In early June, the Australian Internet Industry Association, an ISP industry trade group, published icode [PDF], a voluntary code of conduct for ISPs to follow to better fight bots on their networks. Like the previously-mentioned IETF draft, this document lays out a rationale for, and recommendations on how to implement, an ISP-level response to bots. Unlike the IETF draft, icode is a reflection of a coordinated effort by a large number of ISPs to buy in to a common framework for how to respond.
The icode framework has four parts:
- Education. ISPs that adopt icode are expected to educate their customers about keeping their computers from becoming compromised.
- Detection. ISPs can implement their own detection methods and/or get data from trusted third parties. Even better, they can get data from the Australian Internet Security Initiative, a government-led effort to centralize bot reporting by collecting bot reports from trusted providers and then distributing ISP-specific data daily to participating ISPs. (Wouldn’t it be great if we had something like this for infected URLs and hosting companies?)
- Action. ISPs are encouraged to act on the information about bots, through whatever combination of customer notification, password resets, bandwidth throttling, walled garden quarantining, smtp blocking, or other measures they consider appropriate.
- Reporting. ISPs are expected to report “significant cyber security incidents” to governments.
icode also recommends, though doesn’t require, that participating ISPs share threat data with each other, facilitated by the Australian CERT.
One could quibble over some of the details, but it’s clear that the Australian ISPs that created and will be adopting icode are light years ahead of most ISPs (and web hosting providers) globally in tackling the spread of malware.
We have generally seen an increase in the number of badware URLs reported by our data providers lately, but in the past few weeks, we’ve seen unusually big spikes on three autonomous systems (simplifying slightly, an AS is a set of networks operated by a single entity):
AS16276 (OVH) graph
AS9809 (Nova Network, China) graph
AS22489 (Castle Access) graph
We have attempted to notify all three network operators via abuse@domain_name. The report to abuse@nova.net.cn bounced, so if you know another contact address there, please let us know.
Most likely, these spikes in infection numbers are the result of either targeted attacks at these networks or opportunistic attacks that happened to find their way into large numbers of identically configured (or misconfigured) web servers.
StopBadware is pleased to announce that Paul Mockapetris will join our
board of directors.
Mockapetris created the Domain Name System (DNS), an essential part of
today’s Internet infrastructure, in the 1980s. He is now the Chief
Scientist and Chairman of the Board at Nominum, a global provider of DNS
and DHCP solutions to communication providers. He has previously served
as chair of the Internet Engineering Task Force (IETF) and as a program
manager at the Advanced Research Projects Agency (ARPA).
The board, chaired by PayPal CISO Michael Barrett, also includes Vint
Cerf, Esther Dyson, John Palfrey, Ari Schwartz, Mike Shaver, and
executive director Maxim Weinstein.
Press release: http://www.stopbadware.org/home/pr_06112010
Earlier this week I sat in on the Workshop on the Economics of Information Security. One of the more lively research papers presented was on insecurities in the online pornography industry. The paper 0 has also been written about by Threatpost 1. As noted by Naraine’s article the team crawled just over 35,000 websites using an automated system. Interestingly the team discovered that about 3.23% of those sites were also infected with drive by downloads. One aspect of the research I was curious about was the degree to which those infected porn sites were popular. I spoke with Dr Wondracek after his talk to speak about the possibility of figuring this out. In my own thesis last semester I discovered that of the sampled sites we receive from our data partners less than 3% of the those were listed as popular by Alexa.
To determine this one simply downloads Alexa’s “Top 1,000,000 Websites” list 2 and formats the list for comparison appropriately. (Alexa’s list uses canonical hostnames) Then simply take the intersection of that list (find which hostnames appear on list A and list B) and use that to create a percentage. This statistic should answer Pr(Popularity|Infection) or the probability of popularity given an infection.
[edit: moved links to bottom in footnote format for better readability]
0 http://weis2010.econinfosec.org/papers/session2/weis2010_wondracek.pdf
1 http://threatpost.com/en_us/blogs/understanding-porn-malware-connections-060810
2 http://s3.amazonaws.com/alexa-static/top-1m.csv.zip
