Headlines like “IoT botnet targets enterprise devices” sound very scary, but the reality is that many of these botnets are the work of kids playing around with random exploits they found online, and many of these botnets die out in a matter of weeks as the authors get bored or move on to other projects.
The prime example of this is Kepler, an IoT botnet that made the news last month because of its ability to infect signage TVs and presentation systems, two types of devices found in enterprise networks.
On a first read, any report on Kepler would seem like the botnet author was intentionally aiming to get a foothold on corporate networks so they could later deploy more potent malware.
Kepler authors: We were just having fun!
However, things aren’t actually so. In a recorded interview with Ankit Anubhav, security researcher for NewSky Security, and published on Soundcloud, the botnet’s two authors admitted that they built the botnet for fun, adding exploits taken from the ExploitDB website at random.
“We’re just a couple of friends having fun,” the two said before admitting that this was actually the second botnet they put together, after also creating one last year.
Despite Kepler generating some scary headlines, the duo didn’t take the work on their botnet seriously, admitting that they didn’t even bother counting the number of devices their botnet infected.
Further, the botnet didn’t use 27 exploits, as Palo Alto Networks initially reported, but 43, according to Nipsu, one of the Kepler authors –all, of course, chosen at random.
According to the interview, the two hackers, one of which is a minor, were just testing random vulnerability exploits, wanting to see which one gathered more bots.
The two said they have no current plans to sell the Kepler botnet to other cybercrime gangs or rent it for DDoS attacks –a common practice and a source of profit for many IoT botnet authors.
Kepler is a trend, not an edge case
These revelations aren’t just an isolated edge case in the IoT botnet scene. Many botnet authors are just kids taking their first steps in the world of programming and cybersecurity, playing around with exploits, before realizing the legal problem they could be in, and moving on to other careers –or getting arrested [1, 2].
A large part of the IoT botnet operators listed on this Top 20 IoT Blackhat Hackers list have now moved on from running IoT botnets.
One of them, Switch (#15 on the list), offered his insights on the current IoT botnet scene. Asked how easy it would be to integrate code taken from ExploitDB into a botnet, Switch, who is the author of many YouTube tutorials on building IoT malware, provided the following answer.
“If you’re talking auto scanners such as [the ones looking for] Huawei, Realtek, ThinkPHP etc., it’s VERY easy,” Switch said. “There is only a few lines of code to it. I have made a tutorial before on how to do it as it only takes a few minutes.”
“I have seen a few people write scanners [for vulnerable devices] into sources before, and by what I can see it’s mainly copying and pasting,” he said.
Anubhav, a security researcher who spends his whole day looking at IoT botnets, also shares Switch’s opinion.
“The majority of IoT exploit code is heavily borrowed from ExploitDB,” Anubhav said.
“In many cases, the vulnerabilities are not able to infect a lot of devices, hence the attackers are trying as many vulnerabilities as they can to further assess which one is going to reap maximum benefits.”
It’s a Mirai/Gafgyt party
But besides Kepler, another recent example of an IoT botnet that is not as dangerous as it looks is the one documented by Trend Micro this week.
This botnet, which has no particular name, runs on a variant of the Bashlite (Gafgyt) IoT malware, which is used as a skeleton and customized with several ExploitDB exploits on top –which in this case targeted Belkin WeMo devices.
The anatomy of this botnet is representative of the whole IoT malware scene these days. Botnet operators use the source code of IoT malware that previously leaked online in past years as a wireframe for the delivery of random exploits they’ve copied from ExploitDB.
In most cases, these botnets are built on the Bashlite (Gafgyt) or Mirai IoT malware strains, and only very rarely does a true malware coder come around to innovate and create new malware strains –such as hackers like Wicked or the Janit0r (the BrickerBot author).
Related malware and cybercrime coverage:
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
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.
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.
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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