve as a valuable reference for security researchers and developers.
Proceeding Downloads
Big data privacy and security challenges
The ability to collect and organize large data sets has proven to be transformational: instead of just a linear improvement of older techniques, the ability to effectively process huge amounts of information creates radically new abilities and ...
Big data for security: challenges, opportunities, and examples
This is the age of big data. Enterprises collect large amounts of data about their operations and analyze the data to improve all aspects of their businesses. Big data for security, i.e., the analysis of very large enterprise data sets to identify ...
Examining intrusion prevention system events from worldwide networks
We report preliminary results on analyzing a large dataset of over 35 billion alerts recorded over a 5 year period by Hewlett-Packard (HP) TippingPoint Intrusion Prevention System (IPS) devices located in over 1,000 customer networks worldwide. This ...
Analysis of internet-wide probing using darknets
Recent analysis of traffic reaching the UCSD Network Telescope (a /8 darknet) revealed a sophisticated botnet scanning event that covertly scanned the entire IPv4 space in about 12 days. We only serendipitously discovered this event while studying a ...
A preliminary analysis of vulnerability scores for attacks in wild: the ekits and sym datasets
NVD and Exploit-DB are the de facto standard databases used for research on vulnerabilities, and the CVSS score is the standard measure for risk. On open question is whether such databases and scores are actually representative of attacks found in the ...
Towards understanding residential privacy by analyzing users' activities in foursquare
Location-based social network systems (LBSNSs) are becoming increasingly popular. At the same time, privacy of users' information in these systems is becoming a huge concern. In LBSNSs, such as Foursquare, users' residential addresses and their check-...
Tracking dynamic network properties at internet-scale
Many network properties such as security and performance vary considerably in different parts of the Internet, e.g., many measurement studies have shown that malicious activity is more likely to originate from certain regions of the Internet. Such ...
- Proceedings of the 2012 ACM Workshop on Building analysis datasets and gathering experience returns for security
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| BADGERS '12 | 7 | 4 | 57% |
| Overall | 7 | 4 | 57% |