Trending February 2024 # Simplewall Is A Simple Tool To Block Applications From Using The Internet # Suggested March 2024 # Top 7 Popular

You are reading the article Simplewall Is A Simple Tool To Block Applications From Using The Internet updated in February 2024 on the website Eastwest.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Simplewall Is A Simple Tool To Block Applications From Using The Internet

A Firewall is a very useful piece of software that monitors all incoming and outgoing network activity. By monitoring, you can set up rules and blocklists to filter network activity and ban any application or service from accessing the internet. Firewalls have always been a little difficult to configure and use. The main question always remains, how to block an application from accessing the internet. To answer this question, we’ve covered an awesome tool called SimpleWall that makes it super simple to setup your Firewall settings.

SimpleWall software for Windows PC

SimpleWall is not a firewall software; rather it uses Windows Filtering Platform under the hood. Which means, it is configuring the Windows Firewall with your settings behind the scenes.

Using SimpleWall is quite easy, first, you need to decide the mode you want to run this program in. Either you can run it in whitelist mode, which means only the application you select will be allowed to use the internet. Or you can run it in blacklist mode, which would block the applications that you’ve selected.

Once the filtering has been enabled, no application in the blocked list can access the internet. If any such application tries connecting to the internet, you will be notified with a small notification just above the system tray. The notification itself will give you a lot of information and provide buttons for relevant actions. You can allow an application from the notification or continue to block it. Or you may create an outbound rule for an IP address or port. And lastly, you can completely disable notifications for an app.

The application also comes with a purge option. Where you can set a purge timer and all the unused and invalid applications will be removed from SimpleWall after a given duration of time.

There are great import/export features available as well. You can export your application list and rules and later import them into a different or same computer. The import/export functionality can help you maintain a various set of configurations for different scenarios you might face daily. For instance, you can create one set of configuration when kids are using the computer and the other one when you are using.

Just like its name, the interface is also quite simple to use as well. It comes with various highlighting options to highlight different types of applications such as system, signed the application, windows service, etc. Moreover, you can enable other features such as Always on top, Load on system startup and Start minimized.

The application also maintains a log for dropped packages which can be accessed at a specified location. You can also enable notifications for dropped packages and finally, you can customize notification sound and its display timeout.

SimpleWall is a very simple firewall tool. If you want to block some applications from using the internet, you can quickly do that using SimpleWall. It is simple, convenient and gets the job done quickly. Additionally, the features such as notifications, purge timer, and system rules make this tool more preferable over its counterparts.

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From Secrecy To #Witchtok: How The Internet Is Demystifying Africana Religions

From Secrecy to #WitchTok: How the Internet Is Demystifying Africana Religions

Margarita Guillory, Boston University College of Arts & Sciences associate professor of religion and African American studies, looks at the how religion functions in the everyday lives of African Americans, particularly through practices tracing back to religious and cultural systems from Africa. Photo by Cydney Scott

Africana Religion

From Secrecy to #WitchTok: How the Internet Is Demystifying Africana Religions BU researcher discusses the emergence of online communities dedicated to Africana religious practices on Tumblr, Twitter, Instagram, and TikTok

Before Margarita Guillory started studying religion and spiritualism, she was a high school chemistry teacher. For over 10 years, Guillory ignited her love for teaching through experimentation and hypothesis. Outside of the evidence-based approach she took in the classroom, she was always curious about the kinds of things that can’t be seen in a beaker or through a microscope—like faith.

Although she did not grow up in a religious household, her aunts would take her along with them to visit Christian churches in Mobile, Ala., where she grew up. As a child, Guillory was awed by how her family and people in the community—many who were in the depths of poverty—could, within the walls of church, transition to a spiritual place where they could escape the realities of everyday life. And when they were not at church, she would see women in her family perform non-Christian religious rituals in secret, many of which revolved around healing, protection, and financial security. 

“What I saw demonstrated was the power of African American women to transform their lives by manipulating material objects,” says Guillory, now a Boston University College of Arts & Sciences associate professor of religion and of African American studies. 

After 10 years teaching high schoolers about the “hard” science of chemistry, Guillory was compelled to research and explore the lesser-understood phenomena she had seen as a child. Since then, she has studied how religion functions in the everyday lives of African Americans, particularly through practices tracing back to religious and cultural systems from Africa.

“My goal has always been to challenge monolithic understandings of African American religiosities,” says Guillory, who was named a 2023/2024 Jeffrey Henderson Senior Research Fellow at BU’s Center for the Humanities. In her upcoming book, Africana Religion in the Digital Age, she takes a close look at how religious communities and individuals have transformed their practices in the age of the internet, and the many ways religion and spirituality have emerged across digital platforms ranging from TikTok to video games.  

Guillory’s research is particularly focused on religions such as New Orleans Voodoo, Haitian Vodou, Hoodoo, Cuban Santeria, and others that trace their roots to African religious systems. Researchers have linked Yoruba practices, which include religions practiced most widely in Benin and Western Nigeria, to the rise of many lasting spiritual traditions that formed among people enslaved in the Americas. While Christianity is the most widely practiced religion among Black Americans, Guillory says African Americans are more and more infusing Christianity with Africana-based beliefs, such as witchcraft, or leaving the church community altogether. 

Though Africana conjuring practices have long been present in African American communities in the United States, many times those rituals were performed in secrecy, like Guillory observed her own aunts doing. This was a way of protecting themselves from false assumptions and is also the nature of the rituals, which are intended to be private. 

“Voodoo is a very complex religion,” Guillory says, in which divine intermediaries govern interactions between an overarching Creator and humankind. Voodoo became popular in the Mississippi River Valley in the 17th century and shares commonalities with Haitian Vodou (which is spelled slightly differently).

Newspaper articles from the 1700s and 1800s, Guillory says, intentionally used words like “witchcraft” to invoke fear and cast conjuring traditions as evil, flattening Voodoo and different beliefs under the same umbrella. These harmful ideas still circulate today in popular culture. For example, in the 1999 video game Shadow Man, spiritual powers are bequeathed to voodoo warriors to protect the living from the dead. The idea of a voodoo doll, the most common stereotypical notion about Africana religious practices, was even referenced in the recent Netflix documentary The Social Dilemma to describe how digital platforms seek to control people’s attention. 

But thanks to the internet, the genuine beliefs and practices of Africana religions are coming to light. “Technology has made these practices more visible, particularly among millennials and Gen Zs,” Guillory says.

Today, a number of public online communities dedicated to Africana practices have emerged in places like Tumblr, Twitter, Instagram, and recently TikTok, where the tag #WitchTok has gained over 1.7 billion views. These groups, she says, “are using technology to say what authenticity is all about. It gives them the freedom to practice how they want to practice.” 

And in academia and research, work is being done to make the study of Africana religions more accessible to all. It’s traditionally been difficult to find reputable academic sources about the history of Africana religions. To fill that dearth, Guillory is creating the first-of-its-kind Database of Africana Religious Experiences in the US (DARE-US), a database dedicated to 19th- and 20th-century African American religious history. She envisions the database not just catering to scholars, but to anyone who is interested in learning more about religious studies, or even the church down their street. 

“It’s not just religion, these documents talk about politics and business, especially because religious spaces were so important for social and economic empowerment for African Americans,” she says. Once completed, DARE-US will be the only database of its kind dedicated to Africana religion, serving as a way of digitally preserving history for the next generation of scholars and practitioners.

Explore Related Topics:

Simple Image Classification Using Fastai.jl

The fastai library is now on Julia with similar features available in Python. In this project, we are going to train the Resnet-18 model to classify images from the ImageNet dataset in simple steps.

 Source: Author

Introduction to FastAI.jl

The chúng tôi library is similar to the chúng tôi library in Python and it’s the best way to experiment with your deep learning projects in Julia. The library allows you to use state-of-the-art models that you can modify, train, and evaluate by using few lines of code. The chúng tôi provides a complete ecosystem for deep learning which includes computer vision, Natural Language processing, tabular data, and more submodules are added every month FastAI (fluxml.ai).

Image 1

Getting Started with chúng tôi in Julia

For more detail visit Quickstart (fluxml.ai) as the code used in this project is driven from fastai documentation.

Local Setup

If you have Julia installed in your system, then type:

using Pkg Pkg.add("FastAI") Pkg.add("CairoMakie")

As shown in the image below 👇

Image by author

Deepnote

For the Deepnote environment, you have to create a docker file and add:

FROM gcr.io/deepnote-200602/templates/deepnote tar -xvzf julia-1.6.2-linux-x86_64.tar.gz && sudo mv julia-1.6.2 /usr/lib/ && sudo ln -s /usr/lib/julia-1.6.2/bin/julia /usr/bin/julia && rm julia-1.6.2-linux-x86_64.tar.gz && julia -e "using Pkg;pkg"add IJulia LinearAlgebra SparseArrays Images MAT"" ENV DEFAULT_KERNEL_NAME "julia-1.6.2" Google Colab

For Google Colab you can follow my repo on GitHub or just create a Julia environment by adding an additional cell shown below. Installing Julia packages may take up to 15 minutes.

Change Runtime to GPU for faster results.

Execute the code below.

Reload this page by pressing F5.

%%shell set -e JULIA_VERSION="1.6.2" export JULIA_PACKAGES="CUDA IJulia CairoMakie" JULIA_NUM_THREADS="2" if [ -n "$COLAB_GPU" ] && [ -z `which julia` ]; then # Install Julia JULIA_VER=`cut -d '.' -f -2 <<< "$JULIA_VERSION"` echo "Installing Julia $JULIA_VERSION on the current Colab Runtime..." URL="$BASE_URL/$JULIA_VER/julia-$JULIA_VERSION-linux-x86_64.tar.gz" wget -nv $URL -O chúng tôi # -nv means "not verbose" tar -x -f chúng tôi -C /usr/local --strip-components 1 rm /tmp/julia.tar.gz # Install Packages echo "Installing Julia packages, this may take up to 15 minutes. " # Install kernel and rename it to "julia" echo "Installing IJulia kernel..." julia -e 'using IJulia; IJulia.installkernel("julia", env=Dict( KERNEL_DIR=`julia -e "using IJulia; print(IJulia.kerneldir())"` KERNEL_NAME=`ls -d "$KERNEL_DIR"/julia*` mv -f $KERNEL_NAME "$KERNEL_DIR"/julia echo '' echo "Success! Please reload this page and jump to the next section." fi Implementation of Image Classification Using FastAI.jl

Checking the version to make sure you have a similar version as mine.

versioninfo() Julia Version 1.6.2 Commit 1b93d53fc4 (2024-07-14 15:36 UTC) Platform Info: OS: Linux (x86_64-pc-linux-gnu) CPU: Intel(R) Xeon(R) CPU @ 2.30GHz WORD_SIZE: 64 LIBM: libopenlibm LLVM: libLLVM-11.0.1 (ORCJIT, haswell) Environment: JULIA_NUM_THREADS = 2 Import the libraries using FastAI import CairoMakie Download the Dataset

We are using `loaddataset` function to import the local dataset from fastai. As you can see using a single line of code can download the dataset and provide you with access to all images location with labels.

data, blocks = loaddataset("imagenette2-160", (Image, Label)) ┌ Info: Downloading │ dest = /root/.julia/datadeps/fastai-imagenette2-160/imagenette2-160.tgz │ progress = 1.0 │ time_taken = 2.69 s │ time_remaining = 0.0 s │ average_speed = 35.138 MiB/s │ downloaded = 94.417 MiB │ remaining = 0 bytes │ total = 94.417 MiB └ @ HTTP /root/.julia/packages/HTTP/5e2VH/src/download.jl:128

The data contains the location of images, and the block contains structure and images labels.

data (mapobs(loadfile, ["/root/.julia/datadeps/fastai-imagenette2-160/imagenette2-160/train/n01440764/I…]), mapobs(parentname, ["/root/.julia/datadeps/fastai-imagenette2-160/imagenette2-160/train/n01440764/I…])) blocks (Image{2}(), Label{String}(["n01440764", "n02102040", "n02979186", "n03000684", "n03028079", "n03394916", "n03417042", "n03425413", "n03445777", "n03888257"])) Exploring Image for Image Classification Using FastAI.jl

By using getobs we can check a single sample from our data and check the class. In our case the class name is n01440764 you can also rename the class as fish but for simplicity, we will be using a unique Id for the class. We can also see the image in sample 500.

image, class = sample = getobs(data, 500) @show class image

OR

We can check multiple photos with labels by using `getobs` and `plotsamples`.

idxs = rand(1:nobs(data), 9) samples = [getobs(data, i) for i in idxs] plotsamples(method, samples) Image Classification Function

In order to load our Image data, we need to first create a function that will transform images to 128X128 and preprocesses them to randomly augmentation (sheer, stretch, rotate, flip). For labels, it will use one-hot encoding to convert string labels into integers.

method = BlockMethod( blocks, ( ProjectiveTransforms((128, 128)), ImagePreprocessing(), OneHot() ) )

OR

You can just use the ImageClassificationSingle function to reproduce equivalent results.

method = ImageClassificationSingle(blocks) Building model for Image Classification Using FastAI.jl

First, we need to use a data loader to load image links and use the transformation method. This will convert images and Labels into integer data.

`methodmodel` takes the method from the image loader and ResNet architect to build a model for training.

we can simply create a loss function by adding the method into `metholossfn`. By default, the loss function is `CrossEntropy`.

By using `Learner` we combine, method, data loader, optimizer which is ADAM, loss function, and Metric which is accuracy in our case.

dls = methoddataloaders(data, method) model = methodmodel(method, Models.xresnet18()) lossfn = methodlossfn(method) learner = Learner(model, dls, ADAM(), lossfn, ToGPU(), Metrics(accuracy))

OR

We can simply do all the above steps by using a single line of code as shown below.

learner = methodlearner(method, data, Models.xresnet18(), ToGPU(), Metrics(accuracy)) Training and Evaluation of Image Classification Model

We will be training our model on 10 Epochs with a 0.002 learning rate. As we can see our training and validation loss have decreased with every iteration but after the 5th Epoch, the validation loss has become steady.

Final Metrics:

Training = Loss:  0.07313 │ Accuracy:  98.38%

Validation = Loss: 0.59254 │ Accuracy: 83.27%

This is not bad as we haven’t cleaned our data or performed hyperparameters tuning.

fitonecycle!(learner, 10, 0.002)

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 1.0 │ 1.75345 │ 0.43189 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 1.0 │ 1.54432 │ 0.53701 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 2.0 │ 1.37105 │ 0.56158 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 2.0 │ 1.23731 │ 0.62673 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 3.0 │ 1.09463 │ 0.65127 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 3.0 │ 1.11761 │ 0.65932 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 4.0 │ 0.87518 │ 0.71549 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 4.0 │ 0.95252 │ 0.6995 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 5.0 │ 0.70251 │ 0.77235 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 5.0 │ 0.77653 │ 0.75314 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 6.0 │ 0.52883 │ 0.82565 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 6.0 │ 0.76827 │ 0.7698 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 7.0 │ 0.35174 │ 0.88405 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼────────┼──────────┤

│ ValidationPhase │ 7.0 │ 0.7575 │ 0.77955 │

└─────────────────┴───────┴────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 8.0 │ 0.20262 │ 0.93675 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 8.0 │ 0.63952 │ 0.81444 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 9.0 │ 0.11359 │ 0.96763 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 9.0 │ 0.59102 │ 0.82806 │

└─────────────────┴───────┴─────────┴──────────┘

┌───────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├───────────────┼───────┼─────────┼──────────┤

│ TrainingPhase │ 10.0 │ 0.07313 │ 0.98377 │

└───────────────┴───────┴─────────┴──────────┘

┌─────────────────┬───────┬─────────┬──────────┐

│ Phase │ Epoch │ Loss │ Accuracy │

├─────────────────┼───────┼─────────┼──────────┤

│ ValidationPhase │ 10.0 │ 0.59254 │ 0.83267 │

└─────────────────┴───────┴─────────┴──────────┘

Evaluating the model Prediction

As we can see all four photos were predicted correctly. You can also see the prediction but using `plotprediction` method.

plotpredictions(method, learner) Conclusion

In this project, we have built an image classifier model in ImageNet data by using few lines of code and our model has performed above our expectations. We have also discovered different methods to do similar tasks in image classification and how fastai the library is powerful in exploring and predicting images. Overall, I have enjoyed using this library and there is so much to it that I didn’t discuss in this article.

I hope you experiment with this library and showcase amazing projects that are production-ready.

Source Code

The source code is available at GitHub and Deepnote.

You can follow me on LinkedIn and Polywork where I post amazing articles on data science and machine learning.

Image Sources

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

Related

How To Completely Delete Yourself From The Internet

This is the internet age. Every one of us has a footprint on the internet in some way. Either through the government or by personal usage of the internet. No one is immune from the internet age. The regular data leaks from big companies and services that we use expose us to the world. We put most of our details on the internet either through social media or shopping. Have you ever wondered how can you scrub yourself from the internet? In this guide, we show you how to completely delete yourself from the internet.

How to completely delete yourself from the internet

The Internet lures you to post something anywhere in the form of Digital Footprints – and the chances are high that you have done that! But if you now want to scrub or delete yourself completely from the internet, follow the below steps.

Delete Social media accounts

Delete accounts of e-commerce websites

Delete accounts on other services you use

Delete websites or blogs you own

Remove yourself from forums

Search yourself on search engines and remove your info

Opt-out of data brokers

Delete or deactivate email accounts

While some of them may not be practical in today’s times, we have listed them all for you to consider.

1] Delete Social media accounts

All of us use social media to stay up to date with the trends or interact with our friends and family. If you really want to delete yourself from the internet, the first step in the process is to delete or deactivate all your social media accounts. Every social media platform has options in its Privacy or Account Settings to delete or deactivate accounts. Use that feature and delete all your social media accounts.

Read: How to permanently delete your Facebook Account

2] Delete accounts of e-commerce websites

No one is immune to online shopping. Everyone has ordered something during festive offers or some other times. One of the steps you need to take while deleting yourself from the internet is to delete all the accounts on the shopping sites you use. You will find options to delete accounts in the Account settings of each platform. If you do not find an option, you can contact the Customer support of the platform and seek their help to delete your account permanently. Remember, to download the invoices of your purchases before deleting your account permanently from e-commerce platforms as they might be helpful while claiming any warranty of defective products.

Read: How to permanently delete or temporarily disable Instagram account

3] Delete accounts on other services you use

There are a whole lot of other services and apps we use daily to carry out various tasks like Habit trackers, news websites, and ride-sharing services like Uber and others. You need to delete those accounts permanently in the Account settings of each platform. If you do not find an option to delete or deactivate your account on any platform, you can contact customer service and take their help to deactivate the accounts or delete them permanently.

Read: Find out what Google knows about you.

4] Delete websites or blogs you own

If you have a blog or website in your name that you started as a hobby, you have to delete or remove it from the internet to completely delete yourself from the internet. It might be a hard decision, but if you really want to delete yourself from the internet, you have to delete the websites or blogs you own too. If you have an earning blog you can sell them to interested buyers you find on the internet.

Read: What information is available about you on the internet when online.

5] Remove yourself from forums

If you have ever interacted on a forum or community, you have to delete each interaction as well as the account on the forum. It would take some time to delete your interactions but in the process of deleting yourself from the internet, you have to delete all the traces of yours everywhere.

6] Search yourself on search engines and remove your info

You can remove your name and information from Search Engines. Search yourself on various search engines like Google, Bing, DuckDuckGo, and others and find what information you can find yourself on the internet. Find ways to delete them. If something about you is featured on a website, you can request the admin of that website to remove your information. You can also request search engines like Google to delete your information.

7] Opt-out of data brokers

Collecting information about people on the internet and selling it to others is a lucrative thing. There are many services like BeenVerified, Acxiom, and PeopleFinder that collect information and let others see it. You have to request them to delete your personal information from their services using their contact page. Every website contains an option to request content removal. It would take some time, but it is worth a try to delete yourself from the internet. If you cannot delete yourself from the data-broking sites, you can use services like Incogni to opt out of data brokers automatically.

Read: How to remove Personal Information from Internet?

8] Delete or deactivate email accounts

Our email accounts can be termed as a gateway to online accounts. Every person has one or two email accounts. You need to delete them to delete yourself from the internet. Each service has the option to delete email accounts permanently. Use them and delete your accounts on Google, Yahoo, or other email services.

These are the different ways you can use to delete yourself from the internet completely.

Can anything be permanently deleted from the internet?

Yes and no. It depends on what you delete. If you find your personal information on third-party sites, you can request them to delete your data. You can delete your accounts with all the information on social media. The information on the internet gets copied to different websites. It would be a hard task to delete anything from the internet permanently. You need to monitor regularly to delete anything you find about yourself on the internet.

Read: How to best protect your Privacy on the Internet.

How do I delete all traces of Internet activity?

To delete all traces of your internet activity, you can begin by clearing your browsing history on the browsers. Additionally, you can use a VPN to hide your internet activity from the internet service providers. Or you can use the TOR browser, which routes your traffic through different nodes worldwide. You need to compromise internet speed if you use the TOR network.

Is The Internet Of Things (Iot) A Security Risk?

IoT device security has long been a source of worry, and as a result, both small- and large-scale assaults have been made possible. Most of these attacks result from straightforward security issues, like using telnet services’ default passwords being retained. The Dutch Radio Communications Agency approached our Dutch facility, Eurofins Cyber Security, for guidance on best enforcing security requirements on IoT devices and their makers.

IoT security will remain a key concern for manufacturers and end users as more and more enterprises, consumers, and government organizations use and rely on IoT applications. In this post, we examine the definition of IoT security, its importance, and the main threats it faces; consumers and government organizations use and rely on IoT applications. This post examines the definition of IoT security, its importance, and the main threats it faces. Additionally, we go over how to secure networks, data, and devices in IoT situations. The development teams that want to guarantee the appropriate security of their IoT projects will find this article useful.

What do IoT devices Mean?

Since the variety of IoT devices makes the IoT’s reach so broad and its security so difficult, we start by defining the “things” in the “Internet of Things.” A key feature of an IoT device is its ability to connect to the internet and communicate with its surroundings by gathering and exchanging data. Devices frequently only have a few specialized functions and a small amount of computational power. IoT can be used and applied to various surroundings in infinite ways because devices come in such a wide variety. Internet of Things security is a collection of methods and procedures for defending against various IoT security intrusions on the physical objects, networks, operations, and technology that make up an IoT ecosystem.

IoT security’s two main objectives are to −

Ensure that all data is securely gathered, processed, stored, and transferred.

Identify and fix IoT component vulnerabilities.

Why is IoT Security Important?

A key factor in cybersecurity is the pervasiveness of smart gadgets. The productivity of an entire firm may suffer if one of these IoT devices has a vulnerability, which could result in expensive data breaches. IoT security is essential for maintaining data security. Sensitive data can be stored in enormous quantities on smart devices, and this data is subject to special cybersecurity requirements. Legal repercussions may result if this data is compromised and not safeguarded.

Risks to the Internet of Things (IoT) Security Most Frequently Occur

Regarding the Internet of Things, attack surfaces, threat vectors, and vulnerabilities have all received a lot of investigation. The Internet of Things poses several risks that could harm both consumers and organizations. We’ll review 11 of the most prevalent Internet of Things security threats so you can take precautions to safeguard your company and its stakeholders.

Faulty Access Control

IoT services should only be available to networks or individuals the owner trusts. Unfortunately, IoT devices frequently fail to enforce this enough. IoT devices often have an unwarranted level of trust in the networks to which they are linked, often requiring no authentication or authorization. Without any conditions, other network-connected devices are likewise trusted. When such gadgets are connected to the Internet, it becomes concerning as anyone can use the device’s services.

Shoddy Physical Protection

Physical security also poses a big danger regarding IoT device security beyond digital security. Sensitive information is frequently stored on consumer and commercial IoT devices. Sensitive audio or video data relating to the business, the house, or the user is connected to or utilized as wireless network passwords. Attackers with physical access to the devices can open them and disable security software by reading the memory components’ data directly.

Inadequate Privacy Protection

Sensitive data is frequently stored on consumer IoT devices. For instance, any wireless Internet of Things device will save the network’s password. Any IoT gadget that collects video or audio may also contain data about a business, a house, or a user.

Botnets

A group of web-connected devices known as a “botnet” is used to steal data, compromise networks, or send spam. Botnets are one of the most significant corporate hazards since they contain malware that gives attackers access to an IoT device and its connection to a company’s network. They tend to be more noticeable in appliances that weren’t initially made securely (smart fridges, for example). These gadgets are always changing and evolving. It is, therefore, vital to keep an eye on their adjustments and threat levels to prevent attacks.

Device management issues and poor visibility

Many IoT devices are still not tracked, monitored, or controlled correctly. Keeping track of devices as they connect and disconnect from the IoT network can become quite challenging. Organizations may be unable to identify possible dangers or even take action if they lack visibility into device status. When we look at the healthcare industry, we can see how these hazards can become fatal. If not adequately protected, IoT pacemakers and defibrillators have the potential to be tampered with. Hackers might purposely drain batteries or give false pulses and shocks. Device management systems must be implemented by organizations to effectively monitor IoT devices and cover all potential points of vulnerability.

Conclusion

It’s critical to consider security in early research and development phases while developing IoT initiatives. The frequent cyberattacks and difficulty finding potential system vulnerabilities make it difficult to guarantee adequate devices, networks, and data security in IoT contexts.

In IoT projects, it can be challenging to provide strong security measures. Implementing security measures may increase a solution’s cost and development time, which is undoubtedly undesirable for enterprises. This is in addition to hitting hardware constraints. Expert IoT software developers and quality assurance specialists with penetration testing experience are needed to create secure IoT devices.

Access Any Hard Drive From The Internet

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PogoPlug, available in North America as of today, is a cheap, straightforward, single-purpose device that aims to transform network-attached storage into an appliance. It combines any old USB hard drive with your existing Internet connection, and then, voila: everything delicious and convenient about network-attached storage is now within reach.

Here’s how it works.

The PogoPlugis $99, and no bigger than the wall plate for a light switch. It resembles an oversized wall wart (like the one hanging off the end of your cell phone charger).

By design, it’s dead easy. You plug it into the wall, and into your home router via an ethernet cable, and then into an external USB 2.0-compatible hard drive (or even USB thumb drive) which you’ve probably got sitting around anyway (and if you don’t, they can be had very cheaply).

Go online, register your PogoPlug, and voila —  the drive connected to the PogoPlug is now accessible via chúng tôi No setting up IP addresses for your home server, or tunneling through your firewall, or needing a spare computer to use as a media server —  all of that is taken care of by a combination of firmware in the PogoPlug and an independent back-end service running on Cloud Engine’s own servers.

And this is what it does.

Aside from all the things that any network-attached storage device is capable of, the PogoPlug does a number of nifty things, including automatically generating thumbnails for your media and transcoding video on the fly so that it can be streamed to remote devices without you having to wait for the whole thing to download first.

There’s even an iPhone application in case you want to, say, access to every vacation photo you’ve ever taken, ever. Or swap out your tired playlist for some new music, even if you’re in Aruba and your hard drive is in Saskatchewan.

Sharing files with friends is even easier —  you just punch in their email address and the PogoPlug software emails them a link; they don’t even have to register. There’s no backup software specific to the PogoPlug, though, which, unfortunately, means backing up is still a drag-and-drop operation.

Because it’s based on the ultra-low-power Marvell chipset (Marvell works with the same ARM chips that show up in cell phones and portable gaming devices like the Nintendo DS), the PogoPlug draws fewer than 5 watts of power. Most external hard drives are smart enough to turn themselves off after a pre-set period of inactivity, so together the two devices aren’t going to draw a lot of power unless you’re hitting the server all day long —  even then, it’s a lot less than the 20-100 watts that would be required to run a full-blown laptop or tower-based server.

If you want to get really crazy, Cloud Engine’s engineers have apparently created an API for the PogoPlug. This means you could access it from any other website, thus making it a DIY media server. However, that would mean that your home or apartment would then be a DIY colocation facility, which is fine if you’re sharing baby pictures but not so great if you’re hosting business-critical files. Either way, it’s nice to have the versatility, and it probably means hackers will come up with a number of cool, off-label uses for the device.

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