Big Data. Big Decisions
InformationWeek
Special Coverage Series


Operation Red October Attackers Wielded Spear Phishing

Advanced, malware-driven espionage network employed over 1,000 modules and tools customized for just hundreds of targets, finds Kaspersky analysis.

The Red October malware network is one of the most advanced online espionage operations that's ever been discovered. That's the conclusion of Moscow-based security firm Kaspersky Lab, which first discovered Operation Red October--"Rocra" for short--in October 2012.

"The primary focus of this campaign targets countries in Eastern Europe, former USSR republics, and countries in Central Asia, although victims can be found everywhere, including Western Europe and North America," according to research published by the security firm. The attackers, who appear to speak Russian but to have also used some Chinese-made software, seem to have focused their efforts on stealing diplomatic and government information, as well as scientific research, from not just PCs and servers but also mobile devices.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

The Red October attacks began in 2007, and remained active at least through Sunday, which was the day before Kaspersky Lab first publicly detailed its research into the espionage operation.

In a more detailed technical analysis published Thursday that stretches 140 pages, Kaspersky Lab provided additional information about the operators' attack techniques, including the malware family used in the attacks, which it's dubbed Sputnik, and which was used to infect just hundreds of systems. "According to our knowledge, never before in the history of [information security] has [a] cyber-espionage operation been analyzed in such deep detail, with a focus on the modules used for attack and data exfiltration," said Kaspersky Lab.

[ Get the facts about Java zero-day vulnerabilities. Read Java Security Warnings: Cut Through The Confusion. ]

But studying an espionage malware operation such as Red October, which was designed to steal data from specific targets -- assigning people unique ID numbers and in some cases employing malware modules customized solely for that target -- is complicated by researchers not being able to see the data that was stolen or recover every attack module.

Accordingly, Kaspersky Lab researchers determined to play the victim. "To get around these hiccups, we set up several fake victims around the world and monitored how the attackers handled them over the course of several months," they said. "This allowed us to collect hundreds of attack modules and tools. In addition to these, we identified many other modules used in other attacks, which allowed us to gain a unique insight into the attack."

All known Red October attacks have been launched using spear-phishing emails with attachments carrying "enticing names," said researchers. The attachments recovered to date have been malicious Excel and Word documents, although attackers also appear to have used the so-called Rhino exploit for a Java bug first found in 2011. Regardless of the attack, the goal is to infect a target system with backdoor and dropper software known as Sputnik.

To be clear, Kaspersky Lab said that Sputnik isn't as advanced as the Flame malware that it was the first to discover, and which was reportedly the product of a U.S. cyber-weapons program. Flame tapped world-class crypto to create a never-seen-before type of collision attack on Windows Update, which allowed attackers to instruct targeted Windows operating systems to install their malware. At the time, Kaspersky Lab researcher Alexander Gostev likened the capability to the "god mode" cheat in videogames that makes a user invulnerable and allows them to move about a game at will.

Still, the Red October operation is extensive, and attackers have designed or customized more than 1,000 modules and tools, which they could instruct any Sputnik-infection system to download. To help analyze all of those different attack modules, Kaspersky Lab has grouped them into nine categories: reconnaissance (to gather information about a targeted system immediately upon infection); password (for stealing passwords); email (to steal emails); USB drives (monitor and steal data); keyboard (log keystrokes); persistence (plant malicious plug-ins in applications such as Microsoft Office and Adobe Reader); spreading (scan for new targets on a local network); mobile (grab data from smartphones and other PC-connected devices); and exfiltration (transfer all collected data to command-and-control server).

Researchers have yet to recover samples of all modules that were used by attackers. For example, a USB infection module hasn't yet been recovered. "We suspect that this module is capable of infecting removable storage, running arbitrary modules from other groups and [saving] data back to the USB drives," they said. No doubt the hunt for more Red October and Sputnik clues to continue.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.