The FCC Just Banned All New Foreign-Made Routers. Everything You Need to Know to Keep Your Network Safe


Thinking about buying a new Wi-Fi router? You might want to hold off.

Citing “unacceptable risks” to national security, the Federal Communications Commission says it will be banning all new foreign-made Wi-Fi routers going forward.

The ban doesn’t apply to any existing routers that the FCC has already authorized, but will impact any new models “produced in foreign countries.” Router manufacturers can apply for an exemption, but so far, none have been granted “Conditional Approval” on the FCC’s website

This is a monumental development for the US Wi-Fi router market. With the exception of newer Starlink routers, nearly every router available for purchase in this country is at least partially manufactured outside the US, including TP-Link, Asus and Netgear. An estimated 60% of routers in the US are manufactured in China.

According to a list of FAQs published by the FCC, a router will be considered foreign-made if “any major stage of the process through which the device is made, including manufacturing, assembly, design and development” occurs outside the US. 

“Following President Trump’s leadership, the FCC will continue do our part in making sure that US cyberspace, critical infrastructure and supply chains are safe and secure,” said FCC Chair Brendan Carr in a statement

When CNET reached out to the FCC for more clarity on the order, we were referred to the commission’s “Covered List” FAQ page.

The FCC says that routers produced abroad were “directly implicated” in the Volt, Flax and Salt Typhoon cyberattacks. The Salt Typhoon attack specifically exploited Cisco routers to gain access to the networks of US internet providers like AT&T, Verizon and Lumen, which owns CenturyLink and Quantum Fiber.

“This is using an extremely blunt instrument, and it’s going to impact many harmless products in order to stem a real problem,” William Budington, a technologist for the digital rights nonprofit Electronic Frontier Foundation, told CNET. “This takes place in the context of mass defunding of cyberdefense initiatives. There’s a lack of a good federal testing lab for consumer grade routers due to budget cuts.”

This doesn’t mean you have to replace your existing router. The FCC clarified that the ban doesn’t apply to previously-purchased routers, but you won’t be able to buy new routers that the FCC hadn’t already authorized before the ban. 

TP-Link specifically has been in the US government’s crosshairs for over a year, stemming from its ties to China, with more than half a dozen US departments and agencies reportedly backing a ban at the end of 2025.

But this week’s FCC action goes well beyond TP-Link and will affect nearly every router company operating in the US.

Can your router still be used?

You can still use your existing router, but there is one big caveat hidden in the FCC’s Public Notice: “All routers authorized for use in the United States may continue to receive software and firmware updates that mitigate harm to US consumers at least until March 1, 2027.”

Firmware updates are essential to both your router’s performance and security. Most router companies issue automatic firmware updates to fix security vulnerabilities as they pop up, and you may not even be aware when they happen.

If a router can’t update its firmware after March 1 of next year, it’s generally considered unsafe to continue using, as your Wi-Fi network could become vulnerable to malware or other cybersecurity threats without regular firmware updates.

“The risk is very real,” said Rik Ferguson, vice president of security intelligence at cybersecurity company Forescout. “If you find yourself in a situation where that update pipeline has been switched off, then you definitely have to consider whether you want to keep using that device.”

“The risk just keeps going the longer time passes, because chances are that there will be new vulnerabilities being found that you cannot patch,” added Daniel Dos Santos, vice president of research at Forescout.

Router companies are surely scrambling behind the scenes right now to get added to the FCC’s “Conditional Approval” list, which would allow them to sell new models and continue issuing software and firmware updates to routers that have already been approved. 

There is some wiggle room in there. The FCC notice specifically says “at least” March 1, so it’s possible the deadline will be pushed back.

But if your router hasn’t been added to the exemption list by this time next year, I’d recommend swapping it out for a model that has FCC approval to continue receiving firmware updates. 

“I don’t think it’s going to change the manufacturing landscape, because manufacturing processes are expensive to move and device manufacturers are probably going to just wait it out until the ban is lifted. So I don’t think it’s going to have the intended effect,” Budington said. 

Should I wait or rush to buy a new router? 

The FCC’s ban on foreign-made routers only applies to devices that haven’t already been approved. That means any router that’s currently for sale will still remain on the shelves, and you can continue to use your existing router as long as you’d like.

Because any router that’s available now has already gotten FCC authorization, there’s no need to rush out and buy a new router. In fact, I would recommend the opposite: holding off on buying a new router until some of the dust settles on the FCC order. That advice was echoed by the six cybersecurity experts I polled for this story.

“I would recommend to wait at least for a few weeks or a month to see what are the real implications of this,” Sergey Shykevich, a threat intelligence manager at Check Point Research, told me.

If you buy a new router today, there’s a risk that the FCC won’t exempt it, and it will stop getting software and firmware updates after March 1 of next year.

“A lot of those routers are going to turn into pumpkins in a year unless they extend this waiver,” Alan Butler, senior counsel at the Electronic Privacy Information Center, told me.

CNET recently tested and reviewed more than 30 Wi-Fi routers, and while we stand by all of our picks, I’d recommend holding off on a purchase until we have more information on the FCC’s ban. 

Which routers are impacted by the ban?

Representatives for the FCC couldn’t tell me which specific router companies will be subject to the ban, but nearly every Wi-Fi router available in the US has some stage of “manufacturing, assembly, design and development” occurring outside the country. (Starlink is apparently the only exception; the company says its newer routers are manufactured in Texas, according to the BBC.) 

Untangling each router’s supply chain will be a complicated process, and router companies are likely already lobbying the FCC for “Conditional Approval.” 

“Every single one of these devices, even if the final assembly happens in California, for example, they’re all going to come with components that are manufactured in China, as an example,” Sonu Shankar, chief product officer at Phosphorus Cybersecurity, told CNET. 

CNET reached out to 10 of the top router manufacturers for comment. So far, companies seem to be taking a friendly public approach to the FCC, even when they’re clearly subject to the ban. Netgear, for example, highlighted its US headquarters, even though its routers are manufactured in Vietnam, Thailand, Indonesia and Taiwan.

Router company Status following the announcement
Asus Headquartered in Taiwan, subject to the ban.
Cisco Does not sell new consumer-grade routers, not subject to the ban.
D-Link Headquartered in Taiwan, subject to the ban.
Eero Manufacturing in Asia, subject to the ban.
Linksys Owned by Foxconn, a Taiwanese multinational. Subject to the ban.
Nest Manufacturing in Taiwan and Malaysia, subject to the ban.
Netgear Publicly supporting the ban, but has manufacturing in Vietnam, Thailand, Indonesia and Taiwan.
Starlink Routers are made in Texas, not subject to the ban.
Razer Dual headquarters in California and Singapore, likely subject to the ban.
Synology Headquartered in Taiwan, subject to the ban.
TP-Link Planning to establish US-based manufacturing, the company said the move is a “positive step.” Currently subject to the ban.

A Netgear representative told CNET in an email that the company commends the Trump administration and the FCC for their action toward a safer digital future. “As a US-founded and headquartered company with a legacy of American innovation, Netgear has long invested in security‑first design, transparent practices, and adherence to government regulations, and we will continue to do so,” the representative said.

TP-Link Systems Inc. also applauded the order. “Placing all manufacturers and their supply chains under the same scrutiny is a positive step in the direction of making the router industry more secure,” a TP-Link Systems representative told CNET in an email. According to the representative, the company had already been planning to establish US-based manufacturing. TP-Link says on its website that it has manufactured all products sold in the US in Vietnam since 2018.  

CNET also reached out to Asus, D-Link, Eero, Linksys, Nest, Razer and Synology, but has not yet received responses. 

How to protect yourself if you have a foreign-made router

Router manufacturers aren’t always the most transparent about their supply chains, but unless you use a Starlink router, some component of your router’s manufacturing likely takes place outside the US. 

“Vulnerabilities don’t have an inclination towards a national origin,” Shankar told me. “It doesn’t matter if it’s a Chinese-made router or an American-made router if a user does not change a default password.” 

No matter where it’s from, your router will be far more secure if you follow some basic best practices. Here’s what experts recommend: 

  • Keep your firmware up to date: One of the most common ways malicious actors access your network is through outdated firmware. You can ensure your router has the latest firmware by enabling automatic updates in your router’s settings or manually downloading updates in the app or web portal.  
  • Strengthen your credentials: If you’ve never changed the default login credentials on your router, now’s the time to do it. Weak passwords are the cause of many common attacks. “Devices using default or weak passwords are easy targets,” Itay Cohen, a security researcher at Palo Alto Networks, told me in a previous interview. “Default or simple passwords can be easily brute-forced or guessed.” Most routers have an app that lets you update your login credentials from there, but you can also type your router’s IP address into a URL. These credentials differ from your Wi-Fi name and password, which should also be changed every 6 months or so. The longer and more random your password, the better
  • Consider using a VPN: For an added layer of protection, a virtual private network encrypts all your internet traffic and prevents your internet provider (or anyone else) from tracking the websites or apps you use. You can find CNET’s picks for the best VPN services here





Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews


What is Big Data Modeling?

Data modeling is the method of constructing a specification for the storage of data in a database. It is a theoretical representation of data objects and relationships between them. The process of formulating data in a structured format in an information system is known as data modeling. It facilitates data analysis, which will aid in meeting business requirements.

Data modeling necessitates data modelers who will work closely with stakeholders and potential users of an information system. The data modeling method ends in developing a data model that supports the business information system’s infrastructure. This method also entails comprehending an organization’s structure and suggesting a solution that allows the organization to achieve its goals. It connects the technological and functional aspects of a project.

Why is Data Modeling necessary?

To ensure that we can easily access all books in a library, we must classify them and place them on racks. Likewise, if we have a lot of info, we’ll need a system or a process to keep it all organized. “Data modeling” refers to the method of sorting and storing data.”

A data model is a system for organizing and storing data. A data model helps us organise data according to service, access, and usage, just like the Dewey Decimal System helps us organise books in a library. Big data can benefit from appropriate models and storage environments in the following ways:

Performance: Good data models will help us quickly query the data we need and lower I/O throughput.

Cost: Good data models can help big data systems save money by reducing unnecessary data redundancy, reusing computing results, and lowering storage and computing costs.

Efficiency: Good data models can significantly enhance user experience and data utilization performance.

Quality: Good data models ensure that data statistics are accurate and that computing errors are minimized.

As a result, a big data system unquestionably necessitates high-quality data modeling methods for organizing and storing data, enabling us to achieve the best possible balance of performance, cost, reliability, and quality.

Why use a Data Model?

Data Model

  • Data interpretation can be improved by using a visual representation of the data. It gives developers a complete image of the data, which they can use to build a physical database.
  • The model correctly depicts all of an organization’s essential data. Data omission is less likely thanks to the data model. Data omission can result in inaccurate results and reports.
  • The data model depicts a clearer picture of market requirements.
  • It aids in developing a tangible interface that unifies an organization’s data on a single platform. It also aids in the detection of redundant, duplicate, and incomplete data.
  • A competent data model aids in ensuring continuity across all of an organization’s projects.
    It enhances the data’s quality.
  • It aids Project Managers in achieving greater reach and quality control. It also boosts overall performance.
  • Relational tables, stored procedures, and primary and foreign keys are all described in it.

Data Model Perspectives

Conceptual, logical, and physical data models are the three types of data models. Data models are used to describe data, how it is organized in a database, and how data components are related to one another.

Data Model Perspective

Conceptual Model

This stage specifies what must be included in the model’s configuration to describe and coordinate market principles. It focuses primarily on business-related entries, characteristics, and relationships. Data Architects and Business Stakeholders are mainly responsible for its development.

The Conceptual Data Model is used to specify the scope of the method. It’s a tool for organizing, scoping, and visualizing company ideas. The aim of developing a computational data model is to develop new entities, relationships, and attributes. Data architects and stakeholders typically create a computational data model.

The Conceptual Data Model is held by three key holders.

  • Entity: A real-life thing
  • Attribute: Properties of an entity
  • Relationship: Association between two entities

Let’s take a look at an illustration of this data model.

Consider the following two entities: product and customer. The Product entity’s attributes are the name and price of the product, while the Customer entity’s attributes are the name and number of customers. Sales is the connection between these two entities.

  • The Conceptual Data Model was created with a corporate audience in mind.
  • It offers an overview of corporate principles for the whole organization.
  • It is created separately, with hardware requirements such as location and data storage space and software requirements such as technology and DBMS vendor.

Conceptual Models

Logical Model

The conceptual model lays out how the model can be put into use. It encompasses all types of data that must be captured, such as tables, columns, and so on. Business Analysts and Data Architects are the most prominent designers of this model.

The Logical Data Model is used to describe the arrangement of data structures as well as their relationships. It lays the groundwork for constructing a physical model. This model aids in the inclusion of extra data to the conceptual data model components. There is no primary or secondary key specified in this model. This model helps users to update and check the connector information for relationships that have been set previously.

The logical data model describes the data requirements for a single project, but it may be combined with other logical data models depending on the project’s scope. Data attributes come with a variety of data types, many of which have exact lengths and precisions.

  • The logical data model is created and configured separately from the database management system.
  • Data Types with accurate dimensions and precisions exist for data attributes.
  • It specifies the data needed for a project but, depending on the project’s complexity, interacts with other logical data models.

Logical Model

Top 80+ frequently asked Data Modeling Interview questions!

Big Data Hadoop Training

  • Master Your Craft
  • Lifetime LMS & Faculty Access
  • 24/7 online expert support
  • Real-world & Project Based Learning

Physical Model

The physical model explains how to use a database management system to execute a data model. It lays out the process in terms of tables, CRUD operations, indexes, partitioning, etc. Database Administrators and Developers build it. 

The Physical Data Model specifies how a data model is implemented in a database. It attracts databases and aids in developing schemas by duplicating database constraints, triggers, column keys, other RDBMS functions, and indexes. This data model aids in visualizing the database layout. Views, access

profiles, authorizations, primary and foreign keys, and so on are all specified in this model.

The majority and minority relationships are defined in the Data Model by the relationship between tables. It is created for a specific version of a database management system, data storage, and project site.

  • The Physical Data Model was created for a database management system (DBMS), data storage, and a project site.
  • It contains table relationships that address the nullability and cardinality of the relationships.
  • Views, access profiles, authorizations, primary and foreign keys, and so on are all specified here.

Physical Model

Realted Article: CAP Theorem in Big Data!

Types of Data Models

While there are several different data modeling approaches, the basic principle remains the same with all models. Let’s take a look at some of the most commonly used data models:

Hierarchical Model

This is a database modeling technique that uses a tree-like structure to organise data. Each record in this table has a single root or parent. When it comes to sibling documents, they’re organized in a specific way. This is the physical order in which the information is stored. This method of modeling can be applied to a wide range of real-world model relationships. This database model was popular in the 1960s and 1970s. However, owing to inefficiencies, they are still used infrequently.

The hierarchical model is used to assemble data into a tree-like structure with a single root that connects all of the data. A single root like this evolves like a branch, connecting nodes to the parent nodes, with each child node having just one parent node. The data is structured in a relational system with a one-to-many relationship between two different data types in this model. For example, in a college, a department consists of a set of courses, professors, and students.

Hierarchical Models

Relational Model

In 1970, an IBM researcher suggested this as a possible solution to the hierarchical paradigm. The data path does not need to be defined by developers. Tables are used to merge data segments in this case directly. The program’s complexity has been minimized due to this model. It necessitates a thorough understanding of the organization’s physical data management strategy. This model was quickly merged with Structured Query Language after its introduction (SQL).

A typical field maintains the Relational Model aids in the organization of two-dimensional tables and the interaction. Tables are the data structure of a relational data model. The table’s rows contain all of the information for a given category. In the Relational Model, these tables are referred to as relations.

Relational Models

Network Model

The Network Model is an enhancement of the Hierarchical Model, allowing for various relationships with related records, implying multiple parent records. It will enable users to build models using sets of similar documents following mathematical set theory. A parent record and the number of child records are included in this set. Each record is a member of several sets, allowing the model to define complex relationships. The model can express complex relationships since each record can belong to several sets.

Network Models

Object-oriented Database Model

A set of objects are aligned with methods and functions in the Object-oriented Database. There are characteristics and methods associated with these objects. Multimedia databases, hypertext databases, and other types of object-oriented databases are available. Even if it incorporates tables, this type of database model is known as a post-relational database model since it is not limited to tables. These database models are referred to as hybrid models.

Wish to make a career in the world of Datastage? Start with Datastage Online Training!

Cloud Computings, big-data-modeling-description-4, Cloud Computings, big-data-modeling-description-9

Subscribe to our YouTube channel to get new updates..!

Entity–Relationship Model

The Entity-Relationship Model (ERM) is a diagram that depicts entities and their relationships. The E-R model generates an entity set, attributes, relationship set, and constraints when constructing a real-world scenario database model. The E-R diagram is a graphical representation of this kind.

An entity may be an object, a concept, or a piece of data stored in relation to the data. It has properties called attributes, and a set of values called domain defines each attribute. A relationship is a logical connection between two or more entities. These connections are mapped to entities in several ways.

Consider a College Database, where a Student is an entity, and the Attributes are Student details such as Name, ID, Age, Address, and so on. As a result, there will be a relation between them.

Entity–Relationship Model

Object-relational Model

The object-relational model can be thought of as a relational model with enhanced object-oriented database model features. This kind of database model enables programmers to integrate functions into a familiar table structure.

An Object-relational Data Model combines the advantages of both an Object-oriented and a Relational database model. It supports classes, objects, inheritance, and other features similar to the Object-oriented paradigm and data types, tabular structures, and other features similar to the Relational database model. Designers may use this model to integrate functions into table structures.

Facts and Dimensions

To understand data modelling, one must first grasp its facts and dimensions.

Fact Table: It’s a table that lists all of the measurements and their granularity. Sales, for example, maybe additive or semi-additive.

Dimension Table: It’s a table containing fields with definitions of market elements and is referenced by several fact tables.

Dimensional Modeling: Dimensional modeling is a data warehouse design methodology. It makes use of validated measurements and facts and aids in navigation. The use of dimensional modeling in performance queries speeds up the process. Star schemas are a colloquial term for dimensional models.

Dimensional Modeling-Related Keys

While learning data modeling, it’s critical to understand the keys. There are five different types of dimensional modelling keys.

  • Business or Natural Keys: It is a field that uniquely defines an individual. Customer ID, employee number, and so on.
  • Primary and Alternate Keys: A primary key is an area that contains a single unique record. The consumer must choose one of the available primary keys, with the others being alternative keys.
  • Composite or Compound Keys: A composite key is one in which more than one field is used to represent a key.
  • Surrogate Keys: It is usually an auto-generated field with no business meaning.
  • Foreign Keys: It is a key that refers to another key in some other table.

The process of data modeling entails the development and design of various data models. A data definition language is then used to convert these data models. A database is created using a data definition language. This database will be referred to as a wholly attributed data model at that stage.

Benefits and Drawbacks of Data Models

Benefits:

  • With data modeling, the functional team’s data objects are appropriately presented.
  • Data modeling enables you to query data from a database and generate various reports from it. With the aid of reports, it indirectly contributes to data analysis. These reports can be used to improve the project’s quality and efficiency.
  • Businesses have a large amount of data in various formats. For such unstructured data, data modeling offers a structured framework.
  • Data modeling enhances business intelligence by requiring data modelers to work closely with the project’s realities, such as data collection from various unstructured sources, reporting specifications, spending patterns, and so on.
  • It improves coordination within the business.
  • The documentation of data mapping is aided during the ETL method.

Drawbacks:

  • The development of a data model is a time-consuming process. Should understand the physical characteristics of data storage.
  • This method necessitates complex application creation as well as biographical truth information.
  • The model isn’t particularly user-friendly. Small improvements in the method require a significant rewrite of the entire application.

Big Data Hadoop Training

Weekday / Weekend Batches

Conclusion

Data models are created to store data in a database. The primary goal of these data models is to ensure that the data objects generated by the functional team are correctly denoted. As previously stated, even the little improvement in the system necessitates improvements to the entire model. Despite the problems, the data modelling concept is the first and most important step of database design since it describes data entities, relationships between data objects, and so on. A data model discusses the data’s market rules, government regulations, and regulatory enforcement in a holistic manner.

Related Article:



Source link