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March 2014 Threat Metrics

The median rate of web malware encounters in March 2014 was 1:260, compared to a median rate of 1:341 requests in February. At least some of this increased risk appears to have been a result of interest in the NCAA tournaments (aka March Madness), which kicked off during the second week of March in the United States.

Mar2014rate

In February 2014, web malware encounters from sports and video sites were in the 18 and 28 spot, respectively. During March 2014, web malware from sports- and video-related sites jumped to the number 7 and 8 spots, respectively. The presumed longer time spent viewing sports-related content may have been a factor in a 1% decrease in the total volume of web requests in March coupled with a corresponding 18% increase in terabytes received.

Mar2014catall

The ratio of unique non-malicious hosts to unique malware hosts decreased by 1%, at 1:4841 in March 2014 compared to 1:4775 in February. The ratio of unique non-malicious IP addresses to malicious unique IP addresses also dropped from 1:1351 in February 2014 to 1:1388 in March. There was also far less volatility in the rate of unique malicious IP addresses throughout March compared to February.

Mar2014hosts

Java encounters dropped from 9% of all web malware encounters in February 2014 to 6% in March. At 43% of all Java encounters, Java version 7 exploits were the most frequently encountered, with 26% targeting Java version 6, and 32% targeting other versions of Java.

 

Mar2014java

Web malware encounters from mobile devices decreased 24% from February to March 2014. In March 3.6% of all Web malware encounters resulted from mobile device browsing, compared to 4.7% in February. Conversely, web malware encounters from non-Android and non-iOS devices doubled for the period, from 0.1% in February to 0.2% in March. The cause of this increase was not due to any specific device, but rather an across-the-board increase affecting all non-Android and non-iOS devices.

Mar2014mobile

At 18%, advertising was the most common vector of mobile device encounters, followed by business-related sites at 13% and video-related sites at 11% of mobile device encounters. For comparison purposes, in February 2014, sites in the business category were the most common vector of mobile device encounters (20%), followed by advertising (13%) and personal sites (8%). Video came in fourth in February, at 7%.

Mar2014catmob

Pharmaceutical & Chemical remained at 1100% of median risk for web malware encounters in March 2014, the same rate experienced in February. Companies in the Entertainment vertical experienced an increase from 321% in February to 643% in March. The Energy, Oil & Gas vertical increased from a rate of 276% in February to 397% in March.

To assess vertical risk, we first calculate the median encounter rate for all enterprises, and then calculate the median encounter rate for all enterprises in a particular vertical, then compare the two. A rate higher than 100% is considered an increased risk.

 

Mar2014vert

Following a 73% increase from January to February, spam volumes increased another 45% in March to an average of 207 billion spam messages per day.

Mar2014spamvol

The top five global spam senders in February 2014 were the United States at 8%, followed by the Republic of Korea at 5%, Russian Federation at 3%, China at 2%, and Ukraine at 1%.

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February 2014 Threat Metrics

Web surfers in February 2014 experienced a median malware encounter rate of 1:341 requests, compared to a January 2014 median encounter rate of 1:375. This represents a 10% increase in risk of encountering web-delivered malware during the second month of the year. February 8, 9, and 16 were the highest risk days overall, at 1:244, 1:261, and 1:269, respectively. Interestingly, though perhaps not unexpectedly, web surfers were 77% more likely to encounter Facebook scams on the weekend compared to weekdays. 18% of all web malware encounters in February 2014 were for Facebook related scams.

Feb2014Rate

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Coordinated Website Compromise Campaigns Continue to Plague Internet

This post is co-authored with Levi Gundert and Andrew Tsonchev.

Update 2014-03-21: For clarity, the old kernel is a common indicator on the compromised hosts. We are still investigating the vulnerability, and do not yet know what the initial vector is, only that the compromised hosts are similarly ‘old’.

Update 2014-03-22: This post’s focus relates to a malicious redirection campaign driven by unauthorized access to thousands of websites. The observation of affected hosts running Linux kernel 2.6 is anecdotal and in no way reflects a universal condition among all of the compromised websites. Accordingly, we have adjusted the title for clarity. We have not identified the initial exploit vector for the stage zero URIs. It was not our intention to conflate our anecdotal observations with the technical facts provided in the listed URIs or other demonstrable data, and the below strike through annotations reflect that. We also want to thank the community for the timely feedback.

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TRAC has recently observed a large malicious web redirect campaign affecting hundreds of websites. Attackers compromised legitimate websites, inserting JavaScript that redirects visitors to other compromised websites. All of the affected web servers that we have examined use the Linux 2.6 kernel. Many of the affected servers are using Linux kernel versions first released in 2007 or earlier. It is possible that attackers have identified a vulnerability on the platform and have been able to take advantage of the fact that these are older systems that may not be continuously patched by administrators.
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Understanding Security Through Probability

TRAC-tank-vertical_logoThis post was also authored by Min-yi Shen and Martin Lee.

Security is all about probability. There is a certain probability that something bad will happen to your networks or your systems over the next 24 hours. Hoping that nothing bad will happen is unlikely to change that probability. Investing in security solutions will probably reduce the chance of something bad happening, but by how much? And where should resources be most profitably directed?

Cyber security is a complex environment with many unknowns and interdependencies. TRAC data scientists research this environment to try and understand how different variables affect security. Bayesian graph models are one of our most useful tools for understanding probabilities in security and to explore how the likelihood of outcomes can be changed. Read More »

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January 2014 Threat Metrics

January 2014 started with a bang, with one in every 191 web requests resulting in a web malware encounter. The Cisco Computer Security Incident Response Team (CSIRT) observed this same trend, witnessing a 200% increase in web malware encounters experienced by Cisco employees for the month. Overall, January 1, 25, and 26 were the highest risk days for encountering web delivered malware. In the chart below, the lower the number, the higher the risk of encounters. Still, with a median encounter rate of 1:375 requests, every day of January 2014 represented significant risk for web browsing.

Jan2014Rate

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