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Showing posts with label Threat Intelligence. Show all posts
Showing posts with label Threat Intelligence. Show all posts

Saturday, May 11, 2024

Harnessing the Deep and Dark Web for Cyber Threat Intelligence


As cyber threats evolve, so must our strategies to combat them. The deepdarkCTI project serves as a crucial resource, offering access to a curated collection of intelligence from the Deep and Dark Web. This repository is a goldmine for those in cyber security, providing tools and data that are pivotal for both defensive measures and offensive strategies.


From detailed exploits and vulnerability patches found in obscure forums, to the tracking of ransomware groups' tactics and communication in encrypted channels—every piece of data can be leveraged. Moreover, our community-driven approach allows enthusiasts and professionals to contribute and stay ahead with the latest tactics and techniques discussed in our dedicated Telegram group.


For individuals looking to delve deeper or contribute, detailed methodologies for source analysis are available, ensuring that every user can effectively apply this intelligence. Whether you’re defending an organization or testing its defenses, the insights gained from these sources are invaluable.


Join and contribute to the deepdarkCTI project today to stay at the forefront of cybersecurity intelligence.


Explore more on GitHub

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Friday, February 23, 2024

RepoReaper - An Automated Tool Crafted To Meticulously Scan And Identify Exposed .Git Repositories Within Specified Domains And Their Subdomains


RepoReaper is a precision tool designed to automate the identification of exposed .git repositories across a list of domains and subdomains. By processing a user-provided text file with domain names, RepoReaper systematically checks each for publicly accessible .git files. This enables rapid assessment and protection against information leaks, making RepoReaper an essential resource for security teams and web developers.

Features
  • Automated scanning of domains and subdomains for exposed .git repositories.
  • Streamlines the detection of sensitive data exposures.
  • User-friendly command-line interface.
  • Ideal for security audits and Bug Bounty.

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/YourUsername/RepoReaper.git
cd RepoReaper
pip install -r requirements.txt
chmod +x RepoReaper.py

Usage

RepoReaper is executed from the command line and will prompt for the path to a file containing a list of domains or subdomains to be scanned.

To start RepoReaper, simply run:

./RepoReaper.py
or
python3 RepoReaper.py

Upon execution, RepoReaper will ask for the path to the file containing the domains or subdomains: Enter the path of the file containing domains

Provide the path to your text file when prompted. The file should contain one domain or subdomain per line, like so:

example.com
subdomain.example.com
anotherdomain.com

RepoReaper will then proceed to scan the provided domains or subdomains for exposed .git repositories and report its findings. 


Disclaimer

This tool is intended for educational purposes and security research only. The user assumes all responsibility for any damages or misuse resulting from its use.


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Sunday, February 18, 2024

Navigating Telegram’s Underworld: A Cipher for the Elite Hackers




In the encrypted depths of Telegram, far beyond the scrutiny of average netizens, lies a network pulsating with the lifeblood of the hacking elite. This isn’t your run-of-the-mill tutorial or a hacker’s 101 guide. This post is a deep dive into the abyss, mapping the veins of active and dormant channels that are the backbone of cyber threat intelligence and underground hacking operations.

The channels we’re dissecting today are not just communication lines; they are the hidden layers of the onion, each peel revealing more about the dark arts of digital dominance. From active dens where real-time data breaches, exploit trades, and botnet controls unfold, to the ghostly silence of channels once alive with the chatter of codes and hacks now lying dormant or expired - every link, every channel, serves as a node in the vast neural network of the global hacking community.

Active Channels: The Frontlines

Here, in the buzzing hive of active channels, you're as likely to find a zero-day exploit as you are a discussion on the latest evasion techniques. This isn't just about sharing tools or data; it's a relentless innovation race. Techniques, scripts, and methodologies discussed here are not for the faint-hearted but for those who command the terminal like it’s an extension of their mind.

Dormant/Expired Channels: The Archives

The silent corridors of expired channels are not just digital tombs; they are treasure troves of past operations, a testament to the ephemeral nature of digital power. Each one holds lessons, failures, and victories. They are the archives for those willing to learn from history to master the future.



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Wednesday, February 9, 2022

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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Sunday, February 6, 2022

Cynet's Keys to Extend Threat Visibility

 


We hear about the need for better visibility in the cybersecurity space – detecting threats earlier and more accurately. We often hear about the dwell time and the time to identify and contain a data breach. Many of us are familiar with IBM's Cost of a Data Breach Report that has been tracking this statistic for years. In the 2021 report, IBM found that, on average, it takes an average of 212 days to identify a breach and then another 75 days to contain the breach, for a total of 287 days.

A new solution overview document provides insights on how XDR provider Cynet tackles the difficult problem of greatly improving threat visibility. Cynet takes a modern approach that includes a greater level of native technology integration and advanced automation purposely designed for organizations with smaller security teams than Fortune 500 organizations. A live webinar will discuss the same topic (Register here)

Cynet's Keys for Threat Visibility

Einstein said that the definition of insanity is doing the same thing over and over while expecting a different outcome. The old approach to threat visibility involving multiple protection technologies and trying to sift through a sea of alerts and information is obviously not working well. Cynet's different – and seemingly saner – approach to prevent, detect, and respond to modern-day threats involves several integrated capabilities.

According to the new Cynet solution overview, the following key technologies are used to provide extended threat visibility along with enhanced response capabilities.

Include Multiple Threat Detection Technologies

Cynet includes multiple prevention and detection technologies, all natively orchestrated in the platform:

  • NGAV – Fundamental endpoint protection based on known bad signatures and behaviors.
  • EDR – To detect and prevent more complex endpoint threats that bypass NGAV solutions.
  • NTA – To detect threats that have made their way into the network and so-called lateral movement.
  • UBA – To detect unusual activity that could signal stolen credentials, a rogue insider, or bots.
  • Deception – To uncover intrusions that have bypassed other detection technologies
  • CLM – To mine the extensive log data generated by IT systems.
  • SSPM – To find and correct configuration errors in SaaS applications.

Coordinate All Signals

Making sense out of multiple detection technologies by integrating, coordinating, and prioritizing information was supposed to be the realm of Security Incident and Event Management (SIEM) technologies. Unfortunately, SIEM doesn't do well with real-time data and requires significant care and feeding.

XDR solutions, like Cynet's, are purpose-built to integrate real-time signals from multiple points of telemetry on a single platform. Cynet even includes an Incident Engine that automatically investigates threats to determine the attack's full scope and root cause.

Automate All Response Actions

Quickly and accurately identifying threats is a game-changer. The ability to automatically and fully eradicate identified threats is, well, a game-changer changer. This means security teams won't have to be burdened with lengthy investigations, which many don't have the time or skills to undertake. Cynet provides an extensive set of remediation actions across files, hosts, networks and users as well as remediation playbooks that can be configured to be invoked manually or automatically.

Provide Full MDR Oversight

Beyond the technology platform, Cynet offers all clients a full, 24x7 MDR service at no additional cost. This team continuously monitors client environments to ensure nothing dangerous is overlooked or mishandled. Having an expert team watching out for issues should put smaller organizations with smaller security teams at ease, knowing an expert team of cybersecurity experts has their backs.

In With the New

With the time required to identify and contain data breaches steadily increasing, we need to rethink the traditional cybersecurity approach. It seems companies keep throwing more money, more technology, and more bodies at the problem, yet achieving the same (or worse) results. Cynet is one company that seems to be approaching the problem differently by combining multiple prevention, detection, response, and automation capabilities on a single, unified breach protection platform. Rather than buying all this stuff separately and munging it all together, the Cynet platform seems to expand and improve threat visibility out of the box.

Download the solution brief here

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Kunyu - More Efficient Corporate Asset Collection


0x00 Introduce

Tool introduction

Kunyu (kunyu), whose name is taken from , is actually a professional subject related to geographic information, which counts the geographic information of the sea, land, and sky. The same applies to cyberspace. The same is true for discovering unknown and fragile assets. It is more like a cyberspace map, which is used to comprehensively describe and display cyberspace assets, various elements of cyberspace and the relationship between elements, as well as cyberspace and real space. The mapping relationship. So I think "Kun Yu" still fits this concept.

Kunyu aims to make corporate asset collection more efficient and enable more security-related practitioners to understand and use cyberspace surveying and mapping technology.


Application scenario

For the use of kunyu, there can be many application scenarios, such as:

  • Forgotten and isolated assets in the enterprise are identified and added to security management.
  • Perform quick investigation and statistics on externally exposed assets of the enterprise.
  • Red and blue are used against related requirements, and batch inspections of captured IPs are performed.
  • Collect vulnerable assets in batches (0day/1day) for equipment and terminals within the impact.
  • Information on sites involved in new-type cybercrime cases is quickly collected and merged for more efficient research, judgment, and analysis.
  • Statistic and reproduce the fragile assets on the Internet that are affected by related vulnerabilities.

0x01 Install

Need Python3 or higher support

git clone https://github.com/knownsec/Kunyu.git
cd Kunyu
pip3 install -r requirements.txt

Linux:
python3 setup.py install
kunyu console

Windows:
cd kunyu
python3 console.py

PYPI:
pip3 install kunyu

P.S. Windows also supports python3 setup.py install.

0x02 Configuration instructions

When you run the program for the first time, you can initialize by entering the following command. Other login methods are provided. However, it is recommended to use the API method. Because the user name/password login requires an additional request, the API method is theoretically more efficient.

kunyu init --apikey <your zoomeye key> --seebug <your seebug key>



You need to log in with ZoomEye credentials before using this tool for information collection.

Visit address: https://www.zoomeye.org/

The output file path can be customized by the following command

kunyu init --output C:\Users\风起\kunyu\output



0x03 Tool instructions

Detailed command

kunyu console


 

ZoomEye

Encryption method interface HostCrash <IP> <Domain> Host Header Scan hidden assets Seebug <Query> Search Seebug vulnerability information set <Option> Set arguments values Pocsuite3 Invoke the pocsuite component ExportPath Returns the path of the output file clear Clear the console screen show Show can set options help Print Help info exit Exit KunYu & ">
Global commands:
info Print User info
SearchHost <query> Basic Host search
SearchWeb <query> Basic Web search
SearchIcon <File>/<URL> Icon Image search
SearchBatch <File> Batch search Host
SearchCert <Domain> SSL certificate Search
SearchDomain <Domain> Domain name associated/subdomain search
EncodeHash <encryption> <query> Encryption method interface
HostCrash <IP> <Domain> Host Header Scan hidden assets
Seebug <Query> Search Seebug vulnerability information
set <Option> Set arguments values
Pocsuite3 Invoke the pocsuite component
ExportPath Returns the path of the output file
clear Clear the console screen
show Show can set options
help Print Help info
exit Exit KunYu &

OPTIONS

ZoomEye:
page <Number> The number of pages returned by the query
dtype <0/1> Query associated domain name/subdomain name
btype <host/web> Set the API interface for batch query

Use case introduction

Here we use the ZoomEye module for demonstration

User information query


HOST host search


Web host search


Batch IP search


Icon Search

When collecting corporate assets, we can use this method to retrieve the same ico icon assets, which usually has a good effect when associating related corporate assets. But it should be noted that if some sites also use this ico icon, irrelevant assets may be associated (but people who are bored with other people's ico icons are always in the minority). Support url or local file search.



 

SSL certificate search

Query through the serial number of the SSL certificate, so that the associated assets are more accurate, and services that use the same certificate can be searched. When you encounter an https site, you can use this method.



Multi-factor query

Similarly, Kunyu also supports multi-factor conditional query related assets, which can be realized through ZoomEye logic operation syntax.


 

Feature Search

Through HTTP request packet features or website-related features, the same framework assets can be concatenated more accurately



Associated Domain/Subdomain Search

Search for associated domain names and subdomains, and query associated domain names by default. Two modes can be set by setting the dtype parameter.


 

Encoding hash calculation

In some scenarios, you can use this command to perform common HASH encryption/encoding, such as BASE64, MD5, mmh3, HEX encoding, and debug in this way.



Seebug vulnerability query

You can query historical related vulnerabilities by entering information about the framework and equipment you want to find, but you need to note that only English is supported, and improvements and upgrades will be made later.



Setting parameters

When set page = 2, the returned results are 40. You can modify the page parameter to set the number of pages to be queried. Note that 1 page = 20/items. You can modify the value according to your needs to get more returned results.

The configurable parameters and the current values of the parameters are displayed through show.


 


Pocsuite linkage

In versions after v1.3.1, you can use kunyu to link the console mode of pocsuite3 for integrated use.



HOSTS head collision

Through the HOSTS collision, the hidden assets in the intranet can be effectively collided, and the intranet service can be accessed according to the ServerName domain name and IP configured in the middleware httpf.conf. This can be achieved by setting the local hosts file later, because the local hosts file takes precedence. The level is higher than DNS server resolution. Support reverse check through ZoomEye domain name library or read TXT file to get the list of domain names.

HOSTS cross collision



Data result

All search results are saved in the user's root directory, and the directory is created based on the current timestamp. All query results of a single start are stored in an Excel format under one directory, giving a more intuitive experience. The output path can be returned through the ExportPath command.



0x04 Loading

​ In fact, there are still many ideas, but as an Alpha version, this is the case, and it will continue to be improved in the later period. I hope that Kunyu can be known to more security practitioners. Thank you for your support.

​ The tool framework has reference to Kunlun Mirror and Pocsuite3, which are all very good works.

​ Thanks to all the friends of KnownSec 404 Team.

“ 看得清 ” 是能力的体现,是 “ 器 ” ,而 “ 看得见 ” 就是思想的体现,那最后关联的是 “ 道 ”。

​ --SuperHei


0x05 Issue

1、Multi-factor search

ZoomEye search can use multi-factor search, dork:cisco +port:80 (note the space) can search all data that meet the conditions of cisco and port:80, if there is no space in between, it is the same search condition, it is that cisco is satisfied and the port is All data for 80. Kunyu's dork does not require quotation marks.

2、High-precision geographical location

ZoomEye gives privileged users high-precision geographic location data, but it should be noted that ordinary users do not have this function, so I hope you know.

3、Username/password login

If you use username/password as the initialization condition, the token will be valid for 12 hours. If you find that your search cannot return data, you may wish to info. If the session times out, the initialization command prompt will be returned. In most cases, we recommend that you use the API KEY method, there is no invalidation problem. This design is also for the security of your account and password. After all, the API KEY can be reset and the token will become invalid. However, with the account and password, it is possible to log in to your ZoomEye account.

4、Cert certificate search

It should be noted that, according to the normal logic, you need to encode the serial number of the target SSL certificate in hexadecimal to match the sentence search, but Kunyu only needs to provide the Domain address to search. The principle is to make a request to the target station to obtain the serial number and process it, but if your host cannot access the target that needs to be searched, it cannot be retrieved. At this time, you can also search with the sentence in the usual way.

5、Favicon icon search

ico icon search not only supports URL retrieval, but also supports local ico icon file search, which has better scalability and compatibility.

6、Query data save path

By default, your query data is in the Kunyu folder under the user directory. You can also use the ExportPath command to query the path in the console mode.

7、Autocomplete

Kunyu's auto-completion supports upper and lower case, command logging, etc., use Tab to complete, please refer to Metasploit for usage.

8. Regarding the error when using pip install kunyu

The following error was reported when using pip install kunyu: File "C:\Users\风起\AppData\Local\Programs\Python\Python37\Scripts\kunyu-script.py", line 1 SyntaxError: Non-UTF-8 code starting with'\xb7' in file C: \Users\风起\AppData\Local\Programs\Python\Python37\Scripts\kunyu-script.py on line 1, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details

solution: Modify the C:\Users\风起\AppData\Local\Programs\Python\Python37\Scripts\kunyu-script.py file and add # encoding: utf-8 at the beginning of the file.

Then save it and you can use it normally. The bug appears because there is a Chinese name in the user's directory path, which usually appears on windows.

9. Pocsuite3 module POC storage directory

When using the pocsuite3 module, if you want to add a new POC module, you can add a POC file in project directory/kunyu/pocs/.

10. Pocsuite3 module POC missing issue

When using the Pocsuite command linkage, if it is a packaged Kunyu version, the poc has been fixed. At this time, modifying the poc directory cannot add new modules. At this time, you can repackage it or use the project directory/kunyu /console.py Run kunyu to update the poc module in real time.


0x06 Contributions

风起@knownsec 404
wh0am1i@knownsec 404
fenix@knownsec 404
0x7F@knownsec 404


0x07 Community

If you have any questions, you can submit an issue under the project, or contact us through the following methods.

Scan the QR code to add the ZoomEye staff member WeChat, and remark Kunyu, which will draw everyone to the ZoomEye cyberspace surveying and mapping exchange group




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