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Introduction to chúng tôi security

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Authentication of chúng tôi security

In chúng tôi there are many different types of authentication procedures for web applications. If you want to specify your own authentication methods, then that also is possible. The different modes are accepted through settings that can be applied to the application’s web.config file. The web.config file is XML based file which allows users to change the behavior of the chúng tôi easily. In chúng tôi there are three different authentication providers as windows authentication, Forms authentication, and passport authentication.

1. Windows authentication

This authentication provider is the default provider for chúng tôi It authenticates the users based on the user’s windows accounts. windows authentication relies on the IIS to do the authentication. IIS can be configured so that only users on the Windows domain can log in. If users attempt to access a page and is not authenticated, then the user will be shown a dialogue box that asks the user to enter their username and password. Then this information is passed to the webserver and checked against the list of users in the domain. Based on the result the access is granted to the user.

To use windows authentication, the code is as follows

there are four options in windows authentication that can be configured in IIS

Basic authentication: In this, windows user name and password have to be provided to connect. This information is sent over the network in plain text and hence this is an insecure kind of authentication.

Integrated windows authentication: In this, the password is not sent across the network and some protocols are used to authenticate users. It provides the tools for authentication and strong cryptography is used to help to secure information in systems across the entire network.

Anonymous authentication: In this, IIS does not perform any authentication check and allows access to any user to the chúng tôi application.

Digest authentication: It is almost the same and basic authentication but the password is hashed before it is sent across the network.

2. Forms authentication

It provides a way to handle the authentication using your own custom logic within the chúng tôi application. When the user requests a page for the application, chúng tôi checks for the presence of a special session cookie. If the cookie is present, chúng tôi assumes that the user is authenticated and processes the request. If the cookie is not present, chúng tôi redirects the user to a web form you provide. When the user is authenticated process the request and indicates this to chúng tôi by setting a property, which creates the special cookie to handle the subsequent requests.

To use form authentication, the code is as follows

3. Passport authentication

To use passport authentication, the code is as follows

Authorization of chúng tôi security

Authentication and authorizing are two interconnected security concepts. Authorization is the process of checking whether the user has access to the resources they requested. In chúng tôi there is two forms of authorization available, one is file authorization and another is URL authorization.

File authorization: File authorization is performed by the FileAuthorizationModule. It uses the ACL (Access Control List) of the .aspx to resolve whether a user should have access to the file. ACL permissions are confirmed of the user’s windows identity.

Syntax is as follows

This code will allow user SwatiTawde and deny all other users to access that application. If you want to give permission for more users then just add usernames separated with a comma like SwatiTawde, eduCBA, edu, etc. and if you want to allow only admin roles to access the application and deny permission for all the roles, then write the following code in web.config

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Learn The Dataset Processing Techniques

Introduction to dataset preprocessing

In the actual world, data is frequently incomplete: it lacks attribute values, specific attributes of relevance are missing, or it simply contains aggregate data. Errors or outliers make the data noisy. Inconsistent: having inconsistencies in codes or names. The Keras dataset pre-processing utilities assist us in converting raw disc data to a tf. data file. A dataset is a collection of data that may be used to train a model. In this topic, we are going to learn about dataset preprocessing.

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Why use dataset pre-processing?

By pre-processing data, we can:

Improve the accuracy of our database. We remove any values that are wrong or missing as a consequence of human error or problems.

Consistency should be improved. The accuracy of the results is harmed when there are data discrepancies or duplicates.

Make the database as complete as possible. If necessary, we can fill up the missing properties.

The data should be smooth. We make it easier to use and interpret this manner.

We have few Dataset pre-processing Utilities:

Image

Text

Time series

Importing datasets pre-processing

Steps for Importing a dataset in Python:

Importing appropriate Libraries

import matplotlib.pyplot as mpt

Import Datasets

The datasets are in chúng tôi format. A CSV file is a plain text file that consists of tabular data. A data record is represented by each line in the file.

dataset = pd.read_csv('Data.csv')

We’ll use pandas’ iloc (used to fix indexes for selection) to read the columns, which has two parameters: [row selection, column selection].

x = Dataset.iloc[:, :-1].values

Let’s have the following incomplete datasets

Name Pay Managers

AAA 40000 Yes

BBB 90000

60000 No

CCC

Yes

DDD 30000 Yes

As we can see few missing cells are in the table. To fill these we need to follow a few steps:

from sklearn.preprocessing import Imputer

Next By importing a class

// Using Missing Indicator to fit transform.

Splitting a dataset by training and test set.

Installing a library:

from sklearn.cross_validation import train_test_split

A_train, A_test, B_train, B_test = train_test_split(X, Y, test_size = 0.2)

Feature Scaling

A_test = scale_A.transform(A_test)

Example #1

names = [‘sno’, ‘sname’, ‘age’, ‘Type’, ‘diagnosis’, ‘in’, ‘out’, ‘consultant’, ‘class’] X = array[:, 0:8] Y = array[:, 8]

Explanation

All of the data preprocessing procedures are combined in the above code.

Output:

Feature datasets pre-processing

Outliers are removed during pre-processing, and the features are scaled to an equivalent range.

Steps Involved in Data Pre-processing

Data cleaning: Data can contain a lot of useless and missing information. Data cleaning is carried out to handle this component. It entails dealing with missing data, noisy data, and so on. The purpose of data cleaning is to give machine learning simple, full, and unambiguous collections of examples.

a) Missing Data: This occurs when some data in the data is missing. It can be explored in many ways.

Here are a few examples:

Ignore the tuples: This method is only appropriate when the dataset is huge and many values are missing within a tuple.

Fill in the blanks: There are several options for completing this challenge. You have the option of manually filling the missing values, using the attribute mean, or using the most likely value.

b) Noisy Data: Data with a lot of noise

The term “noise” refers to a great volume of additional worthless data.

Duplicates or semi-duplicates of data records; data segments with no value for certain research; and needless information fields for each of the variables are examples of this.

Method of Binning:

This approach smoothes data that has been sorted. The data is divided into equal-sized parts, and the process is completed using a variety of approaches.

Regression:

Regression analysis aids in determining which variables do have an impact. To smooth massive amounts of data, use regression analysis. This will help to focus on the most important qualities rather than trying to examine a large number of variables.

Clustering: In this method, needed data is grouped in a cluster. Outliers may go unnoticed, or they may fall outside of clusters.

Data Transformation

We’ve already started modifying our data with data cleaning, but data transformation will start the process of transforming the data into the right format(s) for analysis and other downstream operations. This usually occurs in one or more of the following situations:

Aggregation

Normalization

Selection of features

Discretization

The creation of a concept hierarchy

Data Reduction:

Data mining is a strategy for dealing with large amounts of data. When dealing with bigger amounts of data, analysis faces quite a complication. We employ a data reduction technique to overcome this problem. Its goal is to improve storage efficiency and reduce analysis expenses. Data reduction not only simplifies and improves analysis but also reduces data storage.

The following are the steps involved in data reduction:

Attribute selection: Like discretization, can help us fit the data into smaller groups. It essentially combines tags or traits, such as male/female and manager, to create a male manager/female manager.

Reduced quantity: This will aid data storage and transmission. A regression model, for example, can be used to employ only the data and variables that are relevant to the investigation at hand.

Reduced dimensionality: This, too, helps to improve analysis and downstream processes by reducing the amount of data used. Pattern recognition is used by algorithms like K-nearest neighbors to merge similar data and make it more useful.

Conclusion – dataset preprocessing

Therefore, coming to end, we have seen Dataset processing techniques and their libraries in detail. The data set should be organized in such a way that it can run many Machines Learning and Deep Learning algorithms in parallel and choose the best one.

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Learn The Powershell Command For Nslookup

Introduction to powershell nslookup

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Powershell nslookupy overviews

The powershell nslookup it’s the important one for the network management tools and it is the Bind name for accessing the software applications in the servers. Mainly it focuses on the Internet-based computing systems for planned to deprecate the host and other proxy modes, dig, etc. When we use nslookup it does not use the operating system for accessing the domains and it resolves in any type of server modes like both localhost and globally made access. If we use the domain name system, it has some set of rules and libraries for tunning and performing the queries it’s may operate with different manners and situations. For each vendor, its varied depends upon the requirement and versions provided from the system additionally it includes the output of the other data sources which related to the user information’s getting by the other input data contents which related to the configurations that may require by the user end. The Network Information Service(NIS) it’s the main source of the nslookup its related to the host files, sub-domains, and other proxy-related things. The nslookup may vary it depends upon the operating system and system requirements because of the network bandwidth and other related things like pinging the url for to checking the network data packets.

Powershell command for NSLookup

Generally, it has more than a single way to perform the domain name system like DNS query for achieving the nslookup commands to operate the tool and to fetch the DNS record of each of the names specified with the domain resolver for Resolve-DnsName. When we perform this operation first it creates the empty array so it does not initialize the value then once the operation is triggered the each value will be stored as a separate memory allocation. If we use any programming loops iterating the values and the powershell will pass more focus on each data item including variables, operators, and even methods both default and customized methods. For each iteration of the loops it creates the temporary object and then it swaps to the original with the constant memory location. We can also utilize the nslookup command to resolve the ip to a hostname with commands like “nslookup %ipaddress%” in the screen for validating the datas to the powershell server for each and every session the object will be newly created once the session is closed or terminate it will automatically exist on that time.

Use Nslookup in Powershell

The nslookup command is equivalent power of the Domain name System Resolver it’s mostly configured and used in the cmdlet that can be performed using the DNS System the query cannot be fetched and it will not be retrieved from the particular domain name. We can use this in the powershell not only in that it is equivalent to the same and different DNS server. Because we can use the DNS with the specified and for network troubleshooting to identify the issue. We also would use the ping command to check the network connections and the host sites for checking, validate the datas with the specific IP address and which has performed the DNS reverse lookup for validating the datas it has configured and called the reverse proxy using fetch the query DNS and AD with the IP networks for a group of computers will join to the Active Directory Services in the domain for Active computers that already configured using the IP Address in the every User account of the computer system. Generally combining a group of hostnames for every IP address that can be done within the loop also iterates the datas. The recording datas are stored using the DNS record if any of the IP data is mismatched or not assign properly at that time it will throw an error like “IP not resolve” so that we can check the ip address of the specified system.

DNS NsLookup in PowerShell

PowerShell command for NSLookup

Some of the PowerShell commands will use the hostname to finding the IP address with some parameters. Like that below,

Based on the above commands we can retrieve data from the server.

Conclusion

The nslookup is the command for getting the information from the Domain Name System(DNS) server with the help of networking tools using the Administrator rights. Basically, it obtains the domain name of the specific ip-address by using this tool we can identify the mapping of the other DNS records to troubleshoot the problems.

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Learn The Examples Of Bootstrap Combobox

Introduction to Bootstrap Combobox

The Combo-box in bootstrap is a combination of the list box, input field, and dropdown box. The dropdown list mostly used for the combo box with the search button. The list box with the search field is used in the combo box. Sometimes users can choose multiple items using the list box. The input field is editable in the combo box of bootstrap. You can add the value according to the requirement. The user can choose the value from the list otherwise enter the value as per demand and requirement. The developer needed bootstrap and JavaScript together to make a combo box in bootstrap. In this topic, we are going to learn about Bootstrap Combobox.

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Syntax

To understand how to work in combo box, we need syntax of list/ dropdown in bootstrap. How to work search and list/ dropdown together, we need basic JavaScript syntax.

The following syntax is a list box with a search in bootstrap syntax.

The bootstrap syntax for the list.

JavaScript syntax for search.

$(document) .ready(function(){ $("#listbox") .on("keyup", function() { var values = $(this) .val() .toLowerCase(); $("#listItem li") .filter(function() { }); }); });

The toLowerCase() used to convert the characters uppercase to lowercase for display and search.

Examples of Bootstrap Combobox

Here are the following examples mentioned below

Example #1

You can see the combination of the list box with the input box together.

The form-control class used for the input search box in bootstrap.

The list-group and list-group-item used for the list box.

The filter() function used for search the item given list using JavaScript.

Code:

$(document) .ready(function(){ $(“#listbox”) .on(“keyup”, function() { var values = $(this) .val() .toLowerCase(); $(“#listItem li”) .filter(function() { }); }); });

Output

Before search

After search

Description:

The example, IN search in the search box and INDIA and CHINA display.

Example #2

The combination of the dropdown with an input field for searching.

The dropdown class is used to button and input attributes used for search.

Code:

$(document) .ready(function(){ $(“#listbox”) .on(“keyup”, function() { var values = $(this) .val() .toLowerCase(); $(“#listItem li”) .filter(function() { }); }); });

Output

Before Searching

After searching

Example #3

In the dropdown box, the user can directly connect with the search tag and display the required value. If the user can Type required character in the search box then dropdown automatically displays the values.

$(document) .ready(function(){ $(“#listbox”) .on(“keyup”, function() { var values = $(this) .val() .toLowerCase(); $(“#listItem li”) .filter(function() { }); }); });

Output

Example #4

The combo box can add the item in list and dropdown box as per requirement. See the below example.

Code:

Output

Before adding the item in the list.

After adding the item in the list.

Conclusion

The combo box in bootstrap is a combination of many tags and attributes in one form. Along with Bootstrap, JavaScript or JQuery is required for the combo box. Users can search and add the items in the list box or dropdown list. The input field is required for add and search items for the list.

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Learn The Different Examples Of Sqlite Function

Introduction to SQLite functions

SQLite provides different kinds of functions to the user. Basically, SQLite has different types of inbuilt functions, and that function we easily use and handle whenever we require. All SQLite functions work on the string and numeric type data. All functions of SQLite are case sensitive that means we can either use functions in uppercase or lowercase. By using the SQLite function, we sort data as per the user requirements. SQLite functions have a different category, such as aggregate functions, data functions, string functions, and windows functions, and that function we can use as per the requirement.

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SQLite functions

Now let’s see the different functions in SQLite as follows.

1. Aggregate Functions

AVG: It is used to calculate the average value of a non-null column in a group.

COUNT: It is used to return how many rows from the table.

MAX: It is used to return the maximum value from a specified

MIN: It is used to return the minimum value from a specified

SUM: is used to calculate the sum of non-null columns from the specified table.

GROUP_CONCAT: It is used to concatenate the null value from the column.

2. String Functions

SUBSTR: It is used to extract and return the substring from the specified column with predefined length and also its specified position.

TRIM: It is used to return the copy of the string, and it removes the start the end character.

LTRIM: It is used to return the copy of the string that removed the starting character of the string.

RTRIM: It is used to return the copy of the string that removed the ending character of the string.

LENGTH: It is used to return how many characters in the string.

REPLACE: It is used to display the copy of the string with each and every instance of the substring that is replaced by the other specified string.

UPPER: It is used to return the string with uppercase that means it is used to convert the all character into the upper cases.

LOWER: It is used to return the string with a lowercase, which means converting all character into lower cases.

INSTR: It is used to return the integer number that indicates the very first occurrence of the substring.

3. Control Flow Functions

COALESCE: It is used to display the first non-null argument.

IFNULL: It is used to implement if-else statements with the null values.

IIF: By using this, we can add if – else into the queries.

NULLIF: It is used to return the null if first and second the element is equal.

4. Data and Time Function

DATE: It is used to determine the date based on the multiple data modifiers.

TIME: It is used to determine the time based on the multiple data modifiers.

DATETIME: It is used to determine the date and time based on the multiple data modifiers.

STRFTIME: That returns the date with the specified format.

5. Math Functions

ABS: It is used to return the absolute value of the number.

RANDOM: It is used to return the random floating value between the minimum and maximum integer.

ROUND: It is used to specify the precision.

Examples

Now let’s see the different examples of SQLite functions as follows.

create table comp_worker(worker_id integer primary key, worker_name text not null, worker_age text, worker_address text, worker_salary text);

Explanation

In the above example, we use the create table statement to create a new table name as comp_worker with different attributes such as worder_id, worker_name, worker_age, worker_address, and worker_salary with different data types as shown in the above example.

Now insert some record for function implementation by using the following insert into the statement as follows.

insert into comp_worker(worker_id, worker_name, worker_age, worker_address, worker_salary) values(1, "Jenny", "23", "Mumbai", "21000.0"), (2, "Sameer", "31", "Pune", "25000.0"), (3, "John", "19", "Mumbai", "30000.0"), (4, "Pooja", "26", "Ranchi", "50000.0"), (5, "Mark", "29", "Delhi", "45000.0");

Explanation

In the above statement, we use to insert it into the statement. The end output of the above statement we illustrate by using the following screenshot as follows.

Now we can perform the SQLite different functions as follows.

a. COUNT Function

Suppose users need to know how many rows are present in the table at that time; we can use the following statement.

select count(*) from comp_worker;

Explanation

In the above example, we use the count function. The end output of the above statement we illustrate by using the following screenshot.

b. MAX Function

Suppose we need to know the highest salary of the worker so that we can use the following statement as follows.

select max(worker_salary) from comp_worker;

Explanation

In the above example, we use the max function to know the max salary of a worker from the comp_worker table. The end output of the above statement we illustrate by using the following screenshot.

c. MIN Function select min(worker_salary) from comp_worker;

Explanation