phoneinfoga

Pros of Social-Engineer-Toolkit

• Broader scope: Offers a wide range of social engineering tools and techniques
• More comprehensive: Includes phishing, website cloning, and other attack vectors
• Active development: Regularly updated with new features and improvements

Cons of Social-Engineer-Toolkit

• Steeper learning curve: Requires more technical knowledge to use effectively
• Larger footprint: More complex installation and setup process
• Potential for misuse: Powerful tools that could be used maliciously if not handled responsibly

Code Comparison

PhoneInfoga (Python):

def scan(self, number):
    if not is_valid_number(number):
        raise InvalidPhoneNumber("The number is not valid.")
    return self.scanner.scan(number)

Social-Engineer-Toolkit (Python):

def start_web_server(self, port):
    try:
        import SimpleHTTPServer, SocketServer
        Handler = SimpleHTTPServer.SimpleHTTPRequestHandler
        httpd = SocketServer.TCPServer(("", port), Handler)
        httpd.serve_forever()
    except Exception as e:
        print("[!] Error starting web server: " + str(e))

Both repositories use Python, but PhoneInfoga focuses on phone number reconnaissance, while Social-Engineer-Toolkit offers a broader range of social engineering tools. PhoneInfoga's code is more specialized for phone number validation and scanning, whereas Social-Engineer-Toolkit's code example shows its capability to set up web servers for various attack scenarios.

Pros of Sherlock

• Broader scope: Searches for usernames across 300+ websites
• More active community: Higher number of contributors and stars
• Supports multiple output formats (CSV, JSON, etc.)

Cons of Sherlock

• Focuses only on username searches, not phone numbers
• May produce false positives due to the large number of sites checked
• Requires more dependencies and setup compared to PhoneInfoga

Code Comparison

PhoneInfoga (Go):

func (s *Scanner) ScanNumber(number string) (*Number, error) {
    n, err := NewNumber(number)
    if err != nil {
        return nil, err
    }
    return s.Scan(n)
}

Sherlock (Python):

def sherlock(username, site_data, verbose=False, tor=False, unique_tor=False,
             proxy=None, print_found_only=False, timeout=None, color=True):
    for site in site_data:
        result = sherlock_site(username, site, verbose, tor, unique_tor, proxy,
                               timeout)
        if result:
            results_site.append(result)

Both projects serve different purposes: PhoneInfoga specializes in phone number intelligence gathering, while Sherlock focuses on username searches across multiple platforms. PhoneInfoga is written in Go, offering better performance, while Sherlock is in Python, providing easier customization. Choose based on your specific needs: phone number analysis or username discovery.

Pros of theHarvester

• Broader scope: Gathers information from various sources beyond phone numbers
• More versatile: Can be used for general OSINT, not just phone-related data
• Supports multiple search engines and APIs

Cons of theHarvester

• Less specialized: May not provide as detailed phone-specific information
• Potentially overwhelming results for users focused solely on phone intelligence
• Requires more configuration and setup for optimal use

Code Comparison

TheHarvester:

from theHarvester.discovery import *
from theHarvester.discovery.constants import *
search = googlesearch.search_google(word, limit, start)
search.process()

PhoneInfoga:

number, err := phonenumbers.Parse(input, "")
if err != nil {
    return nil, err
}
return &Number{Input: input, International: number.String()}, nil

TheHarvester is written in Python and uses a modular approach for different search engines. PhoneInfoga is written in Go and focuses specifically on phone number parsing and validation.

TheHarvester offers a broader range of information gathering capabilities, while PhoneInfoga specializes in phone number intelligence. TheHarvester may be more suitable for general OSINT tasks, whereas PhoneInfoga excels in phone-specific investigations. Users should choose based on their specific needs and the depth of phone-related information required.

Pros of SpiderFoot

• More comprehensive OSINT tool with broader functionality beyond phone numbers
• Supports a wide range of data sources and modules for information gathering
• Offers a web-based GUI for easier interaction and visualization of results

Cons of SpiderFoot

• More complex setup and configuration compared to PhoneInfoga
• Requires more system resources due to its extensive feature set
• Steeper learning curve for new users

Code Comparison

PhoneInfoga (Python):

def parse_number(number):
    return phonenumbers.parse(number, None)

def is_valid_number(number):
    return phonenumbers.is_valid_number(parse_number(number))

SpiderFoot (Python):

def parseTargets(self, target):
    ret = list()
    if target.lower().startswith('http://') or target.lower().startswith('https://'):
        ret.append(target)
    else:
        ret.extend(self.sf.resolveHost(target))
    return ret

Both projects use Python, but their code focuses on different aspects. PhoneInfoga specializes in phone number parsing and validation, while SpiderFoot has a broader scope, handling various target types including URLs and hostnames.

Pros of Photon

• Broader scope: Photon is a general-purpose OSINT tool for web reconnaissance, while PhoneInfoga focuses specifically on phone number information gathering
• More versatile: Can extract various types of data from websites, including emails, social media links, and subdomains
• Active development: More recent commits and updates compared to PhoneInfoga

Cons of Photon

• Less specialized: May not provide as detailed phone number information as PhoneInfoga
• Steeper learning curve: Due to its broader functionality, it might be more complex for users solely interested in phone number intelligence

Code Comparison

Photon:

def photon(url, level, threadCount, delay, timeout, headers):
    # ... (initialization code)
    for level in range(level):
        links = extract_links(url)
        # ... (link processing and data extraction)

PhoneInfoga:

func localScan(number *Number) (*Results, error) {
    // ... (initialization code)
    results.Number = number
    results.Local = &LocalScan{
        Format: &LocalFormat{
            International: number.International,
            Local:         number.Local,
        },
    }
    // ... (number processing and data gathering)
}

Both projects use different programming languages and approaches, reflecting their distinct purposes. Photon's code focuses on web crawling and link extraction, while PhoneInfoga's code is tailored for phone number analysis and formatting.

Pros of GHunt

• Focuses on Google account information gathering, offering more in-depth Google-specific data
• Provides a graphical user interface (GUI) for easier use
• Supports multiple search methods, including email, phone number, and Google ID

Cons of GHunt

• Limited to Google account information, while PhoneInfoga offers broader phone number intelligence
• Requires more setup and dependencies, including a Google Chrome/Chromium installation
• May be less frequently updated compared to PhoneInfoga

Code Comparison

PhoneInfoga (Python):

def scan(self, number):
    if not self.validate(number):
        raise InvalidPhoneNumber('The phone number is not valid.')
    return self.fetch_info(number)

GHunt (Python):

def hunt(email):
    gaiaID = get_gaia_id(email)
    profile = get_profile(gaiaID)
    return parse_profile(profile)

Both projects use Python, but their focus and implementation differ. PhoneInfoga is centered around phone number scanning, while GHunt targets Google account information retrieval. PhoneInfoga's code snippet shows a method for validating and fetching phone information, whereas GHunt's code demonstrates the process of hunting for Google account details using an email address.