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





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What is Data Science?

Data science is the study of how to gain insightful knowledge from data for business choices, developing strategies, and other reasons utilizing state-of-the-art analytical technologies and scientific ideas. Businesses are becoming aware of its significance: among other things, data science insights assist companies in improving their marketing and sales efforts as well as operational effectiveness. They might eventually give you a competitive edge over other businesses.

Data Science combines a number of fields, including statistics, mathematics, software programming, predictive analytics, data preparation, data engineering, data mining, machine learning, and data visualization. Skilled data scientists are generally responsible for it, however, entry-level data analysts may also be engaged. Additionally, a growing number of firms now depend in part on citizen data scientists, a category that can encompass data engineers, business intelligence (BI) specialists, data-savvy business users, business analysts, and other employees without a formal experience in Data Science.

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What is Linear Algebra

Within Data Science and ML, linear algebra is a field of mathematics that is very helpful. In machine learning, linear algebra is perhaps the most crucial math concept. The vast majority of machine learning models may be written as matrices. A matrix is a common way to represent a dataset. The preprocessing, transformation, and assessment of data and models require linear algebra.

A study of linear algebra may involve the following:

  • Vectors
  • Matrices
  • Transpose of a matrix
  • The inverse of a matrix
  • Determinant of a matrix
  • Trace of a matrix
  • Dot product
  • Eigenvalues
  • Eigenvectors

Why learn Linear Algebra in Data Science?

One of the fundamental building elements of Data Science is linear algebra. Without a solid foundation, you cannot erect a skyscraper, can you? Try to picture this example:

You wish to use Principal Component Analysis to minimize the dimensionality of your data (PCA). If you were unsure of how it would impact your data, how would you choose how many Principal Components to keep? Obviously, in order to make this choice, you must be familiar with the workings of the algorithm.

You will be able to gain a better sense for ML and deep learning algorithms and stop treating them as mysterious black boxes if you have a working knowledge of linear algebra. This would enable you to select suitable hyperparameters and create a more accurate model. Additionally, you would be able to develop original algorithms and algorithmic modifications.

Linear Algebra Applications for Data Scientists

We will now learn more about the most common application of linear algebra for data scientists:

Machine learning: loss functions and recommender systems

Without a question, the most well-known use of artificial intelligence is machine learning (AI). Systems automatically learn and get better with experience employing machine learning algorithms, free from human intervention. In order to detect trends and learn from them, machine learning works by creating programs that access and analyze data (whether static or dynamic). The algorithm can use this expertise to analyze fresh data sets once it has identified relationships in the data. (See this page for more information on how algorithms learn.)

Machine learning uses linear algebra in many different ways, including loss functions, regularization, support vector classification, and plenty more.

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Loss Function

Machine learning algorithms function by gathering data, interpreting it, and then creating a model via various techniques. They can then forecast upcoming data queries depending on the outcomes.

Now, we may assess the model’s correctness by utilizing linear algebra, specifically loss functions. In a nutshell, loss functions provide a way to assess the precision of the prediction models. The output of the loss function will be greater if the model is completely incorrect. In contrast, a good model will cause the function to return a lower value.

Modeling a link involving a dependent variable, Y, and numerous independent variables, Xi’s, is known as regression. We attempt to build a line in place on these variables upon plotting these points, and we utilize this line to forecast future values of Xi’s.

The two most often used loss functions are mean squared error and mean absolute error. There are many different forms of loss functions, many of which are more complex than others.

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Recommender System

A subset of machine learning known as recommender systems provides consumers with pertinent suggestions based on previously gathered data. In order to forecast what the present user (or a new user) might like, recommender systems employ data from the user’s prior interactions with the algorithm focused on their interests, demographics, and other available data. By tailoring material to each user’s tastes, businesses can attract and keep customers.

The performance of recommender systems depends on two types of data being gathered: 

Characteristic data: Knowledge of things, including location, user preferences, and details like their category or price.

User-item interactions: Ratings and the volume of transactions (or purchases of related items).

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Natural language processing: word embedding

Artificial intelligence’s Natural Language Processing (NLP) field focuses on how to connect with people through natural language, most frequently English. Applications for NLP encompass textual analysis, speech recognition, and chatbot.

Applications such as Grammarly, Siri, and Alexa are all based on the concept of NLP.

Word embedding

Text data cannot be understood by computers, not by its own. We use NLP algorithms on text since we need to mathematically express the test data. The use of algebra is now necessary. A sort of word representation known as word embedding enables ML algorithms to comprehend terms with comparable meanings.

With the backdrop of the words still intact, word embeddings portray words as vectors of numbers. These representations are created using the language modeling learning technique of training various neural networks on a huge corpus of text. Word2vec is among the more widely used word embedding methods.

Computer vision: image convolution

Using photos, videos, and deep learning models, the artificial intelligence discipline of computer vision teaches computers to comprehend and interpret the visual environment. This enables algorithms to correctly recognize and categorize items. 

In applications like image recognition as well as certain image processing methods like image convolution and image representation like tensors, we utilize linear algebra in computer vision.

Image Convolution

Convolution results from element-wise multiplying two matrices and then adding them together. Consider the image as a large matrix and the kernel (i.e., convolutional matrix) as just a tiny matrix used for edge recognition, blurring, as well as related image processing tasks. This is one approach to conceiving image convolution. As a result, this kernel slides over the image from top to bottom and from left to right. While doing so, it performs arithmetic operations at every image’s (x, y) location to create a distorted image.

Different forms of image convolutions are performed by various kernels. Square matrices are always used as kernels. They are frequently 3×3, however, you can change the form depending on the size of the image.

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Where do we use linear algebra in Data Science?

Data Scientists often make use of Linear Algebra for various applications including:

  • Vectorized Code: To create vectorized codes that are relatively more effective than their non-vectorized counterparts, linear algebra is helpful. This is so that results from vectorized codes can be produced in a single step instead of results from non-vectorized codes, which frequently involve numerous steps and loops.
  • Dimensionality Reduction: In the preparation of data sets required for machine learning, dimensionality reduction is a crucial step. This is particularly true for big data sets or those with many attributes or dimensions. Many of these characteristics may occasionally have a strong correlation with one another.

The speed and effectiveness of the ML algorithm are improved by doing dimensionality reduction on a big data set. This is due to the fact that the algorithm only needs to consider a small number of features before producing a forecast.

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Concepts of linear algebra for Data Science

Linear Algebra for Data Preprocessing – Linear algebra is used for data preprocessing in the following way:

  • Import the required libraries for linear algebra such as NumPy, pandas, pylab, seaborn, etc.
  • Read datasets and display features
  • Define column matrices to perform data visualization

Covariance Matrix– One of the most crucial matrices in Data Science and ML is the covariance matrix. It offers details on the co-movement (correlation) of characteristics. We can create a scatter pair plot to see how the features are correlated. One could construct the covariance matrix to determine the level of multicollinearity or correlation between characteristics. The covariance matrix could be written as a symmetric and real 4 x 4 matrix.
A unitary transformation, commonly known as a Principal Component Analysis (PCA) transformation, can be used to diagonalize this matrix. We note that the sum of the diagonal matrix’s eigenvalues equals the total variance stored in features because the trace of a matrix stays constant during a unitary transformation.

Linear Discriminant Analysis Matrix – The Linear Discriminant Analysis (LDA) matrix is another illustration of a realistic and symmetrical matrix in Data Science. This matrix could be written as follows

Linear Discriminant Analysis Matrix

where SW stands for the scatter matrix within the feature and SB for the scatter matrix between the feature. It implies that L is real and symmetric because the matrices SW & SB are also realistic and symmetrical. A feature subspace with improved class separability and decreased dimensionality is created by diagonalizing L. So, whereas PCA is not a supervised method, LDA is.

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Conclusion

Often a skipped-over concept due to premeditated assumptions of difficulty, a good hold over linear algebra could help build a crucial foundation for those aspiring to have flourishing careers in Data Science.

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