This Edureka video gives an introduction to Network Security and its nuances. It discusses the need of network security, tools for network security and various other fundamental concepts.
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With more people working from home, companies have started to rely on VPNs to grant secure access to users. However, VPNs aren’t the only way for businesses to guarantee a secure connection for workers.
Network World mentions a protocol known as a zero trust architecture, which only grants users the bare minimum of access to a network. Whatever access a user’s job requires is what the **zero-trust architecture **will allow the user to have.
Zero trust works by verifying every single device’s connection to the network. It uses an identity-management system that only allows specific access to certain parts of the network for particular devices. Authorized users no longer have access to any and all parts of the network. They can only get into the specific locations they need to perform their jobs. Zero-trust is an overarching architecture, but certain elements apply to networks, specifically. This article intends to demystify those elements and show how networks can be used to support zero trust.
Beyond Trust tells us that the principle of least privilege states that access rights for every account, user, or device accessing the network has limited access based on what they need to perform their duties. One way that networking professionals can ensure least-privilege access is via network segmentation. When a device or a user logs into a network, he or she is granted access only to the segment of the system where pertinent data is stored.
Networks can quickly implement this using a simple switching technique. By placing different segments in areas that are unreachable from each other, they limit the incidence of a breach. If a user or device is compromised, their limited access reduces the damage to the overall organization. Any applications or data on the compromised machine is limited to where the user or device has access.
Network segmentation can also be performed physically. Different networks can be located in different locations or separate server points, each with their own dedicated server. Suppose the malicious user is trying to hijack a central server. In that case, this setup ensures that only one of the company’s servers becomes compromised, while the rest of the business’s systems intact and safe.
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A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials
Everything around us has become smart, like smart infrastructures, smart cities, autonomous vehicles, to name a few. The innovation of smart devices makes it possible to achieve these heights in science and technology. But, data is vulnerable, there is a risk of attack by cybercriminals. To get started, let’s know about IoT devices.
The Internet Of Things(IoT) is a system that interrelates computer devices like sensors, software, and actuators, digital machines, etc. They are linked together with particular objects that work through the internet and transfer data over devices without humans interference.
Famous examples are Amazon Alexa, Apple SIRI, Interconnected baby monitors, video doorbells, and smart thermostats.
When technologies grow and evolve, risks are also on the high stakes. Ransomware attacks are on the continuous increase; securing data has become the top priority.
When you think your smart home won’t fudge a thing against cybercriminals, you should also know that they are vulnerable. When cybercriminals access our smart voice speakers like Amazon Alexa or Apple Siri, it becomes easy for them to steal your data.
Cybersecurity report 2020 says popular hacking forums expose 770 million email addresses and 21 million unique passwords, 620 million accounts have been compromised from 16 hacked websites.
The attacks are likely to increase every year. To help you secure your data of IoT devices, here are some best tips you can implement.
Your router has the default name of make and model. When we stick with the manufacturer name, attackers can quickly identify our make and model. So give the router name different from your addresses, without giving away personal information.
If your devices are connected to the internet, these connections are vulnerable to cyber attacks when your devices don’t have the proper security. Almost every web interface is equipped with multiple devices, so it’s hard to track the device. But, it’s crucial to stay aware of them.
When we use the default usernames and passwords, it is attackable. Because the cybercriminals possibly know the default passwords come with IoT devices. So use strong passwords to access our IoT devices.
Use strong or unique passwords that are easily assumed, such as ‘123456’ or ‘password1234’ to protect your accounts. Give strong and complex passwords formed by combinations of alphabets, numeric, and not easily bypassed symbols.
Also, change passwords for multiple accounts and change them regularly to avoid attacks. We can also set several attempts to wrong passwords to set locking the account to safeguard from the hackers.
Are you try to keep an eye on your IoT devices through your mobile devices in different locations. I recommend you not to use the public WI-FI network to access them. Because they are easily accessible through for everyone, you are still in a hurry to access, use VPN that gives them protection against cyber-attacks, giving them privacy and security features, for example, using Express VPN.
There are software and firewalls like intrusion detection system/intrusion prevention system in the market. This will be useful to screen and analyze the wire traffic of a network. You can identify the security weakness by the firewall scanners within the network structure. Use these firewalls to get rid of unwanted security issues and vulnerabilities.
Every smart device comes with the insecure default settings, and sometimes we are not able to change these default settings configurations. These conditions need to be assessed and need to reconfigure the default settings.
Nowadays, every smart app offers authentication to secure the accounts. There are many types of authentication methods like single-factor authentication, two-step authentication, and multi-factor authentication. Use any one of these to send a one time password (OTP) to verify the user who logs in the smart device to keep our accounts from falling into the wrong hands.
Every smart device manufacturer releases updates to fix bugs in their software. These security patches help us to improve our protection of the device. Also, update the software on the smartphone, which we are used to monitoring the IoT devices to avoid vulnerabilities.
When we connect the smart home to the smartphone and control them via smartphone, you need to keep them safe. If you miss the phone almost, every personal information is at risk to the cybercriminals. But sometimes it happens by accident, makes sure that you can clear all the data remotely.
However, securing smart devices is essential in the world of data. There are still cybercriminals bypassing the securities. So make sure to do the safety measures to avoid our accounts falling out into the wrong hands. I hope these steps will help you all to secure your IoT devices.
If you have any, feel free to share them in the comments! I’d love to know them.
Are you looking for more? Subscribe to weekly newsletters that can help your stay updated IoT application developments.
#iot #enterprise iot security #how iot can be used to enhance security #how to improve iot security #how to protect iot devices from hackers #how to secure iot devices #iot security #iot security devices #iot security offerings #iot security technologies iot security plus #iot vulnerable devices #risk based iot security program
In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.
If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
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What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:
Common symptoms of messy data include data that contain:
In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:
As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!
Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package
readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse
readr package function
read_csv() is the function to use (we’ll cover that later).
Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:
The Brooklyn Excel file
Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the
readxlpackage. We specify the function argument
skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:
library(readxl) # Load Excel files brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
Note we saved this dataset with the variable name
brooklyn for future use.
The tidyverse offers a user-friendly way to view this data with the
glimpse() function that is part of the
tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:
Once the package is installed, load it to memory:
tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:
glimpse(brooklyn) ## Observations: 20,185 ## Variables: 21 ## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "… ## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA… ## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 … ## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "… ## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6… ## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,… ## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T… ## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112… ## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3… ## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22… ## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1… ## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "… ## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, … ## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.
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