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Introduction

Working on a Project on image recognition or Object Detection but didn’t have the basics to build an architecture?

 In this article, we will see what are convolutional neural network architectures right from basic and we will take a basic architecture as a case study to apply our learnings, The only pre-requisite is you just need to know how convolution works But don’t worry it is very simple !!

Let us take a simple Convolutional neural network,

We will go layer-wise to get deep insights about this CNN.

First, there a few things to learn from layer 1 that is striding and padding, we will see each of them in brief with examples

Let us suppose this in the input matrix of 5×5 and a filter of matrix 3X3, for those who don’t know what a filter is a set of weights in a matrix applied on an image or a matrix to obtain the required features, please search on convolution if this is your first time!

Note: We always take the sum or average of all the values while doing a convolution.

A filter can be of any depth, if a filter is having a depth d it can go to a depth of d layers and convolute i.e sum all the (weights x inputs) of d layers

Here the input is of size 5×5 after applying a 3×3 kernel or filters you obtain a 3×3 output feature map so let us try to formulate this

So the output height is formulated and the same with o/p width also…

While applying convolutions we will not obtain the output dimensions the same as input we will lose data over borders so we append a border of zeros and recalculate the convolution covering all the input values.

We will try to formulate this,

Here 2 is for two columns of zeros along with height and width, and formulate the same for width also

Some times we do not want to capture all the data or information available so we skip some neighboring cells let us visualize it,

Here the input matrix or image is of dimensions 5×5 with a filter of 3×3 and a stride of 2 so every time we skip two columns  and convolute, let us formulate this

If the dimensions are in float you can take ceil() on the output  i.e (next close integer)

Here H refers to height, so the output height is formulated and the same with o/p width also and here 2 is the stride value so you can make it as S in the formulae.

In general terms pooling refers to a small portion, so here we take a small portion of the input and try to take the average value referred to as average pooling or take a maximum value termed as max pooling, so by doing pooling on an image we are not taking out all the values we are taking a summarized value over all the values present !!!

here this is an example of max pooling so here taking a stride of two we are taking the maximum value present in the matrix

The activation function is a node that is put at the end of or in between Neural Networks. They help to decide if the neuron would fire or not. We have different types of activation functions just as in the figure above, but for this post, my focus will be on Rectified Linear Unit (ReLU)

Don’t drop your jaws, this is not that complex this function simply returns 0 if your value is negative else it returns the same value you gave, nothing but eliminates  negative outputs and maintains values between 0 to +infinity

Now, that we have learned all the basics needed let us study a basic neural net called LeNet.

LeNet-5

Before starting we will see what are the architectures designed to date. These models were tested on ImageNet data where we have over a million images and 1000 classes to predict 

What are the inputs and outputs (Layer 0 and Layer N) :

Here we are predicting digits based on the input image given, note that here the image is of dimensions height = 32 pixels, width = 32 pixels, and a depth of 1, so we can assume that it is a grayscale image or a black and white one, keeping that in mind the output is a softmax of all the 10 values, here softmax gives probabilities or ratios for all the 10 digits, we can take the number as output with highest probability or ratio.

Convolution 1 (Layer 1) :

Here we are taking the input and convoluting with filters of size 5 x 5 thereby producing an output of size 28 x 28 check the formula above to calculate the output dimensions, the thing here is we have taken 6 such filters and therefore the depth of conv1 is 6, hence its dimensions were, 28 x 28 x 6 now pass this to the pooling layer

Pooling 1 (Layer 2) :

Here we are taking the 28 x 28 x 6 as input and applying average pooling of a matrix of 2×2 and a stride of 2 i.e hovering a 2 x 2 matrix on the input and taking the average of all those four pixels and jumping with a skip of 2 columns every time thus giving 14 x 14 x 6 as output we are computing the pooling for every layer so here the output depth is 6

Convolution 2 (Layer 3) :

Here we are taking the 14 x 14 x 6 i.e the previous o/p and convoluting with a filter of size 5 x5, with a stride of 1 i.e (no skip), and with zero paddings so we get a 10 x 10 output, now here we are taking 16 such filters of depth 6 and  convoluting thus obtaining an output of 10 x 10 x 16

Pooling 2 (Layer 4):

Here we are taking the output of the previous layer and performing average pooling with a stride of 2 i.e (skip two columns) and with a filter of size 2 x 2, here we superimpose this filter on the 10 x 10 x 16 layers therefore for each 10 x 10 we obtain 5 x 5 outputs, therefore, obtaining 5 x 5 x 16

Layer (N-2) and Layer (N-1) :

Finally, we flatten all the 5 x 5 x 16 to a single layer of size 400 values an inputting them to a feed-forward neural network of 120 neurons having a weight matrix of size [400,120] and a hidden layer of 84 neurons connected by the 120 neurons with a weight matrix of [120,84] and these 84 neurons indeed are connected to a 10 output neurons

These o/p neurons finalize the predicted number by softmaxing .

How does a Convolutional Neural Network work actually?

It works through weight sharing and sparse connectivity,

So here as you can see the convolution has some weights these weights are shared by all the input neurons, not each input has a separate weight called weight sharing, and not all input neurons are connected to the output neuron a’o only some which are convoluted are fired known as sparse connectivity, CNN is no different from feed-forward neural networks these two properties make them special !!!

1. After every convolution the output is sent to an activation function so as to obtain better features and maintaining positivity eg: ReLu

2. Sparse connectivity and weight sharing are the main reason for a convolutional neural network to work

3. The concept of choosing a number of filters in between layers and padding and stride and filter dimensions are taken on doing a number of experimentations, don’t worry about that, focus on building foundation, someday you will do those experiments and build a more productive one!!!

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What Is Deep Learning And Neural Network

Neural Networks and Deep Learning are the two hot buzzwords used nowadays with Artificial Intelligence. The recent developments in the world of Artificial intelligence can be attributed to these two as they have played a significant role in improving the intelligence of AI.

Look around, and you will find more and more intelligent machines around. Thanks to Neural Networks and Deep Learning, jobs and capabilities that were once considered the forte of humans are now being performed by machines. Today, Machines are no longer made to eat more complex algorithms. Still, instead, they are fed to develop into an autonomous, self-teaching system capable of revolutionizing many industries.

Neural Networks and Deep Learning have lent enormous success to researchers in tasks such as image recognition, speech recognition, and finding deeper relations in data sets. Aided by the availability of massive amounts of data and computational power, machines can recognize objects, translate speech, train themselves to identify complex patterns, learn how to devise strategies, and make contingency plans in real-time.

So, how exactly does this work? Do you know that both Neutral Networks and Deep-Learning are related, in fact, to understand Deep learning, you must first understand Neural Networks? Read on to know more.

What is a Neural Network

A Neural network is a programming pattern or a set of algorithms that enables a computer to learn from observational data. A Neural network is similar to a human brain, which works by recognizing patterns. The sensory data is interpreted using a machine perception, labeling or clustering raw input. The patterns recognized are numerical, enclosed in vectors, into which the data such are images, sound, text, etc. are translated.

Think Neural Network! Think how a human brain function

As mentioned above, a neural network functions just like a human brain; it acquires all the knowledge through a learning process. After that, synaptic weights store the acquired knowledge. During the learning process, the synaptic weights of the network are reformed to achieve the desired objective.

Just like the human brain, Neural Networks work like non-linear parallel information-processing systems which rapidly perform computations such as pattern recognition and perception. As a result, these networks perform very well in areas like speech, audio and image recognition where the inputs/signals are inherently nonlinear.

In simple words, you can remember Neural Network as something which is capable of stocking knowledge like a human brain and using it to make predictions.

Structure of Neural Networks

(Image Credit: Mathworks)

Neural Networks comprise of three layers,

Input layer,

Hidden layer, and

Output layer.

Each layer consists of one or more nodes, as shown in the below diagram by small circles. The lines between the nodes indicate the flow of information from one node to the next. The information flows from the input to the output, i.e. from left to right (in some cases, it may be from right to left or both ways).

The nodes of the input layer are passive, meaning they do not modify the data. They receive a single value on their input and duplicate the value to their multiple outputs. Whereas the nodes of the hidden and output layer are active. Thus can they modify the data.

In an interconnected structure, each value from the input layer is duplicated and sent to all of the hidden nodes. The values entering a hidden node are multiplied by weights, a set of predetermined numbers stored in the program. The weighted inputs are then added to produce a single number. Neural networks can have any number of layers and any number of nodes per layer. Most applications use the three-layer structure with a maximum of a few hundred input nodes

Example of Neural Network

Consider a neural network recognizing objects in a sonar signal, and there are 5000 signal samples stored in the PC. The PC has to figure out if these samples represent a submarine, whale, iceberg, sea rocks, or nothing at all? Conventional DSP methods would approach this problem with mathematics and algorithms, such as correlation and frequency spectrum analysis.

While with a neural network, the 5000 samples would be fed to the input layer, resulting in values popping from the output layer. By selecting the proper weights, the output can be configured to report a wide range of information. For instance, there might be outputs for: submarine (yes/no), sea rock (yes/no), whale (yes/no), etc.

With other weights, the outputs can classify the objects as metal or non-metal, biological or non-biological, enemy or ally, etc. No algorithms, no rules, no procedures; only a relationship between the input and output dictated by the values of the weights selected.

Now, let’s understand the concept of Deep Learning.

What is a Deep Learning

Deep learning is a subset of Neural Networks; perhaps you can say a complex Neural Network with many hidden layers.

Technically speaking, Deep learning can also be defined as a powerful set of techniques for learning in neural networks. It refers to artificial neural networks (ANN) that are composed of many layers, massive data sets, and powerful computer hardware to make complicated training models possible. It contains the class of methods and techniques that employ artificial neural networks with multiple layers of increasingly richer functionality.

Structure of Deep learning network

Deep learning networks mostly use neural network architectures and hence are often referred to as deep neural networks. The use of work “deep” refers to the number of hidden layers in the neural network. A conventional neural network contains three hidden layers, while deep networks can have as many as 120- 150.

Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. Deep learning networks can learn features directly from the data without the need for manual feature extraction.

Examples of Deep Learning

Deep learning is currently being utilized in almost every industry, starting from Automobile, Aerospace, and Automation to Medical. Here are some of examples.

Google, Netflix, and Amazon: Google uses it in its voice and image recognition algorithms. Netflix and Amazon also use deep learning to decide what you want to watch or buy next

Driving without a driver: Researchers are utilizing deep learning networks to automatically detect objects such as stop signs and traffic lights. Deep learning is also used to detect pedestrians, which helps decrease accidents.

Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.

Thanks to Deep Learning, Facebook automatically finds and tags friends in your photos. Skype can translate spoken communications in real-time and pretty accurately too.

Medical Research: Medical researchers are using deep learning to automatically detect cancer cells

Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.

Electronics: Deep learning is being used in automated hearing and speech translation.

Read: What is Machine Learning and Deep Learning?

Conclusion

Neural Networks is not new, and researchers have met with moderate success in the last decade or so. But the real game-changer has been the evolution of Deep neural networks.

By outperforming the traditional machine learning approaches it has showcased that deep neural networks can be trained and trialed not just by a few researchers, but it has the scope to be adopted by multinational technology companies to come with better innovations in the near future.

Thanks to Deep Learning and Neural Network, AI is not just doing the tasks, but it has started to think!

What Is Android Architecture And Libraries?

Introduction to Android Architecture

Android is an operating system for Mobile devices (Smartphones and Tablets) and an open-source platform built on Linux OS. A conglomerate of Handset companies like Sony, Samsung, and Intel developed it. The Open Handset Alliance (OHA), led by Google, releases versions of the Android operating system (OS) for deployment on mobile devices.

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Android Architecture provides an integrated approach for developers to develop mobile applications that can run on any device with Android OS installed in it, and it allows the applications component to be reused and obviate the need for redevelopment. Android source codes are offered under the category of open-source license on multiple websites. Google hosts most of it under Apache License 2.0 and kernel under General public license 2.0. It also provides a robust run-time environment for the execution of apps with a powerful interaction with peripheral devices and other apps.

What is Android Architecture?

Before studying Architecture, let us go through some of the features of the Android Operating system.

Android OS can be customized as needed, and hence we can notice many avatars of this OS are deployed in different mobile devices with multiple unique features.

It supports all mobile connectivity technologies, viz., Wi-Fi, CDMA, GSM, NFC, Bluetooth, etc., and basic functionalities like telephony, SMS, and data transfer. With this connectivity, data can be transferred back and forth between devices thru various apps.

It provides Interfaces (APIs) that support location-dependent services such as GPS.

SQLite database provides storage functionalities needed by Android. Being a lightweight database, it enables simpler storage and quicker data retrieval.

It supports all versions of multimedia files (Audio/Video) and integrates a Microphone, Camera, Accelerometer, and speaker for effective management of recording and playback operations.

Developers can use HTML5 and CSS3 to create an intuitive and impressive front-end screen.

It allows multiple windows to be active simultaneously, performing different tasks.

Graphics 2D/3D are supported.

Supports NFC technology that connects two NFC-enabled devices by touching each other.

Other features include multi-language support, User-adjustable widgets, and Google Cloud messaging.

Architecture:

It consists of several software modules to support the functioning of mobile devices. These software modules mainly contain the kernel and set of Libraries that facilitate mobile application development, and they form part of the runtime, application framework, and the actual application.

The application modules are grouped into five sections under four different layers.

Android runtime layer has two sections, namely DVM and Libraries, and all the layers have only one section each.

1. Application Layer

The application layer is the topmost layer in the architecture, and it is the front end for the users. Native applications developed using Android architecture and third-party applications are installed in this layer. Applications from this layer get executed with the help of the run time layer using the classes and services provided by the framework layer. Example of Application is Email, Contacts, Calendar, Camera, Time, Music, Gallery, Phone, SMS, Alarm, Home, and Clock.

2. Applications Framework Layer

The applications Framework layer holds the classes needed to develop applications in the Android platform. It enables access to hardware, handles the user interface, and manages resources for an application. The services provided by this layer are made available to the application layer for development as a class. Some of the components in the framework layer are NFC service, Notification Manager, Activity Manager, Telephony service, Package Manager, and view system, and used in application development as needed.

3. Android Runtime Layer

Android Runtime layer is vital to this OS, containing sections like Dalvik Virtual Machine (DVM) and Core libraries. This environment provides basic power to the applications with the help of libraries. Dalvik virtual machine exploits the basic inherent power of Java language in managing memory and multi-threading options to provide multiple instances to Android OS and ensure that it runs effectively. It leans on Kernel for threading and OS-level functionalities. This layer provides the services of Zygote to handle the forking of the new process, Android debug bridge, etc. Core Libraries provide features of Java language for the development of applications in Android OS.

4. Kernel Layer Framework of Android Architecture

The application framework provides Java classes for application development. Developers use these Java classes during coding. This component provides the following services.

Activity Manager: Manages the application’s lifecycle and tracks all the activities.

Content Provider: Facilitates sharing data with external applications.

Resource Manager: Enables applications to use other resources like color settings, user interactions and strings.

Notification Manager: Manages alerts and notifications to users on the status of application execution.

View system: Provides various view options for creating user interaction.

Android Architecture Libraries

Some of the components in this library are:

1. Media framework to manage Audio and video recording and playing.

2. Surface Manager to monitor display functionalities and text manipulation during display.

3. SQLite for Database management.

5. Freetype supports the front end.

6. Web-Kit supports browser functionalities.

7. Readily available Widgets such as buttons, layouts, radio buttons, and lists.

8. SSL provides internal security.

9. Interfaces and other services:

Access to OS services for communication across processes.

Access to App model templates for easy development

Enables content access and interactions across applications.

Conclusion

In summary, Android Architecture provides a robust framework, interfaces, and libraries for developing and executing superior applications on mobile devices. It fully uses unique features of Android, such as Open source, Community support, Effective marketing, Low cost of development, a Rich environment for app development, and Solid inter-app and intra-app interfaces.

Recommended Articles

This is a guide to Android Architecture. Here we discuss the introduction, architecture, framework, and Android architecture libraries. You can also go through our other suggested articles to learn more –

Leveraging The Potential Of Neural Network Ai In The Translation Industry

Thanks to the great strides in the translation industry, the world is getting increasingly interrelated and co-dependent. The global translation market is growing by leaps and bounds which can be ascertained by the “Language Services Market: 2023” report published by the CSA (Common Sense Advisory) Research. According to the Language Services Market: 2023 report, the global outsourced language services, and technology market was pegged at US$46.52 billion in 2023, stated to grow and increase to US$56.18 billion by 2023. In a separate report prepared by a US-based market research firm, Nimdzi Insights titled, “The 2023 Nimdzi 100- Language Service Industry Analysis”; 2023 registered revenues of US$53.6 billion for the language services industry, a number which is projected to reach to US$ 70 billion by 2023.  

The Global Integration

An industry that has existed for centuries and forecasted to grow in double digits throws a surprise. The recent protectionist policies between countries add to the premise. Thanks to Globalization, markets across the world are exchanging, trading goods and services fuelling into the demands of multi-lingual economies. Nations want goods and services in their language, an important factor behind the rise of the Translation Industry in recent years.  

The Advent of Technology

It’s been over 10 years since Google launched its Google Translate along with the use of Phase based Machine Translation algorithm. Since then technology has not looked back, Machine learning-powered speech recognition and image recognition capabilities continue to redefine how the world trades. Though there is still is a long way to go, it is a challenging goal to improve the capabilities of machine translation.

Instant Translation with NLP

In the current scenario, state-of-the-art neural machine translation engines translate texts with a 60-90% accuracy. This technology comes with its faults when it is put into test within the real-world translation scenarios, one being technology cannot translate texts consistently. The encoder-decoder attention model architecture lets sequences with sentence-like lengths to be used as inputs to the model. This model goes fine when one sentence is translated but goes haywire when it faces long paragraphs and document texts. The model translates each sentence individually, without the pretext of the preceding sentence. This adds up to translations with inconsistent keywords between translated sentences.  

The Case of Humans vs Machines

If you thought that NLP enabled translators will replace Human translators, then that is not happening in the near future. AI is still far away from being able to do multitasking. TranslateFX, an AI-assisted translation platform for professionals, says in the short-run AI will not replace human translators with AI software. In the long run, though AI will make it conducive to make humans more productive to an extent of 60-70% more efficient in their job. Artificial intelligence software will improve the translation of the difficult legal document’s contracts, confidentiality agreements, agreements, disclaimers, licenses, press releases, business plans, research reports, corporate announcements, financial reports, prospectuses, information memorandums, terms and conditions making businesses across the world transact more easily and comprehensively. In short, AI-powered NLP solutions will augment human intelligence into complex document translations.  

Augmenting Human Intelligence

Neural machine translation is poised to be more accurate as the quality of data enhances, computation powers increase, and neural network architecture improves. This pragmatic shift will attribute humans to adapt to the benefits of technology and focus on what they are good at. Neural machine translation can be deployed to instantly produce accurate first drafts, the subsequent work of the human brains will be to augment the quality of the translation, this includes post-editing or reviewing of the machine-translated texts for accuracy and content mapping. Most of the translation tools that exist in the market are highly generic, they are trained to translate and an assortment of content ranging from chats to news to restaurant menus to storyboards. Without the context, machines are inefficient to accurately translate texts without understanding the target audience, circumstances, and the usage of the text.  

The Answer Lies in Customisation

In the coming times, technology will be more customized to suit the individual industry and enterprise needs, the same holds for the translation industry. Brace yourself for custom machine translation engines designed for specific enterprise documents, these include case studies, brochures, reports targeted to a certain business audience. Custom-developed machine translation engines have the potential to improve the accuracy of translated text to the extent of 20+%. Translation companies are now focussing on a specific company or industry, for instance, TranslateFX focuses on financial and legal documents translation.

Thanks to the great strides in the translation industry, the world is getting increasingly interrelated and co-dependent. The global translation market is growing by leaps and bounds which can be ascertained by the “Language Services Market: 2023” report published by the CSA (Common Sense Advisory) Research. According to the Language Services Market: 2023 report, the global outsourced language services, and technology market was pegged at US$46.52 billion in 2023, stated to grow and increase to US$56.18 billion by 2023. In a separate report prepared by a US-based market research firm, Nimdzi Insights titled, “The 2023 Nimdzi 100- Language Service Industry Analysis”; 2023 registered revenues of US$53.6 billion for the language services industry, a number which is projected to reach to US$ 70 billion by chúng tôi industry that has existed for centuries and forecasted to grow in double digits throws a surprise. The recent protectionist policies between countries add to the premise. Thanks to Globalization, markets across the world are exchanging, trading goods and services fuelling into the demands of multi-lingual economies. Nations want goods and services in their language, an important factor behind the rise of the Translation Industry in recent years.It’s been over 10 years since Google launched its Google Translate along with the use of Phase based Machine Translation algorithm. Since then technology has not looked back, Machine learning-powered speech recognition and image recognition capabilities continue to redefine how the world trades. Though there is still is a long way to go, it is a challenging goal to improve the capabilities of machine translation. AI -powered neural machine translation is the call of the global translation market. Based on the premise that the neural machine translation can be trained directly on the source and target text and not requiring a pipeline of standardized systems as deployed in statistical machine learning. The earlier versions of Machine Translation were based on multilayer perceptron neural network models limited by a fixed-length input sequence, characterized by same length outputs. The architecture of these models has improved considerably, the use of recurrent neural networks arranged in an encoder-decoder architecture which allows for variable-length input and output sequences. The recent addition of attention mechanisms gave leverage to these modes and permitted the model to improve the accuracy of long sequences of words allowing the model to learn where to emphasize the input sequence as each output is chúng tôi the current scenario, state-of-the-art neural machine translation engines translate texts with a 60-90% accuracy. This technology comes with its faults when it is put into test within the real-world translation scenarios, one being technology cannot translate texts consistently. The encoder-decoder attention model architecture lets sequences with sentence-like lengths to be used as inputs to the model. This model goes fine when one sentence is translated but goes haywire when it faces long paragraphs and document texts. The model translates each sentence individually, without the pretext of the preceding sentence. This adds up to translations with inconsistent keywords between translated chúng tôi you thought that NLP enabled translators will replace Human translators, then that is not happening in the near future. AI is still far away from being able to do multitasking. TranslateFX, an AI-assisted translation platform for professionals, says in the short-run AI will not replace human translators with AI software. In the long run, though AI will make it conducive to make humans more productive to an extent of 60-70% more efficient in their job. Artificial intelligence software will improve the translation of the difficult legal document’s contracts, confidentiality agreements, agreements, disclaimers, licenses, press releases, business plans, research reports, corporate announcements, financial reports, prospectuses, information memorandums, terms and conditions making businesses across the world transact more easily and comprehensively. In short, AI-powered NLP solutions will augment human intelligence into complex document translations.Neural machine translation is poised to be more accurate as the quality of data enhances, computation powers increase, and neural network architecture improves. This pragmatic shift will attribute humans to adapt to the benefits of technology and focus on what they are good at. Neural machine translation can be deployed to instantly produce accurate first drafts, the subsequent work of the human brains will be to augment the quality of the translation, this includes post-editing or reviewing of the machine-translated texts for accuracy and content mapping. Most of the translation tools that exist in the market are highly generic, they are trained to translate and an assortment of content ranging from chats to news to restaurant menus to storyboards. Without the context, machines are inefficient to accurately translate texts without understanding the target audience, circumstances, and the usage of the chúng tôi the coming times, technology will be more customized to suit the individual industry and enterprise needs, the same holds for the translation industry. Brace yourself for custom machine translation engines designed for specific enterprise documents, these include case studies, brochures, reports targeted to a certain business audience. Custom-developed machine translation engines have the potential to improve the accuracy of translated text to the extent of 20+%. Translation companies are now focussing on a specific company or industry, for instance, TranslateFX focuses on financial and legal documents translation. In the times to come, consistency will be a bone of contention, an issue which is addressed with additional machine learning or natural language processing algorithms developed as per the context. Augmenting human intelligence has a long way to go in the translation industry. The future will bring economies together with the power of NLP backed translation.

What Is A Network Security Key? Definition & How To Find It

A network security key is a network password that is used to provide access and authorization on a device or network so a user can join.

The key provides a secure connection between the user and the wireless device, such as a router. Without a key system as a roadblock, cybercriminals could access the network and possibly commit a cybercrime.

Read below to see how it works, the difference between network security types, and how to find network security keys for important devices:

For more information on network security: How to Conduct a Network Security Risk Assessment

Should You Ever Change Your Network Security Key?

A network security key is physical, digital, or biometric data that allows a user to connect to a private network. Typically it is a Wi-Fi or wireless network password.

Network security helps you to ensure that the network is secure. Private networks, such as business or home networks, need to keep hackers or unwanted users out of their systems.

Devices like smartphones, tablets, and laptops connect to the network security key to access Wi-Fi, so it is often referred to as a Wi-Fi password. Connections can be set up through a device’s settings, helping the task stay simple for non-experts.

For more on network security: Develop & Implement a Network Security Plan in 6 Easy Steps

The most well-known and widely used types of network security keys are WEP, WPA, and WPA2:

WEP is the oldest and considered outdated

WPA is a newer key with some issues

WPA2 is the newest and built to prevent the main WPA and WEP problems

WEP (wired equivalent privacy) is a standard network security key protocol that adds security to Wi-Fi and other wireless networks. WEP was designed to give wireless networks the level of privacy protection a wired network provides. 

WEP uses encryption based on a combination of user and system-generated key values. Originally, WEP supported encryption keys of 40 bits plus 24 bits of system-generated data, making the keys 64 bits in total length. Now as an updated network security key, the encryption keys have been extended to support 104-bit, 128-bit, and 232-bit encryption keys. 

WEP encrypts the data a company uses, making the keys unreadable to a human, but is processed through receiving devices. Many tech experts recommend against WEP, as it is now considered outdated.

WPA (Wi-Fi Protected Access) was created to be the Wi-Fi Alliance’s replacement for WEP. WEP provides authorized systems with the same network security key, while WPA uses the temporal key integrity protocol (TKIP), which actively changes the key that a company or consumers use. 

WPA includes integrity checks to determine if a cybercriminal had stolen data packets. The keys used by WPA can support up to 256-bit, but certain elements of WPA can be exploited.

A WPA key is a network security key that connects to a wireless network. Whoever has access to the WPA password can give the key to employees or consumers. Some wireless routers will have the default WPA passphrase or password.

WPA2 is an upgraded version of WPA. WPA2 is based on the robust security network (RSN) mechanism, and it works in two modes:

Personal mode or Pre-shared Key (WPA2-PSK)

: Usually used in consumers’ homes, WPA-PSK uses a shared password for access. 

Enterprise mode (WPA2-EAP)

: Used by enterprises or businesses, the password is usually only accessible through another administrator.

CCMP (Counter Mode Cipher Block Chaining Message Authentication Code Protocol) is used by both modes and is based on the Advanced Encryption Standard (AES) algorithm. This offers message authenticity and integrity verification. 

However, like WEP and WPA, WPA2 has flaws. Attackers can exploit a system weakness in WPA2, allowing attackers to pose as another network and make the user connect to a fake and dangerous network. Hackers could decrypt encryption keys. Still, WPA2 is thought of as more secure than WEP or WPA.

For more information on network security: What is Network Detection and Response? 

Each different device has its own way to connect to a network security key. Familiar devices, such as smartphones and computers, will connect through the Wi-Fi network. Once connected, the device should remember the network security key. 

Routers and modems often have network security on them, or if used as a business key, an administrator will likely have access to the password.

For more details on how to find the network security key, see below:

Each ISP (Internet service provider) and the manufacturer will likely use different phrasing, so if there is a sticker on the router, it might be phrased differently than the network security key.

Here are some names ISP and manufacturers might use for network security keys:

Password

Network Key

Wireless password

WPA or WPA2 key

Occasionally, an ISP or manufacturer might require a user to go to their account settings for the network security key. Once the router has been identified and the network security key is on the hardware or given to a user, a connection for users will be available.

Finding a network security key for Android and iPhone takes little time. Additionally, these two platforms will have slightly different steps. Updates on the devices have the potential to change the process.

Tap the “i” icon next to the network.

Enter or find the router’s login credentials.

Type in the password to connect.

Select the current network.

Scan the QR code or see the router to find the Wi-Fi password.

In the Network and Sharing Center, next to Connections, select your Wi-Fi network name.

In Wi-Fi Status, select Wireless Properties.

In Wireless Network Properties, select the Security tab, then select the Show characters check box.

A user’s Wi-Fi network password is displayed in the Network security key box.

Open the search function.

Search keychain access.

In the Keychain Access screen, search for the Wi-Fi network.

Check Show Password to make the network security key visible.

Enter the Mac password to confirm user access rights.

Network security key mismatch errors can be frustrating for businesses and consumers. There is not one specific answer for a mismatch error. However, the top three reasons this might happen are:

Wrong security mode

: A user’s device might be under a different security type or the device remembers its user as a certain security type. If this does happen, a user can go into network settings and change the security type.

Third-party antivirus tools

: While antivirus tools are essential for cybersecurity, third-party tools might cause connection issues. They can affect how the Wi-Fi passwords are stored. If this is the case, the antivirus tools may need to be uninstalled.

Old or faulty wireless drivers

: The user’s wireless drivers can cause mismatch errors as well. An old or faulty wireless driver may not have the same tech or connection, making it difficult to make changes to the network. If this is the case, tech experts recommend getting newer wireless drivers or updating the driver. 

It is vital to try different commands and other forms of updating if the top mismatch errors are not helpful. 

Changing the network security key is recommended, due to its importance. To keep a system safe, it is almost necessary to do it every 6 to 12 months.

A company’s or user’s computer is needed to change a network security key, but it only requires a little bit of computer knowledge. The process depends on the router’s brand and model, but commonly works with the directions below: 

Finding the Router’s IP Address

Type “ipconfig /all“ and press enter. A user will be given details about the router connection. Once the details are up, a user needs to look for the “default gateway” and write down the IP address. 

Open the web browser and type the IP address. The router will require a username and password that can generally be found on the router’s web management interface. Once access is given, a user may want to change their credentials for security purposes.

Network security keys are vital for any user or company with a private network. Cybercrime, uninvited users, and hackers can be prevented through network security keys.

Whether devices are using WEP, WPA, or WPA2 types, protection is necessary within network connections. Changing the password is a necessary step as well, to save any worry about unsafe networks.

Also see: Why Firewalls are Important for Network Security 

Community Cloud: Example, Architecture, Advantages

A community cloud can be defined as a cloud-based infrastructure that enables multiple organizations to share services and resources derived from common regulatory and operational requirements. It offers a shared platform and resources for different organizations to work on their business requirements. It is operated and managed by community members, third-party vendors, or even both.

The organizations that share common business requirements constitute the members of the community cloud. Common business requirements comprise Shared industry regulations, shared data storage, or shared infrastructure requirements.

Factors to consider before adopting Community Cloud

Community cloud helps organizations with common business concerns use the public or private cloud cost-effectively. It offers unlimited scalability and delivers faster cloud deployment

Here are the key factors that you need to consider before adopting the community cloud:

The community cloud allows individual organizations to work together.

It enables data sharing among different organizations while adhering to strict regulations and security requirements.

Service level agreements should be reviewed and understood by organizations.

The trading firms need to understand the economic model of the community cloud.

They should understand how it manages data storage and security issues.

Organizations should consider the availability/uptime of the community cloud.

Organizations should evaluate how tenants manage issues when selecting a community cloud.

Community cloud challenges should be mitigated by the cloud provider.

Sensitive data should be managed effectively by the cloud provider.

Community Cloud Architecture

Members of the community cloud are essentially organizations with similar business needs. These business requirements are derived from the industry regulations along with the need for shared data and services.

Here are all the entities of community cloud architecture:

In the above image, they are represented as “Org 1”, “Org2”, and “Org 3,” respectively. Such organizations generally have shared policies and protocols.

IAM: is an abbreviation for identity and access management that provides authorization and access to the specific cloud that meets the shared protocols and policies adopted by different organizations.

Cloud manager: is an entity that becomes an interface for different organizations to manage their shared resources and protocols.

Storage requirements: Different clouds may offer separate storage in accordance with the requirements of different organizations. They are documented under the service level agreements of the community cloud.

The cloud managers have to further split the data centers’ responsibilities and costs among participating organizations.

Since implementing the community cloud could be complex, the community cloud prepares a handbook that covers the mission statement, services, and resources ownership. The handbook provides detailed information on several cloud solutions.

Key Components of Community Cloud Architecture

The community cloud generally has a distributive Architecture. The components of the community cloud can be listed as shown below: –

Shared Policies and protocols:

Participating organizations are able to operate and maintain the community cloud through a long-term commitment. The members have to collaborate and design the following as explained below: –

Governance Policies: A community cloud is always monitored through a governance model. In the shared cloud platform, governance policies enable effective monitoring among all stakeholders.

Security protocols: Regulations must be designed, analyzed, and interpreted periodically to ensure the community cloud’s smooth functioning. Such regulations together constitute security protocols for a community cloud.

HIPPA is an example of a security protocol that requires email encryption to meet up with standards of compliance protocols.

Access policies: Participating organizations need to document and maintain access policies. These are policies that govern who is authorized to use which resources under the community cloud.

Policies about allocation: The community cloud developers should answer all questions related to business continuity before setting up the community cloud.

Cloud:

Cloud computing is a key component of any community cloud.

A community cloud is built on top of a private cloud.

Moreover, there is an off-the-shelf community cloud that is tailored to meet the needs of specific industries and government agencies.

Cloud management system:

Under the community cloud, a cloud management system plays an important role. It helps in delivering cloud operations with ease.

It further runs regular updates from time to time to ensure systems are up to date. A community cloud also needs more specific controls for resource allocation, app and data management as well as placement of security protocols.

Identity and access management system:

The identity and access management system helps in the identification of multiple users that are a part of different organizations and would access the community cloud.

Data governance tool:

The community cloud offers a tool that supports data governance. It oversees the creation, updating, and deletion of the data.

These activities are performed per the data policies already agreed upon and defined between stakeholders.

Shared application service:

This is one of the crucial components of a community cloud. It primarily looks into getting common services and applications. Different departments working under the same organization utilize these clouds.

The key building step in the community cloud is Resource allocation. The specifics of bandwidth and storage should be carefully considered while allocating resources for the community cloud.

Advantages of Community Cloud

The community cloud offers many benefits, as described below: –

Cost efficacy: The community cloud allows multiple and different users to connect with the same environment. The sessions of such users are arranged logically. The system ensures that there is no additional need for separate servers. It offers cost-effective solutions for organizations.

Regulatory compliance: Regulatory laws that govern privacy tend to evolve every second. They tend to vary at the national, regional, and global levels.

High unlimited scalability and availability: The cloud community offers the same level of availability and scalability as cloud services. There is no downtime under community cloud operations.

Security requirements per industry standards: Community clouds typically provide the expertise that meets industry security standards.

More and Better Control: A community cloud is designed in such a manner that it combines the best features of both public and private clouds. It also offers off-site back up at regular intervals.

Community Cloud Use cases & Examples

A number of industry-specific community clouds are featured in several use cases telling their success story. There is an exponential growth in demand for the usage of community clouds. Various cloud service providers are now delivering solutions based on the community cloud model.

Finance: The community cloud has become a popular model for financial institutions to manage sensitive customer information and applicable monetary transactions. Models like these are designed to meet the security and compliance requirements of financial institutions.

Public and Government sectors: The community cloud model has become popular among government departments for managing sensitive communication and infrastructure needs.

Federal agencies generally develop highly secure government clouds which ensure that the data remains protected.

Educational institutions: The community cloud model is the most suited model for educational institutions as it allows them to share information, research material, and educational content on the cloud.

In group projects, the model can be used to facilitate question-and-answer sessions that help foster collaboration.

Health care industry: The community cloud model has use cases for the healthcare sector. This sector deals with highly sensitive information when collaborating with several pharmaceutical companies.

Community cloud helps in information sharing without disclosing any private information. Many pharmaceutical companies are part of the healthcare sector and collaborate with hospitals to provide quick healthcare solutions.

Best Practices for Community Cloud

Below are the best practices that should be adopted for the community cloud model: –

Evaluation and selection of correct cloud management system: Organizations need to select robust and comprehensive management systems. It must be of high priority when selecting third-party vendors. The system should be robust enough for the administrators to identify how the storage space is being used and provide a proper audit trail for work.

Documentation on shared ownership terms: Each term and condition documented under the community cloud should be thoroughly discussed. The terms and conditions are to be approved by participating organizations before they make it to the final draft.

The service level agreement should specifically highlight the allotment of storage percentage and bandwidth for each approved member of the community cloud.

Determine the cost policies applicable to procurement of a new community cloud: Organizations must decide on who provides basic funding for the community cloud and who hires the cloud experts and integrators. There should be an additional decision on the management and terms of fund transfers. They also need to establish the metering capability to check on granular resources.

Management of security and patch requirements: Community clouds should establish security standards that match those established within their industry. Members of the community must establish their security standards.

Decision over data segmentation plan: Data segmentation is contingent on the regulations put forward and defined under the industry’s regulations. For example, a community cloud may segment and cloud resources to meet the security requirements of high-level government departments.

Summary

It helps in data sharing for remote employees.

It provides separate servers for different organizations.

The community cloud offers customization that meets the security requirements and specific industry regulations.

It offers unlimited scalability and is a highly flexible solution.

The cloud providers help in the mitigation of community cloud challenges.

Community cloud is derived from the concept of cloud computing.

Community cloud provides an end-to-end integration setup.

Public clouds are cheaper as compared to the community cloud model.

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