Phishing attacks use social engineering in an attempt to lure victims to fake websites. The websites could allow the attacker to retrieve sensitive or private information such as usernames, passwords, and credit card details. Attacks of this kind have been around since 1995, evolving in sophistication in order to increase their success rate. Up until now, phishing attacks were generally viewed as isolated events that were dealt with on a case-by-case basis. The dawn of big data analysis in computer security allows us to store data indefinitely and watch the changes and growth of attacks over long periods of time. In 2012, we began tracking a sophisticated phishing campaign that is still going strong.
Google, one of the largest players in the cloud business, offers dozens of free cloud services: Google Email, Google Drive, Google Docs, Google Analytics, YouTube, etc. To enable easy access across all of these properties, Google built what they call, “One account. All of Google.” Read More »
Tags: anti-spam, Google, identity theft, phishing, scam, spam, spear phishing, threat intelligence, TRAC, TRAC Big Data Analysis
It is not uncommon to see an anti-spam system catch >99% of the spam passing through it. Most of the best anti-spam systems catch >99.9% of spam. In this environment, spammers try just about anything to evade spam filters. Some spammers believe that blasting at high volume is the key to success. Others believe complete randomization of the message headers will confuse the anti-spam system. Still others take a minimalist approach, sending only a URL in the body. As anti-spam systems close gaps in their coverage, spammers are forced to find new tricks (or resort to variations on old tricks). It’s an arms race.
One spam technique in particular is attracting more and more spammers. This technique is known in the email industry as “snowshoe” spam. Snowshoes are footwear that allows a person to walk over deep snow by distributing their weight over a larger surface area, thus preventing the wearer’s foot from sinking. But what do snowshoes have to do with unsolicited bulk email? In the email world “snowshoe” spam is unsolicited bulk email that is sent using a large number of IP addresses, and at a low message volume per IP address.
Cisco’s worldwide sensor network records details about a substantial quantity of spam. We analyze this large dataset for trends among senders. Below is a breakdown of spam by sender type. Note that the volume of snowshoe spam has more than doubled since November 2013.
Spam broken down by Sender Type
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Tags: spam, SpamCop, spamtrap, TRAC
April kicked off with a 1:292 rate of malware encounters and closed with a rate of 1:315. Highest peak day was April 20 when the rate reached 1:177. Lowest was April 4 at 1:338. The median rate of web malware encounters in April 2014 was 1:292, representing a slight improvement over the median of 1:260 requests in March but still worse than the median of 1:341 requests in February.
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Tags: malware, metrics, spam, TRAC
Takedowns of prolific spam botnets, such as Rustock in 2011 and Grum in 2012, had a substantial effect on reducing overall global spam volumes. This, combined with diminishing returns for spammers sending via bots, had left many email recipients basking in the comfort of (mostly) clean inboxes. No doubt this downward trend in global spam volumes also saved countless dollars that would have otherwise been frittered away on phony university degrees, suspect weight loss products, and erectile dysfunction medication.
Unfortunately, however, the good times seem to be coming to an end. Spam volumes have increased to the point that spam is now at its highest level since late 2010. Below is the graph of global spam volume as reported by Cisco SenderBase. From June 2013 to January 2014, spam was averaging between 50-100 billion messages per month, but as of March 2014 volumes were peaking above 200 billion messages per month–more than a 2X increase above normal.
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Tags: CBL, SenderBase, spam
On January 16, 2014, Proofpoint discussed a spam attack conducted via “smart devices which have been compromised.” Among the devices cited by Proofpoint as participating in the “Thingbot” were routers, set-top boxes, game consoles, and purportedly, even one refrigerator. Of course, news about a refrigerator sending spam generates considerable media attention, as it should, since an attack by the Internet of Things (IoT) would represent a high-water mark in the evolution of (in)security on the Internet. However, soon after Proofpoint’s post, Symantec published a response indicating that IoT devices were not responsible for the spam attack in question, and the machines behind the spam attack were all really just infected Windows boxes. So why is determining the identify of the devices used in this spam attack so difficult?
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Tags: IoT, spam, TRAC