DEV Community: brlikhon

Troubleshooting Wi-Fi Adapter Detection in Kali Linux

brlikhon — Mon, 30 Oct 2023 13:26:11 +0000

Introduction:
In this guide, we'll walk you through the steps to resolve the issue of a Wi-Fi adapter not being detected in Kali Linux. We'll start by downloading and installing the necessary drivers to enable your Wi-Fi adapter. This process involves unloading and reloading services and installing compatible drivers to ensure you can connect to available Wi-Fi networks.

Step 1: Download the Compatible Drivers
Visit the following website to download a suitable version of compatible drivers for your system: dev.gateworks.com/sources.

Step 2: Open the Terminal and Navigate to the Download Directory
After downloading the drivers, open your terminal and navigate to the directory where the downloaded file is located. You can use the 'ls' command to list the files in that folder:

cd /path/to/your/download/directory
ls

Step 3: Decompress the Downloaded File
Decompress the downloaded file using the 'tar' command. Replace with the actual filename. For example:

tar -jxvf <name of the file>
# For example:
tar -jxvf compat-wireless-2010-07-06.tar.bz2

Step 4: Move to the Decompressed Directory
Navigate to the directory where the files have been decompressed. Replace with the actual directory name. For example:

cd <name of the directory>
# For example:
cd compat-wireless-2010-07-06

Step 5: Unload Services
To prepare for driver installation, unload the services using the following command:

sudo make unload

Step 6: Load Services
Activate the services again with the following command:

sudo make load

Step 7: Install Required Packages
To ensure proper functionality, install the necessary packages using the following command:

sudo apt install -y linux-headers-$(uname -r) build-essential dkms git libelf-dev

Step 8: Clone the Git Repository
Clone the Git repository containing the drivers you need for your Wi-Fi adapter:

git clone https://github.com/morrownr/8814au
cd 8814au

Step 9: Make the Installation Script Executable
Locate the 'install-drivers.sh' file in the directory, make it executable, and run it:

sudo chmod +x install-drivers.sh
sudo ./install-drivers.sh

Step 10: Driver Configuration and Reboot
During the driver installation process, you may be asked if you want to edit driver options. Type 'n' for no and press Enter. After the installation is complete, you will be prompted to reboot. Type 'Y' and press Enter to restart your system.

Conclusion:
After completing these steps, your Kali Linux machine should detect nearby Wi-Fi networks. You can now connect to available Wi-Fi networks and enjoy a fully functional Wi-Fi adapter.

From Bits to Qubits: Exploring the Quantum Computing Revolution

brlikhon — Thu, 26 Oct 2023 04:42:38 +0000

In recent years, quantum computing has captured significant attention, with claims of performing calculations that would take traditional supercomputers decades to complete in a mere matter of seconds. Google's quantum computer, in particular, has showcased this remarkable feat. As we delve into the realm of quantum computing, a fundamental question arises: how do quantum computers work, and what sets them apart from their traditional counterparts? This article aims to provide a comprehensive exploration of the workings of quantum computers and elucidate the stark differences that distinguish them from conventional computing systems.

Quantum computers and tradional computers have same working process. Both has bits, gates, circuits, algorithms and protocols and application. Bits are the fundamental binary units of information, while logic gates process these bits in digital circuits to execute algorithms that follow specific instructions for tasks. Communication between devices relies on protocols that ensure data is exchanged accurately, and applications utilize this digital foundation to provide diverse software functionalities, powering our modern digital world. In quantum computers bits are known as Qubits, gates are Quantum Gates, circuits are Quantum Circuit, algorithms and protocols as Quantum Algorithms and Protocols.

Some Key Concepts of Quantum Computing:

Quantum computers process information in a fundamentally quantum mechanical way, utilizing phenomena like superposition, interference, entanglement, and quantum measurement. This is different from classical computers.
The quantum stack organizes the layers of a quantum computer's operation, from the fundamental qubits up through quantum gates, circuits, algorithms/protocols, and applications.
Qubits can exist in a superposition between 0 and 1 states, unlike classical bits that are limited to just 0 or 1.
Quantum gates like X, H, etc. manipulate qubits to perform quantum computations when connected into quantum circuits.
Quantum circuits operate on qubits with sequences of quantum gates, ending in a measurement which collapses the qubit into 0 or 1 randomly.
Quantum algorithms and protocols built from circuits can be applied to solve problems in areas like chemistry, medicine, finance, optimization, and more.
Key representations for qubits are ket notation for quantum math, Bloch spheres for visualization, and vectors for coding quantum states.
Progress is being made on quantum hardware, with device qubit counts increasing into the hundreds. But challenges remain compared to billions of transistors in classical computers. The key distinguishing factor of quantum computing is the use of quantum mechanical phenomena like superposition, entanglement, and measurement to process information in new ways not possible on classical machines. Representing and manipulating qubits with things like quantum gates connected into circuits enables this novel form of computation. Difference Between Traditional Computers and Quantum Computers:

Aspect	Traditional Computers	Quantum Computers
Basic Unit of Information	Bits (0 or 1)	Qubits (Superposition of 0 and 1)
Parallel Processing	Limited	Exponential (Entanglement of Qubits)
Computational Power	Sequential	Potential for Exponential Speedup
Problem Solving	Conventional	Potential to Solve Certain Problems Faster
Impact on Cryptography	Existing Methods	Potential to Impact Cryptographic Security
Technological Maturity	Mature	Emerging, Facing Practical Challenges

The key difference is qubits allowing exponential parallel states compared to traditional bits with singular states, giving quantum computers transformational potential. But practical quantum computing is still in early stages.

Installing Essential Software on Kali Linux: Browsers, Office Suite, Code Editors, and More

brlikhon — Tue, 24 Oct 2023 04:29:55 +0000

Open your terminal and run following commands and relax.

Install Tor Browser



sudo apt-get install tor torbrowser-launcher

This command installs the Tor Browser, which is a privacy-focused web browser that allows users to browse the internet anonymously by routing their traffic through the Tor network.

Install LibreOffice



sudo apt install libreoffice -y

This command installs LibreOffice, a free and open-source office suite that includes applications for word processing, spreadsheets, presentations, and more. It's an alternative to commercial office suites like Microsoft Office.

Install Google Chrome



wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb

apt install ./google-chrome-stable_current_amd64.deb

These commands first download the Google Chrome web browser from Google's servers using wget and then install it on your system. Google Chrome is a widely used web browser known for its speed and compatibility with many websites and extensions.

Install Brave Browser



sudo apt install curl

sudo curl -fsSLo /usr/share/keyrings/brave-browser-archive-keyring.gpg https://brave-browser-apt-release.s3.brave.com/brave-browser-archive-keyring.gpg

echo "deb [signed-by=/usr/share/keyrings/brave-browser-archive-keyring.gpg] https://brave-browser-apt-release.s3.brave.com/ stable main"|sudo tee /etc/apt/sources.list.d/brave-browser-release.list

sudo apt update

sudo apt install brave-browser

These commands install the Brave Browser, another privacy-focused web browser. The first two commands set up the necessary keyring and repository information, and the subsequent commands update the package list and install Brave Browser.

Install qBittorrent



sudo apt install qbittorrent

This command installs qBittorrent, a popular open-source BitTorrent client that allows you to download and manage torrent files. It's known for its lightweight and user-friendly interface.

Install Python



sudo apt install python3 python3-pip

# confirm you have installed Python
python3 --version

These commands install Python, a widely used high-level programming language. The first command installs both Python 3 and pip, a package manager for Python. The second command confirms the installed Python version, allowing you to check if the installation was successful.

Install Visual Studio Code
Download VS Code .deb file: Go to the VS Code website at https://code.visualstudio.com/download.
Download the .deb file for Debian/Ubuntu by clicking on the "Download .deb" button. This file is specific to your system architecture (e.g., amd64 for 64-bit).
Install VS Code:
Open your terminal and navigate to the directory where you downloaded the .deb file. Then, run the following command to install VS Code:



sudo apt install ./<name of .deb file>

For example, if the downloaded file is named code_1.83.1-1696982868_amd64.deb, you would run:



sudo apt install ./code_1.83.1-1696982868_amd64.deb

Once the installation is complete, you can launch VS Code from the applications menu or by typing code in the terminal.

Please make sure to replace with the actual filename of the .deb file you downloaded.

Cloud Wars: A Comparative Analysis of AI and ML Offerings from AWS, Azure, and GCP

brlikhon — Sun, 22 Oct 2023 14:01:12 +0000

Artificial intelligence (AI) and machine learning (ML) have become indispensable technologies for businesses today. Major tech companies like Amazon, Microsoft, and Google offer robust cloud platforms with AI and ML capabilities to meet this growing demand. In this article, we'll compare the AI/ML offerings of the top 3 public cloud providers - AWS, Azure, and GCP.
Amazon Web Services:
Amazon Web Services (AWS) was the first cloud computing platform to offer Infrastructure as a Service in 2008 and has been a leader in cloud-based services. AWS is the most mature and widely adopted cloud AI/ML platform. SageMaker provides end-to-end capabilities for building, training, deploying ML models. Tight integration with other AWS services like S3, Lambda, etc. Broad range of AI services like Lex, Rekognition, Translate. Leverages the ecosystem of small/medium businesses.

AWS provides a comprehensive set of AI and ML products and services including:
AWS AI and ML API modules for services

AWS Service	API Module	Description
AI Services	Comprehend	Extract insights about the content of documents without the need of any special preprocessing.
	Comprehend Medical	Comprehend Medical detects and returns useful information in unstructured clinical text.
	Forecast	A fully managed deep learning service for time series forecasting.
	Fraud Detector	A fully managed service that helps you detect suspicious online activities
	HealthLake	A Fast Healthcare Interoperability Resources (FHIR)-enabled patient Data Store.
	Kendra	A search service that enables users to search unstructured text using natural language.
	Lex	Building conversational interfaces into applications using voice and text.
	Lookout for Equipment	Provides a conceptual overview of Amazon Lookout for Equipment.
	Lookout for Metrics	Helps you continuously find anomalies in business and operational data based on the same technology used by Amazon.com.
	Lookout for Vision	Find visual defects in industrial products, accurately and at scale.
	Monitron	An end-to-end system that detects abnormal behavior in industrial machinery.
	Panorama	Improve their operations by automating monitoring and visual inspection tasks at the edge.
	Personalize	Real-time personalization and recommendations.
	Polly	Text-to-Speech (TTS) cloud service that converts text into lifelike speech.
	Rekognition	Add image and video analysis to your applications.
	Textract	Add document text detection and analysis to your applications.
	Translate	Translate text to and from English across a breadth of supported languages.
	Transcribe	Provide transcription services for your audio files and audio streams.
	DeepComposer	An artificial intelligence (AI)-enabled music keyboard that provides you with a hands-on learning experience to explore generative learning.
	DeepLens	A connected HD camera developer kit with a set of sample projects to help developers learn machine learning concepts using hands-on computer vision use cases.

AWS AI and ML API modules for platform functions

AWS Service	API Module	Description
AI Platform	SageMaker	Build and train machine learning models, and then deploy them into a production-ready hosted environment.
	Augmented AI (A2I)	Build the workflows required for human review of ML predictions.
	DevOps Guru	Generates operational insights to help you improve the performance of your operational applications.
	Elastic Inference	Attach low-cost GPU-powered acceleration to many Amazon machine instances to reduce the cost of running deep learning inference.
	Deep Learning AMIs	Equip machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud at any scale.
	Deep Learning Containers	A set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
	DeepRacer	A reinforcement learning (RL)-enabled autonomous 1/18th-scale vehicle with supporting services in the AWS Machine Learning ecosystem.
	Apache MXNet on AWS	An open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of platforms, from cloud infrastructure to mobile devices.
	Machine Learning	Build smart applications, including applications for fraud detection, demand forecasting, targeted marketing, and click prediction.
	CodeGuru	Provides intelligent recommendations for improving application performance, efficiency, and code quality in Java applications.

AWS AI and ML industry cases

Industry	Description
Healthcare and Life Sciences	Unlock hidden potential in your health data with HIPAA-eligible ML for petabyte-scale analysis and fast unstructured text and speech documentation.
Financial Services	Innovate with ML across banking, payments, capital markets, and insurance sectors to improve customer experience with personalization, and virtual assistants and prevent online fraud.
Industrial and Manufacturing	Detect abnormal machine behavior, spot defects, enable predictive maintenance, and improve operations with purpose-built Industrial AI services with no ML experience required.

AWS is the most well-rounded and widely used. A key advantage of AWS is how tightly its AI and machine learning services integrate with its broader platform and ecosystem. For example, it's easy to leverage other AWS offerings like S3 storage and Lambda functions when building an end-to-end ML workflow. The large ecosystem of small and medium businesses that AWS has accumulated helps drive adoption of its AI/ML platform. Through its flywheel effect, AWS can gain more data and use cases by having a bigger customer base, which enables it to improve its artificial intelligence capabilities even further. In summary, AWS has established itself as a mature, full-featured, and widely used cloud platform for artificial intelligence and machine learning applications. The combination of tight internal integration and a thriving ecosystem give AWS a strong competitive position in cloud-based AI/ML.In summary, AWS provides a mature, full-featured, and widely adopted cloud platform for building and deploying AI/ML applications. The tight integration across AWS services and large ecosystem gives it a competitive advantage.

Microsoft Azure:
Microsoft Azure is another leading cloud platform that offers a comprehensive set of artificial intelligence and machine learning services. Azure's machine learning platform enables building, training and deploying ML models and includes automated ML capabilities to quickly generate models with low code or no code. Azure Machine Learning seamlessly supports open source frameworks like PyTorch and TensorFlow. One standout feature of Azure is Azure Applied AI Services which provides prebuilt AI modules and solutions tailored for business scenarios like customer service, process automation and search. Azure AI platform also includes Azure Cognitive Services which offers APIs and services for computer vision, speech, language and decision-making.

Four parts of Azure AI and ML platform by functions

Azure AI/ML Service	Description
Azure Applied AI Services	Modernize business processes with task-specific AI, Accelerate development with built-in business logic, Run responsibly with security both in the cloud and the edge
Azure Cognitive Services	Easily access sophisticated AI models, Build with AI services to achieve CV, ASR, and NLP applications,Deploy anywhere from the cloud to edge with containers,Get started quickly and customize solutions
Azure Machine Learning	Develop with your choice of tools, Create and deploy models at scale, Innovate responsibly with built-in responsible capabilities, Build your way for open-source frameworks and languages
AI Infrastructure	Access large-scale, Enable hybrid and multi-cloud deployments, Access a breadth of AI hardware

A key strength of Microsoft Azure is its deep focus on the enterprise segment and integration with other Microsoft products and services. Azure ML naturally leverages Microsoft's expertise in operating systems, databases, Office products and other enterprise software. This allows it to deliver AI solutions tailored for heavily regulated industries like healthcare which have specific compliance needs. Azure also utilizes Microsoft's strong foothold with large organizations to offer robust AI capabilities customized for companies with complex IT systems and workflows. Its hybrid cloud approach allows easy integration of public and private clouds. In the AI application scenario, the high-frequency applications based on the cloud and the edge are extracted: Machine Learning, Knowledge Mining, Conversation AI, Document process automation, Machine translation, and Speech transcription, Azure high frequency AI and ML scenarios based on the cloud and the edge:

AI Service	Description
Machine Learning	Build, train, and deploy models, Use the latest tools and frameworks and Provide low-code and no-code tools
Knowledge Mining	Uncover latent insights from documents, images, and media and The only cloud search service with built-in AI capabilities
Conversation AI	Develop enterprise-grade conversational AI experiences and Build multilingual and multimodal bots for nearly any scenario
Document Process Automation	Turn documents into usable data and Accelerate document processing
Machine Translation	Translate text and documents in real time or in batches
Speech Transcription	Transcribe speech to text and Produce natural-sounding text-to-speech voices

In terms of AI and ML industrial application, Azure has integrated the four industry solutions of financial services, manufacturing, retail, and healthcare after summarizing and refining several single-point solutions in the same industry. AI and ML industrial use cases:

AI/ML Industry Module	Description
Financial Services	Combat financial crime, Improve customer experiences and Modernize core banking
Manufacturing	Automate quality control,Practice proactive maintenance and Enhance worker safety
Retail	Improve customer experiences, Optimize customer assistance and Optimize stock replenishment
Healthcare	Enable predictive care, Improve operational outcomes and Accelerate innovation

In summary, Microsoft Azure provides an end-to-end AI/ML platform focused on ease of use, prebuilt solutions, and tight enterprise integration. Its automated ML and seamless support for open source frameworks lowers the barrier to AI adoption. And Microsoft's long-standing relationships with large enterprises give Azure an advantage in bringing AI to complex, regulated industries. These factors contribute to Azure's position as a top cloud AI/ML platform.

Google Cloud Platform:
Google Cloud Platform (GCP) takes a differentiated approach to providing AI and machine learning services compared to AWS and Azure. GCP offers an end-to-end machine learning platform called Vertex AI that supports the full model development lifecycle including tools and services for data preparation, training, deployment and monitoring. A highlight of Vertex AI is its feature stores for simplified data management for ML. GCP also provides AutoML services that leverage Google's advanced transfer learning and neural architecture search technologies to allow developers with limited ML expertise to train high-quality custom models. A key advantage of GCP is its specialization in AI research and technologies. For example, it offers TensorFlow, a popular open source ML framework originated at Google, as well as Cloud TPUs or Tensor Processing Units tailored for ML workloads. GCP also provides a range of prebuilt AI services like video/image analytics, speech recognition, natural language processing and translation. Overall, GCP takes a cloud-first approach focused on providing ML developers advanced capabilities and flexibility.

GCP AI and ML API modules for data scientists

API Module	Description
Vertex AI	Accelerating data preparation, Scaling data, Training and experimentation and Model deployment
Vertex AI Workbench	Rapid prototyping and model development and Developing and deploying AI solutions on Vertex AI with minimal transition

GCP AI and ML API modules for developers

API Module	Description
AutoML	Building custom machine learning models in minutes and Training models specific to your business needs
Cloud Inference API	Indexing and loading a dataset consisting of multiple stored data sources, Executing Inference queries over loaded datasets and Unloading or canceling the loading of a dataset
Dialogflow	Creating natural interaction for complex multi-turn conversations,Building and deploying advanced agents quickly and Building enterprise-grade scalability
Media Translation (Beta)	Delivering real-time speech translation directly from your audio data and Scaling quickly with straightforward internationalization
Speech-to-Text	Creating automatic speech recognition, Transcribing in real time and Empowering Google Contact Center AI
Text-to-Speech	Improving customer interactions, Engaging users with voice user interface in devices and applications and Personalizing communication
Timeseries Insights API (Preview)	Gathering insights in real time from time series datasets, Detecting anomalies while they are happening and Handling large scale datasets and running thousands of queries per second
Translation AI	Delivering seamless user experience with real-time translation, Engaging your audience with compelling localization of your content and Reaching global markets through internationalization of your products
Video AI	Extracting rich metadata at the video, shot, or frame level and Creating your own custom entity labels with AutoML Video Intelligence
Vision AI	Using ML to understand images with industry-leading prediction accuracy and Training ML models to classify images by custom labels using AutoML Vision

GCP AI and ML API modules infrastructure

API Module	Description
Deep Learning Containers	Prototyping your AI applications in a portable and consistent environment
Deep Learning VM Image	Accelerating your model training and deployment
GPUs	Speeding up compute jobs like machine learning and HPC and Accelerating specific workloads on your VMs
TensorFlow Enterprise	Boosting enterprise development with long-term support on specific distributions, Scaling resources across CPUs, GPUs, and Cloud TPUs and Developing and deploying TensorFlow across managed services
TPUs	Running cutting-edge machine learning models with AI services on Google Cloud and Iterating quickly and frequently on machine learning solutions and Building your own ML-powered solutions for real-world use cases

Google Cloud Platform takes a leading role in providing advanced artificial intelligence and machine learning technologies through its cloud platform. A core advantage of GCP is its extensive experience in AI research and deeply technical approach to developing AI/ML services. For example, TensorFlow, one of the most widely used open source ML frameworks, was originally developed at Google. GCP offer TensorFlow-based services as well as Cloud TPUs, its custom-designed hardware accelerators optimized for ML workloads. GCP provides Vertex AI, a unified end-to-end platform for building, deploying and managing ML models. Highlights of Vertex AI include its feature stores for managing data for ML in a scalable way. GCP also enables no-code or low-code ML model development through its AutoML technology which leverages Google’s expertise in transfer learning and neural architecture search. In addition, GCP offers pre-built AI services like computer vision, natural language processing, speech recognition and translation. While GCP's footprint in enterprise AI is smaller than AWS and Azure, it aims to appeal to leading-edge ML developers and researchers through access to Google's latest AI research and technologies. GCP's API-based approach gives developers flexibility while making Google's AI advancements available. In summary, GCP's contributions center around providing ML developers with an advanced cloud platform to leverage state-of-the-art capabilities, tools, and Google's extensive AI research - rather than more turnkey solutions tailored for business users.

In summary, AWS (Amazon Web Services) has established a first-mover advantage through continuous technological innovation, customer-centric product polishing, and the creation of socio-economic benefits. It offers services like Amazon Lookout for Equipment, which helps find anomalies in business and operational data, and Amazon Comprehend, which extracts insights from unstructured text . Azure (Microsoft) and GCP (Google) have learned from Amazon's success and built their moats based on their company characteristics. They are relatively late market entrants but have made significant contributions to the industry. Azure offers services like Azure Machine Learning, a fully managed deep learning service for time series forecasting. GCP provides cloud-based AI and ML capabilities through its platform, with a focus on open API interfaces and their documentation. All three companies have contributed to the development of cloud-based AI and ML platforms, each with its own competitive strategy and focus. They have played a crucial role in shaping the industry and driving its continuous growth and innovation.

Deploying WordPress with Amazon EC2 (Elastic Compute Cloud) and Amazon RDS (Relational Database Service)

brlikhon — Thu, 19 Oct 2023 11:02:11 +0000

Deploying WordPress with Amazon EC2 (Elastic Compute Cloud) and Amazon RDS (Relational Database Service) involves setting up a web server (EC2 instance) to host WordPress and a database server (RDS instance) to store WordPress data. Here's a step-by-step guide on how to do it:

Step By Step Tutorial

Prerequisites:
An AWS account with appropriate permissions.
Basic knowledge of AWS services.

Step 1: Launch an EC2 Instance

Log in to the AWS Management Console.
In the top right corner of the Amazon RDS console, select the Region in which you want to create the DB instance.
Navigate to the EC2 dashboard.
Choosing an Amazon Machine Image. a. Click "Launch Instance" and select an Amazon Machine Image (AMI) based on your preferences (e.g., Amazon Linux 2, Ubuntu).

b. On the first page, enter wordpress app as your instance name.

c. Next, choose an Amazon Machine Image (AMI).

Choose an instance type based on your requirements. A t2.micro instance is suitable for testing, but you may need a larger instance for production use.
Configuring SSH Key.
a. Open the key pair (login) section and choose Create new key pair for your instance.

b. Give your key pair a name. Then choose the Create key pair button, which will download the .pem file to your machine.

Configure a Network Settings. To configure this, select Allow SSH traffic from My IP and select Allow HTTP traffic from the internet.
Review and launch the instance, creating or selecting an existing key pair for SSH access.

You have successfully launched your EC2 instance. Next, we will configure your Amazon RDS database to work with your EC2 instance.

Step 2: CREATING A MYSQL DATABASE WITH AMAZON RDS
Open the AWS Management Console. When the screen loads, enter RDS in the search bar, then select RDS to open the service console.

Choose the Create database button to get started.

The first step is to choose the database engine you want to use. Amazon RDS supports six different engines, from popular open-source options like MySQL and PostgreSQL, to commercial options like Oracle and Microsoft SQL Server, to a cloud-native option called Amazon Aurora that was custom-built to take advantage of the cloud. WordPress uses MySQL, so select Standard create for the database creation method and choose the MySQL engine.
Next, you will specify the authentication settings for your MySQL deployment. These include the database name and the master username and password. In the Settings section, enter wordpress as your DB instance identifier. Then specify the master username and password for your database. Choose a strong, secure password to help protect your database. Store the username and password for safekeeping as you will need it in a later module.
Finally, Amazon RDS provides a number of additional configuration options to customize your deployment. You need to make one change in this area. Select the Additional configuration line to expand the options. Set the Initial database name to wordpress. This will ensure Amazon RDS creates the database in your MySQL instance upon initialization. You will use this database name when connecting to your database.

Choose the Create database button to create your database.

You have now created a fully managed MySQL database using Amazon RDS.

Step 3: CONFIGURING YOUR AMAZON RDS DATABASE
To configure this, go to the Amazon RDS databases page in the AWS console. Choose the MySQL database you created.

Scroll to the Connectivity & security tab in the display and choose the security group listed in VPC security groups. The console will take you to the security group configured for your database.

Select the Inbound rules tab, then choose the Edit inbound rules button to change the rules for your security group.

The default security group has a rule that allows all inbound traffic from other instances in the default security group. Click on Add Rule. Change the Type property to MYSQL/Aurora, which will update the Protocol and Port range to the proper values. Then, in the Source put the Private IPv4 addresses of the EC2 instance.

Step 4: Connect to the EC2 Instance
Once the instance is running, use SSH to connect to it using the private key pair you specified during instance creation. Previously, you downloaded the .pem file for the key pair of your instance. Locate that file now. It will likely be in a Downloads folder on your desktop.

For Mac or Linux users:
Open a terminal window. If you are on a Mac, you can use the default Terminal program that is installed, or you can use your own terminal.
In your terminal, run the following commands to use SSH to connect to your instance. Replace the “” with the path to your file, e.g., “~/Downloads/wordpress.pem”, and the “” with the public IP address for your EC2 instance.

chmod 400 <path/to/pem/file>
ssh -i <path/to/pem/file> ec2-user@<public_IP_DNSAddress>

You should see the following in your terminal to indicate that you connected successfully:

Step 5: Install Required Software on EC2 to Configure WordPress

Create a database user . a. First, run the following command in your terminal to install a MySQL client to interact with the database.

sudo yum install -y mysql

b. Go to the Amazon RDS databases page in the AWS console. You should see the wordpress database you created for the WordPress installation. Select it to find the hostname for your Amazon RDS database.

c. In the details of your Amazon RDS database, the hostname will be shown as the Endpoint in the Connectivity & security section.

In your terminal, enter the following command to set an environment variable for your MySQL host. Be sure to replace “” with the hostname of your RDS instance.

export MYSQL_HOST=<your-endpoint>

Next, run the following command in your terminal to connect to your MySQL database. Replace “” and “” with the master username and password you configured when creating your Amazon RDS database.

mysql --user=<user> --password=<password> wordpress

Finally, create a database user for your WordPress application and give the user permission to access the wordpress database.
Run the following commands in your terminal:

CREATE USER 'wordpress' IDENTIFIED BY 'wordpress-pass';
GRANT ALL PRIVILEGES ON wordpress.* TO wordpress;
FLUSH PRIVILEGES;
Exit

As a best practice, you should use a better password than wordpress-pass to secure your database.

Installing Apache Web Server a. To install Apache on your EC2 instance, run the following command in your terminal:

sudo yum install -y httpd

b. To start the Apache web server, run the following command in your terminal:

sudo service httpd start

Go to the EC2 Instances page and find your instance. In the Description below, find the Public IPv4 DNS of your instance.

Download and Configure Wordpress In this step, you will download the WordPress software and set up the configuration. a. First, download and uncompress the software by running the following commands in your terminal:

wget https://wordpress.org/latest.tar.gz
tar -xzf latest.tar.gz

If you run ls to view the contents of your directory, you will see a tar file and a directory called wordpress with the uncompressed contents.

$ ls

[ec2-user@~]$ ls
latest.tar.gz wordpress
b. Change the directory to the wordpress directory and create a copy of the default config file using the following commands:

cd wordpress
cp wp-config-sample.php wp-config.php

Then, open the wp-config.php file using the nano editor by running the following command.
nano wp-config.php

You need to edit two areas of configuration.
First, edit the database configuration by changing the following lines:
// ** MySQL settings - You can get this info from your web host ** //
/** The name of the database for WordPress */

define( 'DB_NAME', 'database_name_here' );

/** MySQL database username */

define( 'DB_USER', 'username_here' );

/** MySQL database password */

define( 'DB_PASSWORD', 'password_here' );

/** MySQL hostname */

define( 'DB_HOST', 'localhost' );

The values should be:
DB_NAME: “wordpress”
DB_USER: The name of the user you created in the database. In this case admin.
DB_PASSWORD: The password for the user (admin) you created.
DB_HOST: The hostname of the database that you found in the endpoint of the RDS.

Deploy Wordpress a. First, install the application dependencies you need for WordPress. In your terminal, run the following command. sudo amazon-linux-extras install -y mariadb10.5 php8.2

b. Second, change to the proper directory by running the following command:

cd /home/ec2-user

c. Then, copy your WordPress application files into the /var/www/html directory used by Apache.

sudo cp -r wordpress/* /var/www/html/

d. Finally, restart the Apache web server to pick up the changes.

sudo service httpd restart

You should see the WordPress welcome page and the five-minute installation process.

That’s it. You have a live, publicly accessible WordPress installation using a fully managed MySQL database on Amazon RDS.
Step By Step Tutorial

Caching Web and Databases Part-3

brlikhon — Fri, 05 Aug 2022 13:51:02 +0000

DAX
Amazon DynamoDB accelerator or in the short form you can also call it as DAX is a fully managed highly available in-memory cache for Amazon DynamoDB. That delivers up to a 10 times improvement in the performance from milliseconds to microseconds even at millions of requests per second and DAX will do all the heavy lifting that is required to add in-memory acceleration to the DynamoDB tables without requiring the developers to manage any kind of caching validation data population or cluster management. There are a lot of benefits of using that since it is a fully managed component. Which means it will be highly scalable, flexible and secure. It is delivering extreme performance all because these things will be built into the component itself. Since it is fully managed and it's really easy to use because it is integrated with DynamoDB. DAX is designed to run within an Amazon VPC environment and you can just launch a DAX cluster in your virtual network and control the access to the cluster by using the security groups of the VPC.

So, once it is all set up and configured the data will start getting written to the DynamoDB and the cache will also be into action and the reads will be
happening from the cache so as to get the faster response times.
Amazon ElastiCache
So, ElastiCache is a in memory data store that is available in AWS it allows you to seamlessly set up run and scale popular open source compatible in memory data stores in the cloud and since it is a web service it is easy to deploy, operate and scale and also it allows you to choose between two in-memory database engines that is Memcached which is a widely adopted memory object caching system and the Redis which is a popular open source in-memory key value store. That supports data structure such as sorted sets and the list. It is used for retrieving the data from high throughput and low latency in memory data stores and it significantly improved the latency and throughput for many read heavy applications. Workloads for example social media, gaming or media sharing improving the application performance by storing critical pieces of data in memory for low latency access also Amazon ElastiCache is a popular choice for real-time use cases like caching session stores gaming, geospatial services, real-time analytics and queuing. It offers fully managed renders which is watered the most loved database by developers in the stack overflow 2020 developer survey and the Memcached for your most demanding applications. That require sub millisecond response times. Also you can choose between Radius and Memcached based on your needs there are certain special features with both like ElastiCache for Radius is highly available and ElastiCache for Memcached is multi-threaded. So both have certain similarities both have certain differences. It totally depends on your use case that what engine you have to use with your ElastiCache. You can go for ElastiCache for radius or you can go for ElastiCache with Memcached.

Let's understand the high level working of the ElastiCache for all the internet scale applications like real-time applications in the gaming, media streaming, dating, social media that needs fast access and also for the use cases like all the real-time transactions, chat applications BI and analytics session, store gaming leaderboards and caching ElastiCache works wonders it sits between your web servers and the database to serve the request faster by serving them from the cache itself. It can also be used as a message broker and it stores ephemeral or the temporary data in memory for the quick responses now let's have a look at a few of the use cases of ElastiCache. First of all we can use ElastiCache with our existing database to cache the frequently accessed records it will easily load of your relational or the NoSQL database either Redis or Memcached engine because of its low latency and the high throughput. It can also be used with Amazon S3 and additionally ElastiCache for Redis can deliver query results caching persistent session caching and full page caching. Apart from this it can be used to manage a session information for both Redis and Memcached. The session management is commonly required for the online applications including games, e-commerce website and the social media platforms. Apart from this in terms of machine learning we can use ElastiCache for reduce in the use cases such as fraud detection in gaming and financial services real time bidding in AdTech and match making in dating and ride sharing to process live data and make the decisions within tens of milliseconds apart from this in streaming solutions we can use ElastiCache for reduce with the streaming solutions such as Apache Kafka and Amazon Kinesis as a store to ingest process and analyze real-time data and ElastiCache can be used for real-time analytic use cases such as social media Ad targeting personalization IOT and the time series data analytics.

Caching Web and Databases Part-2

brlikhon — Wed, 27 Jul 2022 06:11:04 +0000

Database Caching
Caching can be applied to any type of database including relational databases such as Amazon RDS or NoSQL databases such as Amazon DynamoDB, MongoDB and Apache Cassandra. Also the best part of caching is that it is minimally invasive to implement and by doing so your application performance regarding both scale and speed is dramatically improved. When building distributed applications that require low latency and scalability there are a number of challenges that, the disk based databases can pose to your applications and a few common challenges include the slow processing queries. Like when there are number of query optimization techniques and schema designs that can help boost query performance. The data retrieval speed from disk plus the added query processing times generally will put your query response times in double digit milliseconds at best. Apart from this cost to scale is another challenge that can be faced. Whether the data is distributed in a disk based NoSQL database or vertically scaled up in a relational database. Scaling for extremely high reads can be costly and may require a number of database read replicas to match what a single in-memory cache node can deliver in terms of request per second and the need to simplify data access. While the relational databases provide excellent means to data model relationships they aren't optimal for the data access. There are instances where your applications may want to access the data in a particular structure or view to simplify the data retrievable and increase the application performance.
Strategies
Let's have a look at a few strategies how we can do the database caching. So, database cache basically supplements your primary database by removing the unnecessary pressure on it. Because it is going to hold the frequently accessed read data so that once the query is processed the result will be stored in the cache and when the same request comes up next time the response can be sent from the cache itself instead of processing it again in the database. The cache itself can live in a number of areas including your database, application or as a standalone cache layer. So the three most common types of database caches are there. First is the Database Integrated Cache. Some database such as Amazon Aurora they offer an integrated caching that is managed within the database engine and has built in right through capabilities when the underlying data changes on the database table the database updates its cache automatically. There's nothing within the application tier that is required to leverage this cache additionally there is DAX, the DynamoDB accelerator which does all the heavy lifting that is required to add the in-memory acceleration to your DynamoDB tables without requiring the developers to manage the cache invalidation data population of the cluster management. Another type of cache that we are having is a Local Caching. Which stores your frequently used data within the application itself. This not only speeds up your the data retrieval but also removes the network traffic that is associated with retrieving the data. Making the data retrieval faster than any other caching architectures. But a major disadvantage with this kind of cache is that among your applications each node has its own resident cache working in a disconnected manner the information that is stored within an individual cache node. Whether it's database cash data, web sessions or users shopping carts cannot be shared with other local caches. But the majority of the disadvantages that are with the local caching are mitigated with the remote caches. So, let's understand what are remote caching what is a remote cache and what is Remote Caching. This is a separate instance which is dedicated for storing the cache data in memory the remote caches will be stored on dedicated servers. And typically they will be built on key value NoSQL store such as Redis and Memcached. They provide hundreds of thousands of up to a million requests per second per cache unit and many solutions such as Amazon ElastiCache for Redis also provide the high availability needed for the critical workloads. Also the average latency of a request to a remote cache is fulfilled in sub millisecond latency orders of magnitude faster than a disk based database. This was about the three main strategies or the types of database caching.

I have a special offer for you to enjoy some summer savings - a $500 credit to play with Redis Enterprise this summer. You can redeem this coupon for any paid plan!
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Caching Web and Databases Part-1

brlikhon — Tue, 26 Jul 2022 03:53:00 +0000

Caching
Let's understand caching with an example, we need to go to store to buy necessary things which is 30 minutes away from home and but usually we buy a lot of things and stoke them. For frequent uses when we need them we don't need to travel 30 minutes to store to buy it again. Here we are doing caching using our home as a temporary storage for frequent access.
Home --> Store (30 minutes)
Home --> Stocked Items (2 minutes)
Now, in technical term when we keep some data in temporary storage so that quickly we can retrieve it for frequent uses. There can be certain types of data that can be cached and few of the examples include frequently accessed data the static data and the data that requires quite slow and expensive queries. I will be discussing about the caching of web session state and the databases let's start with the database caching.

I have a special offer for you to enjoy some summer savings - a $500 credit to play with Redis Enterprise this summer. You can redeem this coupon for any paid plan!
Follow these steps to use the coupon. Terms and conditions apply*:
Register or Login to the app
Add a payment method: Billing and Payments > Payment methods
Add a payment method: Billing and Payments > Credits >Use coupon SUMMER500
Create any paid subscription
Use code SUMMER500

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Offer expires July 31, 2022
Use with a new subscription only
Cannot combine with any other coupon offer
Must use all $500 in two months

Install Kali Linux 2022.2 On Your Android Device

brlikhon — Sat, 23 Jul 2022 12:57:00 +0000

When we say desktop operating systems we might say Windows or Mac. These operating systems are not free to use. But on other hand we have open source, Debian-based Linux distributions where we can pick from a lot of operating systems like Ubuntu, Deepin, MX Linux, Kali Linux, Parrot OS and many more. Among these operating systems one of the most popular one is Kali Linux. It is popular for most of its tools used in penetration testing, security research, computer forensics and reverse engineering. For some case you are travelling somewhere there you can't take you laptops with you or for enthusiasm you might think that what if I could keep Kali Linux in my pocket device.

Installing Kali on Android device is difficult and easy. Depends on your expertise and how much you can solve the problems you are facing. It's completely different from installing Kali on Desktop version. For, installing Kali there is a untold perquisite that you will need 9GB of free storage. You have to install it on main memory you can't install on external memory(SD card). kali Linux is light weight OS. You don't need high end hardware in the device to smoothly run it. But my suggestion a device with good chipset, 4GB RAM or more and Android Version 10 or above. Let's start the installation process.
First go to Play Store and install Termux. Open Termux and provide it storage permission:



termux-setup-storage

Click Allow when asking permission

Now, we need to Wget on termux run following commands one by one



pkg install update
pkg install root-repo
pkg install wget

Press y and Enter where needed.
To make Wget executable we will need to upgrade all the packages. Run the command



apt upgrade

Press y and Enter where needed.

Now let's download and install Kali 2022.2/ latest version. Run the commands



wget -O install-nethunter-termux https://offs.ec/2MceZWr
chmod +x install-nethunter-termux
./install-nethunter-termux

It will take 20-30 minutes. Depending on your internet speed and hardware configuration of your device. When all done there will be
[?] Delete downloaded roofts files? [y/N]. Its better you delete the files which will save the storage. Press y and Enter.
Here are some important commands. Now we can use the CLI mode. Type



nethunter

To see OS name type



uname

To see version name type



grep VERSION /etc/os-release

OHH!!! We want to use the desktop mode. For that download NetHunter App Store. Download from https://store.nethunter.com and install. Open NetHunter Store and search KeX. Download and install NetHunter Kex.

Go back to Termux set KeX password. Type



kex passwd

Set any 6 digit or more password and set a view-only password.

Now launch a VNC server.

kex

Here you will find a port number this case it is 5901.

Go to NetHunter Kex and change the port number and put the password you set and click on connect. Now you are in Kali Linux GNU. Here you can have the complete desktop exprerience.

To stop Desktop mode type



kex stop

You can check this in the form of video tutorial on YouTube: https://youtu.be/6GBcahIgVjM

Amazon Web Services (AWS)

brlikhon — Thu, 07 Jul 2022 15:23:22 +0000

What is Amazon Web Services?

Amazon Web Services(AWS) is the world's most comprehensive and broadly adopted cloud platform. In 2006, AWS began offering IT infrastructure services to business in the form of web services now commonly known as cloud computing. Now, AWS has more than 200 services offered. AWS provides a highly reliable scalable low cost infrastructure platform in the cloud that powers hundreds of thousands of businesses. In, 190+ countries around the globe AWS has architected to be the most flexible and secure cloud computing environment available today.

This is the AWS global infrastructure it has 26 launch region with multiple availability zones. The regions are the clusters of data centers. Each region has maximum of three availability zones as you can see the in the picture the blue dots are the availability zones and the yellow dots are upcoming regions. AWS has 84 availability zones around the globe. It has 400+ Edge Locations and 13 Regional Edge Caches(410+ Points of Presence).

Why you should pick AWS?

AWS is leading cloud infrastructure service provider in the industry with the largest share of 33%. AWS offers low pay, as you go pricing. With no upfront expenses or long-term commitments. You pay only for what you use. It is scalable and supports higher performance. AWS provides a massive global cloud infrastructure that allows you to quickly innovate experiment and iterate instead of waiting weeks or months for hardware. You can instantly deploy a new application instantly scale up as per your workload requirement. You can instantly scale down as well based on the demand. Whether you need one virtual server or thousands. Whether you need them for a few hours or 24/7. You still pay for what you use so this is a benefit of AWS. AWS offers flexible environment. AWS is a language and operating system agnostic platform you choose the development platform or programming model that makes the most sense for your business. You can choose which services you use, choose how you use them. This flexibility allows you to focus more on innovation less on infrastructure.

Virtual Machines

brlikhon — Fri, 01 Jul 2022 17:01:57 +0000

Virtual Machine

We have a process known as server virtualization which is a process of dividing a physical server into multiple unique and isolated virtual servers where each virtual server can run its own operating systems independently. Before we have one physical server on which each application was hosted. As an example, if we have one server then we have only one operating system there and one application will be hosted and that will be serving the request of the users. But, now after the server virtualization concept and the concept of virtual machines(VM) what we have got that single server will be now virtualized which means on this single server itself we will be able to run a lot of virtual machines. There will be one physical server but all of the machines that are running on this single server they will be known as the virtual machines. They won't be present physically but they will be inside the server. So, basically earlier this single server was used for just one purpose but since now the virtualization has happened that means many VMs can run on single server. Depending on the capacity the physical server we can determine what are the number of virtual machines that can be there on a single server. So, basically earlier it was just used for one purpose now it can be used for multiple purposes. Because on a single physical server, we can have multiple virtual machines for different applications and operating systems.

Ethics in AI and Data Science By Linux Foundation [edX]

brlikhon — Sat, 25 Jun 2022 16:00:03 +0000

Currently I have completed the course Ethics in AI and Data Science [LinuxFoundationX LFS112x]. Here, I will be sharing what I have learnt through the course. Let's begin.

What’s at Stake? With great power comes great responsibility, Abigail Higgins quotes that Steven Hawking’s biggest warning in his last book was about the rise of artificial intelligence:

"It will either be the best thing that’s ever happened to us, or it will be the worst thing. If we’re not careful, it may very well be the last thing."

What Hawking’s is alluding to in the near term is not so much the fear of the rise of AI, but rather the lack of care of the people behind the rise of AI. The fast and furious pace of AI adoption that is projected to contribute $15.7 trillion to the global economy by 2030. The opportunity with AI & Data Science is vast and the technology so exciting that some companies have rushed to market without regard for the societal consequences, which have also resulted in tarnished brands that can impact the bottom line. In summary, the data of ordinary people, biased system/governments(Like COVID Vaccine Issue), unimaginable dominance of big tech organizations are not careful about anything other than their own.

In the next chapter there is described what is Data Science, AI and the effect of AI, mostly uses and how it can be misused.

In the third chapter there is an in depth details of data and data science. Its value, nature, lifecycle and data governance.

Finally, comes the part of ethics is practice. How world is working against the development of misuse of AI, mainly in warfare. But, in some cases the US government and Google also broken the regulation.

This course will be very helpful for the students, professionals are willing to work not only in the field of data science and AI but also for everyone who want to work in the tech industry, to learn about the danger of misuse of technology like AI and how to create an unbiased cyber space by knowing the regulations.

I have successfully cleared this course. If anyone wants to learn you can enroll for free on edX. But, that won't provide the certificate. If anyone needs to know anything in details, please let me know and if you need any help with this course I will be willing to help you until I forget. Haha