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Over 4 percent of all Monero was mined by malware botnets



An estimated 4.32 percent of all the Monero cryptocurrency currently in circulation has been mined by botnets and cyber-criminal operations, according to a study published earlier this month by academics in Spain and the UK.

The research was one of the biggest undertakings of its kind in recent years. Scientists analyzed around 4.4 million malware samples to identify one million malware strains that mined cryptocurrency on infected hosts.

The malware strains they analyzed spanned a period of a whopping twelve years, between 2007 and 2018.

The research team says it looked at IOCs (indicators of compromise) and used static and dynamic analysis techniques to extract information from malware strains, such as cryptocurrency addresses and mining pools that malware strains used in the past to collect and funnel money through.

Researchers used the data they collected to track down past payments from mining pools to the groups behind each wallet. Further, the also organized the malware strains in campaigns based on similarities and shared cryptocurrency addresses.

According to the study’s findings, while some groups mined various cryptocurrencies in the late 2000s and early 2010s, Monero (XMR) is by far the most popular cryptocurrency among cyber-criminals in underground economies, at the current time.

Excluding the earnings of groups that mined Monero using rogue JavaScript code loaded in users’ browsers (a technique called cryptojacking), researchers say that crypto-mining malware botnets have been responsible for mining 4.32 percent of all Monero coins.

“Although this depends on when criminals cash-out their earnings, we estimate that the total revenue accounts for nearly [$57 million],” researchers said in their paper.

Some criminal groups have been more efficient than others. Researchers say that the groups who rented malware and third-party server infrastructure on underground marketplaces were usually more successful than the vast majority of groups who built their own tools.

However, overall, regardless if they rented the malware or built it themselves, the most successful groups were the ones who used botnets to deploy their malware at scale.

The report mentions previously well-known Monero-mining campaigns such as Adylkuzz and Smominru, but researchers also said they uncovered new groups, with the biggest being one they called Freebuf and one called USA-138.

Monero miners groups

Image: Pastrana et al.

One group, in particular, made over $18 million worth of Monero, which would round up to roughly 1.45 percent of all Monero coins.

Some of these groups existed for small periods of time, but others updated their infrastructure and malware cosntantly, still being active to this day. Researchers say crooks usually modified their code when their XMR addresses got banned from certain mining pools, or when they needed to update the malware to apply mining protocol changes.

When researchers looked at what mining pool criminal groups preferred to handle mining operations and withdrawals to their private addresses, by far the number one choice was, responsible for cashing out $47 million of the $57 million the researchers managed to track.

Monero miner pools

Image: Pastrana et al.

While researchers saw some criminal groups put efforts into mining other cryptocurrencies in past years, Monero is now the preferred cryptocurrencies for almost all crypto-mining operations.

The reason isn’t hard to guess, as mining Bitcoin-based cryptocurrencies have now a higher mining difficulty and also need special hardware, which makes deploying malware mining these types of currencies on regular PCs both useless and unprofitable.

A last conclusion of this research paper, but not the least interesting, is that most of today’s criminal mining operations rely on the open source tool named xmrig, around which most crooks have built their crypto-mining malware around.

While researchers saw more malware samples built around the Claymore mining software, most of the active malware campaigns they tracked used a xmrig-based malware strain.

This statistics can be explained by the somewhat experimental nature of Bitcoin-based crypto-mining malware strain in the late 2000s when malware authors toyed around with Bitcoin mining malware but never deployed it in the types of large scale campaigns we’ve seen Monero miners deployed in the past two years.

Monero mining tools

Image: Pastrana et al.

For more details about the cryptocurrency mining malware scene, please refer to the researcher paper entitled “A First Look at the Crypto-Mining Malware Ecosystem: A Decade of Unrestricted Wealth,” authored by researchers from the Universidad Carlos III de Madrid and King’s College London.

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Cloud Data Security



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



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



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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