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Security firm identifies hacker behind Collection 1 leak, as Collection 2-5 become public

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The threat intel team at Recorded Future, a US-based cyber-security firm, claims to have identified the hacker who assembled and then sold a massive collection of email addresses and passwords known as Collection #1.

The company’s experts believe a hacker going online by the pseudonym of “C0rpz” is the person who rigorously and meticulously collected billions of user records over the past three years. This includes records from companies that were hacked in the past and whose data was posted or sold online.

Recorded Future says that C0rpz isn’t only responsible for assembling and selling Collection #1, a data trove of 773 million unique email addresses and just under 22 million unique passwords that grabbed headlines at the start of the year, but many more other data collections.

Researchers say Collection #1 was part of a larger package containing seven other “collections” in total.

  • “ANTIPUBLIC #1” (102.04 GB)
  • “AP MYR & ZABUGOR #2” (19.49 GB)
  • “Collection #1” (87.18 GB)
  • “Collection #2” (528.50 GB)
  • “Collection #3” (37.18 GB)
  • “Collection #4” (178.58 GB)
  • “Collection #5” (40.56 GB)

Of the seven, the AntiPublic collection had already leaked online and had been shared among other hackers since April 2017. The rest appear to be new items, that hadn’t been seen online until this month.

In total, these databases appear to contain more than 3.5 billion user records, in combinations such as email addresses and passwords, usernames and passwords, and cell phone numbers and passwords.

Recorded Future says C0rpz sold this data to other hackers, who are now disseminating it for free via online sharing portal MEGA and via torrent magnet links.

Some of the hackers who bought this data from C0rpz are Sanix, another hacker who infosec journalist Brian Krebs first identified as the source of Collection #1, and Clorox, the person who initially shared Collection #1 for free on Raid Forums at the start of the month, inadvertently exposing this huge data trove to security researchers and journalists.

“Neither of three actors has ever been on our radar,” Andrei Barysevich, Director of Advanced Collection at Recorded Future, told ZDNet in an email today. “However, we did find a previous online footprint on all actors, which does not suggest that these actors are sophisticated.”

Barysevich also told ZDNet that his team didn’t find “any proof” that the named three, including C0rpz, are hackers, responsible for actual breaches at any company.

“We believe they have merely aggregated the data over the time,” Barysevich told us.

But Recorded Future experts aren’t 100 percent sure in their attribution of these data collections to C0rpz –as no attribution that involves self-aggrandizing and braggadocio hackers can truly ever be 100 percent. Experts are also looking into another possible source of the leak, which they did not name yet.

“On January 10, 2019, an actor on a well-known Russian-speaking hacker forum posted both a magnet link and a direct download link to a database containing 100 billion user accounts hosted on a personal website,” Recorded Future said in a report published earlier today. “The following week, the actor made clear that the data dump referenced in Troy Hunt’s [Collection #1] article was included in their dump as well.”

To be fair, it doesn’t really matter who assembled, sold, or shared this data in the end. All this data was previously available for years. The difference was that in past, this data was shared in individual packages, per site of origin.

It’s only become a recent trend for data hoarders (hackers who collected data from hacked sites) to assemble these smaller leaks and breaches into gigantic packages.

This became a trend because more and more companies are getting hacked, and the value of individual leaks became smaller. Data sellers adapted and started merging leaks together to continue to make a profit.

There are likely hundreds of similar mega-packages being shared on hacking forums out of the public eye as we speak, which have not made the light of day yet.

Eventually, they will. When that happens, cyber-crime groups will collect these aggregated leaks, extract any new user records they don’t have, and use this information to spam our email inboxes, attempt brute-force attacks against our online accounts, or, even worse, use these details for extortion or financial fraud.

It is highly likely that most of our data has already leaked online by now. All, we, the users, can do is protect our accounts with strong passwords that are unique per site, enable multi-factor authentication wherever possible, and avoid entrusting our data to any company that asks for our details for no good reason.

Now, if we could only get journalists to stop blowing these “collections” out of proportion every time one of them surfaces online.

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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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