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Hacker stole unreleased music and then tried to frame someone else

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Image: Anthony Roberts

US authorities charged a Texas man this week for hacking into the cloud accounts of two music companies and the social media account of a high-profile music producer, from where he stole unreleased songs that he later published online for free on public internet forums.

When the man realized he could be caught, he contacted one of the hacked companies and tried to pin the blame on another individual.

Hacks targeted cloud accounts for music labels

According to court documents published on Monday by the Department of Justice, the suspect is a 27-year-old named Christian Erazo, from Austin, Texas.

US authorities say that Erazo worked with three other co-conspirators on a series of hacks that took place between late 2016 and April 2017.

The group’s primary targets were two music management companies, one located in New York, and the second in Los Angeles.

According to investigators, the four hackers obtained and used employee credentials to access the companies’ cloud storage accounts, from where they downloaded more than 100 unreleased music songs.

Most of the data came from the New York-based music label, from where the Erazo and co-conspirators stole more than 50 GBs of music. Erazo’s indictment claims the group accessed the company’s cloud storage account more than 2,300 times across several months.

Hackers also went after producers and artists

Erazo also allegedly hacked the “microblogging and social networking” account (very likely Twitter) of an LA-based musician and producer.

The suspect used the access to this hacked account to send private messages to other producers and music artists, asking them to send unreleased songs to an email address under Erazo’s control, investigators said.

Songs gathered through this scheme were later also leaked online on internet forums, damaging the producer’s reputation.

The attempted cover-up

US officials said that Erazo began discussing with his co-conspirators in December 2016 about the idea of pinning the hacks on someone else — referred in the indictment only as “Individual-1.”

The group went ahead with their plan on January 8, when one of the co-conspirators emailed the NY-based music label stating that Individual-1 had gained access to their cloud data and was currently selling their songs online for $300 per track.

The music label contacted authorities, and when Erazo and a co-conspirator called the music label ten days later on January 18, they talked on the phone with an undercover FBI agent posing as the label’s security staff.

Investigators say that during this conversation and later emails, Erazo and friends posed as do-gooders trying to help to company and its music artists.

According to statements quoted in the indictment, Erazo said he was “doing this for the love of the artists” and claiming they want no harm done to the producer — who, they were still actively hacking at the time.

US authorities said that Erazo offered to help the music company in its investigation into Individual-1.

“I’m happy to help out if you need any of the info or anything I could dig up for you guys just let me know and I’m more than happy to help you guys out with this,” Erazo was quoted in the indictment as saying.

“Yeah and another thing to why we are going to you guys is we just hate this fucking [person]. Bottom line. We aren’t even going to beat around the bush,” Erazo also allegedly said, also offering to play a double agent if the music company asked.

In addition, Erazo urged the music label to take legal action against this person, and also advised the company about improving the security of its cloud storage account.

A week after contacting the NY music company, investigators said that Erazo sent on online message to one of his co-conspirators saying that “this is the perf[ect] cover up.”

Charges and sentence

However, Erazo’s plans didn’t work. He was charged in a New York court on Monday under three counts.

Charges include one count of conspiracy to commit wire fraud, which carries a maximum sentence of 20 years; one count of conspiracy to commit computer intrusion, which carries a maximum sentence of five years; and one count of aggravated identity theft, which carries a mandatory minimum term of imprisonment of two years.

News of Erazo’s arrest comes after in mid-September UK police arrested two teens — one in London and one in Ipswich — for similar charges of stealing data from music artists. It is unclear if the two cases are related, however, the two UK teens were accused of selling the tracks online, rather than releasing them on forums.

Erazo’s case is not related to the Radiohead incident from March 2019 when a hacker gained access to unreleased Radiohead music and tried to extort the band for $150,000. In response to the ransom attempt, the band published the music on a Bandcamp account ahead of its planned release.

This is by no means the first incident of its kind, and hacks like these have been happening for years. For example, in 2012, hackers stole and published more than 50,000 songs from Sony Music, including unreleased Michael Jackson songs.



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Key Criteria for Evaluating Security Information and Event Management Solutions (SIEM)

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Security Information and Event Management (SIEM) solutions consolidate multiple security data streams under a single roof. Initially, SIEM supported early detection of cyberattacks and data breaches by collecting and correlating security event logs. Over time, it evolved into sophisticated systems capable of ingesting huge volumes of data from disparate sources, analyzing data in real time, and gathering additional context from threat intelligence feeds and new sources of security-related data. Next-generation SIEM solutions deliver tight integrations with other security products, advanced analytics, and semi-autonomous incident response.

SIEM solutions can be deployed on-premises, in the cloud, or a mix of the two. Deployment models must be weighed with regard to the environments the SIEM solution will protect. With more and more digital infrastructure and services becoming mission critical to every enterprise, SIEMs must handle higher volumes of data. Vendors and customers are increasingly focused on cloud-based solutions, whether SaaS or cloud-hosted models, for their scalability and flexibility.

The latest developments for SIEM solutions include machine learning capabilities for incident detection, advanced analytics features that include user behavior analytics (UBA), and integrations with other security solutions, such as security orchestration automation and response (SOAR) and endpoint detection and response (EDR) systems. Even though additional capabilities within the SIEM environment are a natural progression, customers are finding it even more difficult to deploy, customize, and operate SIEM solutions.

Other improvements include better user experience and lower time-to-value for new deployments. To achieve this, vendors are working on:

  • Streamlining data onboarding
  • Preloading customizable content—use cases, rulesets, and playbooks
  • Standardizing data formats and labels
  • Mapping incident alerts to common frameworks, such as the MITRE ATT&CK framework

Vendors and service providers are also expanding their offerings beyond managed SIEM solutions to à la carte services, such as content development services and threat hunting-as-a-service.

There is no one-size-fits-all SIEM solution. Each organization will have to evaluate its own requirements and resource constraints to find the right solution. Organizations will weigh factors such as deployment models or integrations with existing applications and security solutions. However, the main decision factor for most customers will revolve around usability, affordability, and return on investment. Fortunately, a wide range of solutions available in the market can almost guarantee a good fit for every customer.

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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Key Criteria for Evaluating Secure Service Access

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Since the inception of large-scale computing, enterprises, organizations, and service providers have protected their digital assets by securing the perimeter of their on-premises data centers. With the advent of cloud computing, the perimeter has dissolved, but—in most cases—the legacy approach to security hasn not. Many corporations still manage the expanded enterprise and remote workforce as an extension of the old headquarters office/branch model serviced by LANs and WANs.

Bolting new security products onto their aging networks increased costs and complexity exponentially, while at the same time severely limiting their ability to meet regulatory compliance mandates, scale elastically, or secure the threat surface of the new any place/any user/any device perimeter.

The result? Patchwork security ill-suited to the demands of the post-COVID distributed enterprise.

Converging networking and security, secure service access (SSA) represents a significant shift in the way organizations consume network security, enabling them to replace multiple security vendors with a single, integrated platform offering full interoperability and end-to-end redundancy. Encompassing secure access service edge (SASE), zero-trust network access (ZTNA), and extended detection and response (XDR), SSA shifts the focus of security consumption from being either data center or edge-centric to being ubiquitous, with an emphasis on securing services irrespective of user identity or resources accessed.

This GigaOm Key Criteria report outlines critical criteria and evaluation metrics for selecting an SSA solution. The corresponding GigaOm Radar Report provides an overview of notable SSA vendors and their offerings available today. Together, these reports are designed to help educate decision-makers, making them aware of various approaches and vendors that are meeting the challenges of the distributed enterprise in the post-pandemic era.

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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Key Criteria for Evaluating Edge Platforms

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Edge platforms leverage distributed infrastructure to deliver content, computing, and security closer to end devices, offloading networks and improving performance. We define edge platforms as the solutions capable of providing end users with millisecond access to processing power, media files, storage, secure connectivity, and related “cloud-like” services.

The key benefit of edge platforms is bringing websites, applications, media, security, and a multitude of virtual infrastructures and services closer to end devices compared to public or private cloud locations.

The need for content proximity started to become more evident in the early 2000s as the web evolved from a read-only service to a read-write experience, and users worldwide began both consuming and creating content. Today, this is even more important, as live and on-demand video streaming at very high resolutions cannot be sustained from a single central location. Content delivery networks (CDNs) helped host these types of media at the edge, and the associated network optimization methods allowed them to provide these new demanding services.

As we moved into the early 2010s, we experienced the rapid cloudification of traditional infrastructure. Roughly speaking, cloud computing takes a server from a user’s office, puts it in a faraway data center, and allows it to be used across the internet. Cloud providers manage the underlying hardware and provide it as a service, allowing users to provision their own virtual infrastructure. There are many operational benefits, but at least one unavoidable downside: the increase in latency. This is especially true in this dawning age of distributed enterprises for which there is not just a single office to optimize. Instead, “the office” is now anywhere and everywhere employees happen to be.

Even so, this centralized, cloud-based compute methodology works very well for most enterprise applications, as long as there is no critical sensitivity to delay. But what about use cases that cannot tolerate latency? Think industrial monitoring and control, real-time machine learning, autonomous vehicles, augmented reality, and gaming. If a cloud data center is a few hundred or even thousands of miles away, the physical limitations of sending an optical or electrical pulse through a cable mean there are no options to lower the latency. The answer to this is leveraging a distributed infrastructure model, which has traditionally been used by content delivery networks.

As CDNs have brought the internet’s content closer to everyone, CDN providers have positioned themselves in the unique space of owning much of the infrastructure required to bring computing and security closer to users and end devices. With servers close to the topological edge of the network, CDN providers can offer processing power and other “cloud-like” services to end devices with only a few milliseconds latency.

While CDN operators are in the right place at the right time to develop edge platforms, we’ve observed a total of four types of vendors that have been building out relevant—and potentially competing—edge infrastructure. These include traditional CDNs, hyperscale cloud providers, telecommunications companies, and new dedicated edge platform operators, purpose-built for this emerging requirement.

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.

Vendor 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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