Pros of Photon

• More versatile, capable of crawling and extracting various types of data from websites
• Actively maintained with regular updates and bug fixes
• Supports multiple output formats (JSON, CSV, etc.)

Cons of Photon

• Less focused on IoT device discovery compared to Kamerka
• May require more setup and configuration for specific use cases
• Potentially overwhelming for users seeking a simple IoT device scanner

Code Comparison

Kamerka (device discovery):

def shodan_search(query):
    try:
        results = api.search(query)
        return results
    except Exception as e:
        print("Error: %s" % e)
        return None

Photon (web crawling):

def photon(seedurl, headers, level, threadCount):
    dataset = set() # urls found
    processing_queue = deque([seedurl]) # urls to crawl
    for i in range(level):
        urls = extract_urls(processing_queue, threadCount)
        dataset.update(urls)
        processing_queue = urls
    return dataset

The code snippets highlight the different focus areas of each tool. Kamerka is geared towards IoT device discovery using Shodan, while Photon is designed for web crawling and data extraction.

Pros of theHarvester

• More comprehensive data gathering capabilities across multiple sources
• Longer development history and larger community support
• Supports both passive and active information gathering techniques

Cons of theHarvester

• Less focused on visual representation of gathered data
• May require more manual analysis of results
• Not specifically designed for IoT device discovery

Code Comparison

theHarvester:

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

Kamerka:

def shodan_search(query):
    api = shodan.Shodan(shodan_api)
    try:
        results = api.search(query)
        return results
    except shodan.APIError as e:
        print('Error: {}'.format(e))

Both tools utilize external APIs for data gathering, but theHarvester focuses on a broader range of sources, while Kamerka is more specialized for IoT device discovery using Shodan. theHarvester's code snippet demonstrates its use of Google search, while Kamerka's code shows its integration with Shodan API for targeted IoT searches.

Pros of PhoneInfoga

• More focused and specialized tool for phone number information gathering
• Regularly updated with recent commits and active development
• Provides a web interface for easier use and visualization of results

Cons of PhoneInfoga

• Limited to phone number information, while Kamerka offers broader IoT device scanning
• Requires more setup and dependencies compared to Kamerka's simpler installation
• Less versatile in terms of target types and scanning capabilities

Code Comparison

PhoneInfoga (Python):

def parse_number(number):
    try:
        phonenumber = phonenumbers.parse(number, None)
    except NumberParseException as e:
        return {'error': str(e)}
    return {'number': phonenumbers.format_number(phonenumber, PhoneNumberFormat.E164)}

Kamerka (Python):

def shodan_search(query):
    try:
        results = api.search(query)
        return results
    except Exception as e:
        print("Error: %s" % e)
        return None

Both projects use Python, but PhoneInfoga focuses on parsing and formatting phone numbers, while Kamerka utilizes Shodan API for broader IoT device searching. PhoneInfoga's code is more specialized for phone number handling, whereas Kamerka's code is geared towards general IoT device discovery and information gathering.

Pros of SpiderFoot

• More comprehensive OSINT tool with a wider range of data sources and modules
• User-friendly web interface for easier operation and result visualization
• Actively maintained with regular updates and a larger community

Cons of SpiderFoot

• Requires more setup and configuration compared to Kamerka's simpler approach
• May be overwhelming for users seeking a more focused, camera-specific tool
• Higher resource consumption due to its broader scope and functionality

Code Comparison

SpiderFoot (Python):

class sfp_example(SpiderFootPlugin):
    def handleEvent(self, event):
        eventName = event.eventType
        srcModuleName = event.module
        eventData = event.data
        # Plugin logic here

Kamerka (Python):

def search_shodan(query):
    try:
        results = api.search(query)
        return results
    except Exception as e:
        print(f"Error: {e}")
        return None

SpiderFoot offers a more structured plugin system, allowing for easier extension of functionality. Kamerka's code is more straightforward, focusing on specific tasks like Shodan searches.

Both tools serve different purposes: SpiderFoot is a comprehensive OSINT framework, while Kamerka specializes in discovering and mapping internet-connected cameras and other IoT devices. The choice between them depends on the user's specific needs and level of expertise in OSINT investigations.

Pros of cloud_enum

• Focuses on enumerating public cloud storage across multiple providers (AWS, Azure, Google)
• Lightweight and easy to use with minimal dependencies
• Provides detailed output and supports various output formats

Cons of cloud_enum

• Limited to cloud storage enumeration, while kamerka offers broader IoT device discovery
• Less visual representation of results compared to kamerka's mapping features
• May require more manual analysis of results for actionable insights

Code Comparison

cloud_enum:

def check_storage(storage_name):
    url = f"https://{storage_name}.blob.core.windows.net"
    try:
        response = requests.get(url, timeout=3)
        if response.status_code == 404:
            return False
        return True
    except requests.exceptions.RequestException:
        return False

kamerka:

def search_shodan(query):
    try:
        results = api.search(query)
        for result in results['matches']:
            ip = result['ip_str']
            port = result['port']
            org = result.get('org', 'N/A')
            data = result['data']
            print(f"IP: {ip}:{port}\nOrganization: {org}\nData: {data}\n")
    except shodan.APIError as e:
        print(f"Error: {e}")

Both tools utilize HTTP requests for data gathering, but cloud_enum focuses on cloud storage detection, while kamerka leverages Shodan API for broader IoT device discovery.