Thursday, January 18, 2024

How To Recover Files That Are Deleted From Recycle Bin

How To Recover Files That Are Deleted From Recycle Bin

How To Recover Files That Are Deleted From Recycle Bin

How To Recover Files That Are Deleted From Recycle Bin

Well, Windows users have a bad habit of removing files from recycle bin. Deleting useless files from Recycle Bin helps a user to save some storage space which can improve the speed of a computer. However, Windows users do that more frequently and later regret. Actually, Recycle Bin is a place from where we can get back our deleted files and folders.
Sometimes, we accidentally delete our important files and folders and due to some reason we lose them from Recycle bin too! At that time, we search for recovery methods. Well, there are many methods available over the web which can help you to recover deleted files from computer, but, when it comes to reliability, nothing can beat EaseUS.

What Is EaseUS Data Recovery Wizard?

EaseUS is one of the leading software providers which is known for its data recovery programme. EaseUS offers some free data recovery software that can help you to recover deleted, formatted or lost data from PC, laptop or removable device easily and quickly.
One of the best thing about EaseUS is that it provides tools for recycle bin recovery which can help you to get back files that you have deleted from the Recycle bin itself. EaseUS Data Recovery Wizard free can easily recover lost files, pictures, documents, videos and more from deleting, formatting, partition loss, OS crash, virus attack and other data loss cases.

How To Recover Deleted Files?

If you are struggling to get back your lost data files, then here is an easy data recovery method which will help you to get back deleted files on your computer. Below, we are going to share a mini guide on how to use EaseUS Recycle Bin Recovery to get back your deleted data.
Step 1. First of all, you need to download and install EaseUS Data Recovery Wizard on your Windows computer and then launch it normally. You need to select the location where you want to scan and then click on 'Scan'
Step 2. Now, wait for few minutes until the tool finishes scanning your drive. The tool search for files that got deleted or cleaned from the Recycle Bin.
Step 3. Now it will show you files that you can recover. Here you need to click on the file which you want to recover and click on 'Recover'
That's it, you are done! This is how you can use EaseUS data recovery to get back your deleted files on your computer.

Benefits of EaseUS Data Recovery

Well, the data recovery tool provided by EaseUS comes with many benefits. It can not only recover accidentally deleted files, but it can also recover data in case of Virus Attacks, Hard Disk Damage, OS Crash etc.
You can also use this awesome data recovery wizard to recover deleted files from External Disk, USB Drive, Camcorder, Mobile Devices, Zip Drive, iPod, Music Player and more.
Overall, this is one of the best data recovery tools you can have on your Windows computer. EaseUS Data Recovery also has a free version with restricted features. Overall, this is a stress-free solution to get back your deleted files

More information


CEH: Gathering Network And Host Information, Types Of Scan


In Hacking the main focus is over gathering the information about victim or victim's machine. Which will help to find out which type of exploit will works according to the given circumstances. Gathering the network and host information means to find out by which network, the which victim's machine is connected and communicating over the network. Moreover, scanning is also performed for gathering information about open and closed ports. After that they'll able to find the vulnerabilities in the target system and try to get access to the system.

Types Of Scan

As a CEH you should know the scan types and uses:

SYN

SYN scan doesn't complete the TCP three way handshake that is why it is known as a half-open scan. An attacker send a SYN packet to the victim machine if SYN/ACK packet is received back to attacker, then it clarify that the port is listening due to the acknowledgment by the victim that it has completed the connection. While if the attacker is received the RST/ACK packet then it assumed that the port is closed or open.


XMAS

XMAS scan works only on target system that has the RFC 793 development of TCP/IP and it doesn't works against any version of windows.
XMAS scan send a packet with by setting up the FIN, URG and PSH flags of the TCP header. The function of this scan is if the port is active there will be no response but if the port is closed the target responds with a RST/ACK packet.


FIN

A FIN scan send a packet by setting up only the FIN flag of the TCP. This scan is similar to XMAS scan. FIN scan receives no response if the port is active while if the port is closed it receives the RST/ACK packet.


NULL 

NULL scan is also similar to the XMAS scan. But the only difference is that it sends a packet without setting up the any flag of TCP header. NULL scan receives no response if the port is open but if the port is closed it receives the RST/ACK packet.


IDLE

It is just like spoofing an IP address by sending a SYN packet to the victim's machine to find out which services are available over the system. This scan is completed with the help of another system called as "Zombie" (that is not receiving or transmitting any information).


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Msticpy - Microsoft Threat Intelligence Security Tools

Microsoft Threat Intelligence Python Security Tools.

msticpy is a library for InfoSec investigation and hunting in Jupyter Notebooks. It includes functionality to:

  • query log data from multiple sources
  • enrich the data with Threat Intelligence, geolocations and Azure resource data
  • extract Indicators of Activity (IoA) from logs and unpack encoded data
  • perform sophisticated analysis such as anomalous session detection and time series decomposition
  • visualize data using interactive timelines, process trees and multi-dimensional Morph Charts

It also includes some time-saving notebook tools such as widgets to set query time boundaries, select and display items from lists, and configure the notebook environment.



The msticpy package was initially developed to support Jupyter Notebooks authoring for Azure Sentinel. While Azure Sentinel is still a big focus of our work, we are extending the data query/acquisition components to pull log data from other sources (currently Splunk, Microsoft Defender for Endpoint and Microsoft Graph are supported but we are actively working on support for data from other SIEM platforms). Most of the components can also be used with data from any source. Pandas DataFrames are used as the ubiquitous input and output format of almost all components. There is also a data provider to make it easy to and process data from local CSV files and pickled DataFrames.

The package addresses three central needs for security investigators and hunters:

  • Acquiring and enriching data
  • Analyzing data
  • Visualizing data

We welcome feedback, bug reports, suggestions for new features and contributions.


Installing

For core install:

pip install msticpy

If you are using MSTICPy with Azure Sentinel you should install with the "azsentinel" extra package:

pip install msticpy[azsentinel]

or for the latest dev build

pip install git+https://github.com/microsoft/msticpy


Documentation

Full documentation is at ReadTheDocs

Sample notebooks for many of the modules are in the docs/notebooks folder and accompanying notebooks.

You can also browse through the sample notebooks referenced at the end of this document to see some of the functionality used in context. You can play with some of the package functions in this interactive demo on mybinder.org.


Log Data Acquisition

QueryProvider is an extensible query library targeting Azure Sentinel/Log Analytics, Splunk, OData and other log data sources. It also has special support for Mordor data sets and using local data.

Built-in parameterized queries allow complex queries to be run from a single function call. Add your own queries using a simple YAML schema.

Data Queries Notebook


Data Enrichment

Threat Intelligence providers

The TILookup class can lookup IoCs across multiple TI providers. built-in providers include AlienVault OTX, IBM XForce, VirusTotal and Azure Sentinel.

The input can be a single IoC observable or a pandas DataFrame containing multiple observables. Depending on the provider, you may require an account and an API key. Some providers also enforce throttling (especially for free tiers), which might affect performing bulk lookups.

TIProviders and TILookup Usage Notebook


GeoLocation Data

The GeoIP lookup classes allow you to match the geo-locations of IP addresses using either:

GeoIP Lookup and GeoIP Notebook


Azure Resource Data, Storage and Azure Sentinel API

The AzureData module contains functionality for enriching data regarding Azure host details with additional host details exposed via the Azure API. The AzureSentinel module allows you to query incidents, retrieve detector and hunting queries. AzureBlogStorage lets you read and write data from blob storage.

Azure Resource APIs, Azure Sentinel APIs, Azure Storage


Security Analysis

This subpackage contains several modules helpful for working on security investigations and hunting:


Anomalous Sequence Detection

Detect unusual sequences of events in your Office, Active Directory or other log data. You can extract sessions (e.g. activity initiated by the same account) and identify and visualize unusual sequences of activity. For example, detecting an attacker setting a mail forwarding rule on someone's mailbox.

Anomalous Sessions and Anomalous Sequence Notebook


Time Series Analysis

Time series analysis allows you to identify unusual patterns in your log data taking into account normal seasonal variations (e.g. the regular ebb and flow of events over hours of the day, days of the week, etc.). Using both analysis and visualization highlights unusual traffic flows or event activity for any data set.


Time Series


Visualization

Event Timelines

Display any log events on an interactive timeline. Using the Bokeh Visualization Library the timeline control enables you to visualize one or more event streams, interactively zoom into specific time slots and view event details for plotted events.


Timeline and Timeline Notebook


Process Trees

The process tree functionality has two main components:

  • Process Tree creation - taking a process creation log from a host and building the parent-child relationships between processes in the data set.
  • Process Tree visualization - this takes the processed output displays an interactive process tree using Bokeh plots.

There are a set of utility functions to extract individual and partial trees from the processed data set.


Process Tree and Process Tree Notebook


Data Manipulation and Utility functions

Pivot Functions

Lets you use MSTICPy functionality in an "entity-centric" way. All functions, queries and lookups that relate to a particular entity type (e.g. Host, IpAddress, Url) are collected together as methods of that entity class. So, if you want to do things with an IP address, just load the IpAddress entity and browse its methods.

Pivot Functions and Pivot Functions Notebook


base64unpack

Base64 and archive (gz, zip, tar) extractor. It will try to identify any base64 encoded strings and try decode them. If the result looks like one of the supported archive types it will unpack the contents. The results of each decode/unpack are rechecked for further base64 content and up to a specified depth.

Base64 Decoding and Base64Unpack Notebook


iocextract

Uses regular expressions to look for Indicator of Compromise (IoC) patterns - IP Addresses, URLs, DNS domains, Hashes, file paths. Input can be a single string or a pandas dataframe.

IoC Extraction and IoCExtract Notebook


eventcluster (experimental)

This module is intended to be used to summarize large numbers of events into clusters of different patterns. High volume repeating events can often make it difficult to see unique and interesting items.



This is an unsupervised learning module implemented using SciKit Learn DBScan.

Event Clustering and Event Clustering Notebook


auditdextract

Module to load and decode Linux audit logs. It collapses messages sharing the same message ID into single events, decodes hex-encoded data fields and performs some event-specific formatting and normalization (e.g. for process start events it will re-assemble the process command line arguments into a single string).


syslog_utils

Module to support an investigation of a Linux host with only syslog logging enabled. This includes functions for collating host data, clustering logon events and detecting user sessions containing suspicious activity.


cmd_line

A module to support he detection of known malicious command line activity or suspicious patterns of command line activity.


domain_utils

A module to support investigation of domain names and URLs with functions to validate a domain name and screenshot a URL.


Notebook widgets

These are built from the Jupyter ipywidgets collection and group common functionality useful in InfoSec tasks such as list pickers, query time boundary settings and event display into an easy-to-use format.


 



More Notebooks on Azure Sentinel Notebooks GitHub

Azure Sentinel Notebooks

Example notebooks:

View directly on GitHub or copy and paste the link into nbviewer.org


Notebook examples with saved data

See the following notebooks for more examples of the use of this package in practice:


Supported Platforms and Packages

Contributing

For (brief) developer guidelines, see this wiki article Contributor Guidelines

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.



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