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FireEye links Russian research lab to Triton ICS malware attacks

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CNIIHM, Moscow


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A Russian research laboratory is behind cyber-attacks on critical infrastructure, including on a Saudi petrochemical plant, according to a report published today by US cyber-security firm FireEye.

The cyber-attacks took place in 2017 and deployed a never-before-seen malware strain known as Triton –or Trisis– specifically engineered to interact with Schneider Electric’s Triconex Safety Instrumented System (SIS) controllers

According to technical reports from FireEye, Dragos, and Symantec, Triton was designed to either shut down a production process or allow SIS-controlled machinery to work in an unsafe state.

The group behind the malware, which FireEye has been tracking under the codename of TEMP.Veles, nearly succeeded last year, when it almost caused an explosion at a Saudi petrochemical plant owned by Tasnee, a privately owned Saudi company, according to a New York Times report.

The malware’s origins were a mystery when FireEye first discovered Triton in 2017 and remained a mystery even after the New York Times report in March 2018.

But in a report published today, FireEye says that following further research into incidents where the Triton malware was deployed, it can now assess with “high confidence” that the Central Scientific Research Institute of Chemistry and Mechanics (CNIIHM; ЦНИИХМ), a government-owned technical research institution located in Moscow, was involved in these attacks.

FireEye’s report does not link the Triton malware itself to CNIIHM, but the secondary malware strains used by TEMP.Veles and deployed during the incidents where Triton was deployed.

Clues in these secondary malware strains used to aid the deployment of the main Triton payloads contained enough artifacts that allowed researchers to identify their source.

The company lists some –but not all– indicators that led its researchers to reach the conclusion that CNIIHM was behind the development of these Triton-adjacent malware strains deployed during the main Triton attacks.

  • A PDB path for one of the files contained a string that appears to be a unique handle or username. That handle/username belongs to a Moscow-based infosec expert and a former professor at CNIIHM.
  • Malicious activity tied to TEMP.Veles scans and monitoring operations originated from 87.245.143.140, an IP address registered to CNIIHM.
  • Multiple Triton-related files –which were also accidentally uploaded online in December 2017– contained Cyrillic names and artifacts.
  • Malware file creation times are consistent with regular working hours specific the Moscow timezone.
cniihm-timezone.png

Image: FireEye

“Some possibility remains that one or more CNIIHM employees could have conducted the activity linking TEMP.Veles to CNIIHM without their employer’s approval,” FireEye said today. “However, this scenario is highly unlikely.”

FireEye says that based on CNIIHM’s self-described mission and other public information, the research lab had both the tools and expertise to develop this type of malware, but also reasons to do so because of its ties to various Russian military and critical infrastructure apparatus.

As for why Russia would be interested in sabotaging a Saudi petrochemical power plant, the reasons are unknown.

When the attack on the Tasnee plant came to light, many infosec experts attributed Triton –without any sustaining evidence– to Iran’s cyber-intelligence apparatus.

Anything is possible at this point, from Russia wanting to destabilize the Gulf region to CNIIHM renting its cyber-capabilities to external threat actors.

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Cloud Data Security

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Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.

This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:

  • Dynamic versus static
  • Scalability
  • Evolvability

In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.

This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.

Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.

The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.

Figure 1. Difference in Cumulative Policy Changes

The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.

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GigaOm Radar for Data Loss Prevention

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Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.

With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.

This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.

Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.

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Key Criteria for Evaluating Data Loss Prevention Platforms

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Data is a crucial asset for modern businesses and has to be protected in the same way as any other corporate asset, with diligence and care. Loss of data can have catastrophic effects, from reputational damage to significant fines for breaking increasingly stringent regulations.

While the risk of data loss is not new, the landscape we operate in is evolving rapidly. Data can leave data centers in many ways, whether accidental or malicious. The routes for exfiltration also continue to grow, ranging from email, USB sticks, and laptops to ever-more-widely-adopted cloud applications, collaboration tools, and mobile devices. This is driving a resurgence in the enterprise’s need to ensure that no data leaves the organization without appropriate checks and balances in place.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. Policy, people, and technology are critical components in a data loss prevention (DLP) strategy.

As with any information security strategy, technology plays a significant role. DLP technology has traditionally played a part in helping organizations to mitigate some of the risks of uncontrolled data exfiltration. However, both the technology and threat landscape have shifted significantly, which has led to a reevaluation of DLP tools and strategy.

The modern approach to the challenge needs to be holistic and intelligent, capable of applying context to data usage by building a broader understanding of what the data is, who is using it, and why. Systems in place must also be able to learn when user activity should be classified as unusual so they can better interpret signs of a potential breach.

This advanced approach is also driving new ways of defining the discipline of data loss prevention. Dealing with these risks cannot be viewed in isolation; rather, it must be part of a wider insider risk-management strategy.

Stopping the loss of data, accidental or otherwise, is no small task. This GigaOM Key Criteria Report details DLP solutions and identifies key criteria and evaluation metrics for selecting such a solution. The corresponding GigOm Radar Report identifies vendors and products in this sector that excel. Together, these reports will give decision-makers an overview of the market to help them evaluate existing platforms and decide where to invest.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:

Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.

GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.

Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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