Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Cloud

1/24/2017
04:00 PM
Connect Directly
Google+
Twitter
RSS
E-Mail
100%
0%

Bad Bots Up Their Human Impersonation Game

Every third website visitor was an attack bot in 2016, and humans represent just under half of all Internet traffic, new Imperva data sample shows.

Unsavory traffic on the Web continues to flow at a steady clip with nearly one-third of it from bad bots.

New data released from Imperva today shows bots with the upper hand overall, with humans representing 48.2% of website traffic in 2016; so-called "good" bots (think feed-fetchers, search engine bots and crawlers) at 22.9%, and bad bots accounting for 28.9% of the traffic. Bad bots mainly include automated accounts posing as humans, which make up about one-fourth of all bad bots. Other bad actor bots: hacker tools, scrapers, and spammers.

Every third visitor to a website was a bad bot last year, and more than 94% of websites in the study suffered at least one attack by a bad bot during the 90-day study of some 16.7 billion visits to 100,000 randomly-selected domains on Imperva's Incapsula network, a cloud-based service that provides web security, DDoS protection, and optimization for content delivery networks.

"I don't know if people  know that every third visitor to their website is an attack bot," says Igal Zeifman, a senior manager at Imperva. "The majority of automated visits … are doing something they shouldn't be doing, scraping the content of a website," spamming, comment-spamming, link-spamming, auto-filling online forms, and of course, waging distributed denial-of-service (DDoS) attacks, he says.

Bad bots have maintained a steady presence online as the Internet continues to grow, at 31% in 2012, a dipping slightly to around 29% the past couple of years, according to Imperva's data.  "This talks [of] motivation" of attackers, and their ability to successfully attack via bots, he says.

Imperva's report says "impersonator" bots remain the most prolific brand of bad bot: they accounted for 243.% of all traffic on Imperva's Incapsula network last year. These are bots that not only launch DDoS attacks, but also pose as browsing users in order to evade security detection tools. Depending on the website, the biggest risk of these nasty bots is DDoS attacks, using the site as a malware distribution forum, or as the first phase of an attack that ultimately infiltrates the organization itself, according to Zeifman.

Distil Networks last year found that last year humans outnumbered bad bots on the Web for the first time since 2013. But Distil's data drew from its Hadoop cluster that includes some 74 million bot requests and other customer data. Unlike Imperva's data set, it doesn't include DDoS bots but instead all other types of bad bots, including digital ad fraud.

What was in common, however, was that Distil also saw an increase bad bots imitating human online behavior. "I think that what's interesting is that the sophistication of bots seems to be increasing," says Edward Roberts, director of product marketing at Distil, which currently is putting the finishing touches on its new 2016 bot activity report.

For example, these smarter and more human-like bad bots are spreading around website activity requests among thousands of IPs, he says, in order to remain under the radar of web security tools and teams. They pause a few seconds between page requests, for instance, and move the mouse similar to the way a human does, he says.

Some organizations are suffering more than others from bad bot activity. "Two of three requests are [via] bad bots on some companies'" websites, he says.

"This is something that's not going away," Distil's Roberts says.

Related Content:

Kelly Jackson Higgins is the Executive Editor of Dark Reading. She is an award-winning veteran technology and business journalist with more than two decades of experience in reporting and editing for various publications, including Network Computing, Secure Enterprise ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Joe Stanganelli
50%
50%
Joe Stanganelli,
User Rank: Ninja
1/25/2017 | 10:21:48 AM
Sophistication in automation
At the end of the day, though, bot activity -- by definition -- is automated.

Therefore, as bots get more sophisticated, the "good guys" working to stop them have to as well.

So if the bots move the mouse similar to the way a human does, they're still doing this activity on repeat from a set of instructions.  Accordingly, certain patterns must be learned -- and, from there, treated in an escalatedly guarded manner when detected.
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...