Cybercrime is on track to cost the world $11.88 trillion in 2026, with the trajectory pointing toward $19.71 trillion by 2030. The threat industrialized over 2025: payment volumes fell as attack volumes climbed to record highs. AI lowered the barrier to entry for new attackers. Manufacturing absorbed the largest year-over-year ransomware surge. Third-party breaches doubled.

This article distills the key findings from our full Global Cybercrime Report 2026, which combines forecast modeling, third-party primary research, and Proxyrack's internal calendar 2025 KYC enforcement data into a single picture of where the threat is heading.

1. Global cybercrime cost is estimated to reach $11.88 trillion in 2026

, up from $10.5 trillion in 2025, on a path to a projected $19.71 trillion by 2030. That figure approximates the GDP of China and exceeds the combined GDP of Germany, the UK, and India.

2. The 10 highest-risk countries cluster across emerging markets

 in Latin America, Africa, and the Middle East. The 2026 update places Myanmar, Haiti, and the DR Congo at the top, reflecting Basel AML Index 2025 data. Nordic countries continue to dominate the safest end.

3. Ransomware payments fell roughly 8% to an estimated $820 million in 2025

 (Chainalysis), but the median payment per victim climbed 368%. Attack volume reached the highest level on record. Q1 2026 trackers show activity holding at the elevated 2025 plateau.

4. Manufacturing absorbed a 61% surge in ransomware activity

 in 2025. Healthcare took 290+ ransomware incidents at provider organizations. SMBs faced disproportionate exposure: 88% of ransomware-affected organizations had fewer than 1,000 employees (Sophos).

5. AI lowered the skill floor for cybercriminals

 rather than raising the ceiling. The FunkSec ransomware group, whose developers told researchers they were not coders, used generative AI to ship working malware and reportedly claimed 113 victims by March 2025.

6. Proxyrack internal KYC enforcement data

 for calendar 2025 shows the United States accounted for the largest single share of suspended accounts (26.32%), followed by a tier of 6 countries at 5.26% each (Brazil, Hong Kong, Indonesia, Cambodia, Nigeria, Poland). The dataset reflects Proxyrack's platform enforcement activity and is not a measurement of global cybercrime distribution.

Global Cybercrime Cost: $11.88 Trillion in 2026

Cybercrime is estimated to cost the world $11.88 trillion in 2026, growing at roughly 13% compound annually through 2030. The forecast comes from Proxyrack's internal model, which uses Cybersecurity Ventures historical baseline data only (2015-2023) and applies growth-rate inputs derived from Verizon DBIR breach prevalence trends, IBM Cost of a Data Breach annual averages, and Chainalysis crypto-crime payment volumes.

The 2025 actual landed within 4% of our prior-year projection, which means the model is calibrated against ground truth. Three structural shifts amplify the trajectory: AI-augmented attack tooling has expanded the attacker pool, the Initial Access Broker economy now functions as a 30-day leading indicator for ransomware activity, and third-party breaches doubled to 30% of all breaches in 2025. For deeper analysis on direct breach costs, see our 

The 2026 update places Myanmar at the top of the risk list with a Cybercrime Risk Score of 8.43, followed by Haiti at 8.12 and the Democratic Republic of the Congo at 7.94. Venezuela, Panama, Costa Rica, Chile, Saudi Arabia, Uruguay, and the UAE round out the top 10. The shift at the top reflects the Basel AML Index 2025 update, which elevated Myanmar, Haiti, and the DRC to the highest-risk tier for financial crime vulnerability. For the previous year's rankings, see our 

Finland, Iceland, and San Marino take the safest spots. Nordic countries continue to dominate the safest end of the rankings, with mandatory cybersecurity legislation, deep public-private cooperation, and high digital infrastructure investment per capita all reinforcing each other.

The Proxyrack Cybercrime Risk Score combines five international indices weighted equally: the Basel AML Index 2025 (14th Public Edition), the Cybersecurity Exposure Index (held at 2020 because no newer edition exists), the National Cyber Security Index (live data), the Digital Development Level (live data), and the ITU Global Cybersecurity Index v5 (held at 2024 because v6 has not been released). Each index is normalized to a 0-10 scale before averaging. The methodology is unchanged from the 2025 Proxyrack report to preserve year-over-year comparability.

Ransomware in 2026: Industry Restructured, Did Not Weaken

Ransomware payments fell roughly 8% to an estimated $820 million in 2025 (Chainalysis), the second consecutive annual decline. At the same time, attack volume reached the highest level on record. The two facts together describe an industry that did not weaken. It restructured around a smaller pool of paying victims, a fragmented supplier base, and an AI-enabled new class of low-effort operators.

70 active ransomware groups tracked in Q1 2026

 (Ransomware.live), up from 67 a year earlier and roughly double the count from 2022. New groups keep arriving even as established names get disrupted by law enforcement.

Top 10 groups captured 73% of payment volume

 (Chainalysis 2026 Crypto Crime Report). The long tail of 60+ smaller groups absorbs the rest, often through volume-over-value pricing.

 from 2023 to 2024 ($175,000 to $814,000) and continued rising in 2025. The smaller pool of organizations that still pay are paying significantly more per incident.

Manufacturing absorbed a 61% year-over-year surge

 in ransomware activity. The Akira, Qilin, and Play groups dominated the segment.

Public incidents anchored the year. The Marks & Spencer breach in April and May 2025, attributed to Scattered Spider, disrupted UK retail operations. The Jaguar Land Rover incident has been reported in industry coverage to have inflicted multi-billion-dollar damages. The DaVita ransomware breach is reported to have exposed approximately 2.7 million patient records.

AI-Powered Cybercrime: The Skill Floor Collapse

AI cybercrime is the dominant cybersecurity narrative of 2026, but the popular framing has it backwards. The risk is not that AI created a small number of more dangerous attackers. The risk is that AI eliminated the apprenticeship that previously kept low-skill actors out of the cybercrime market, creating a much larger pool of mediocre but functional attackers operating at higher volume.

FunkSec is the case study. The group launched in late 2024 with developers who told researchers they were not coders. Using generative AI tools to produce Rust-based malware, they shipped a functional ransomware payload in weeks. By March 2025, FunkSec had reportedly claimed 113 victims across 10 countries, ranking among the top groups by victim count for that quarter despite the small team size and limited capital.

IBM's 2025 study of 600 breached organizations found that 

16% of breaches involved AI-assisted attacks

, most commonly in the phishing and impersonation categories. The percentage is rising quarter over quarter according to IBM's tracking through Q1 2026.

The defensive side of the AI shift is more encouraging. IBM found that organizations using AI tools in security operations cut their breach lifecycle by 80 days and saved nearly 

 compared to organizations without AI in the SOC.

Supply Chain Attacks: The Largest Single-Year Shift

Third-party breaches doubled in 2025 to 30% of all breaches, the largest single-year shift in attack vector composition Verizon has recorded in the DBIR's history. Vendor risk management still receives a small fraction of enterprise security spend despite this exposure. For the broader picture of which industries are most exposed, see our 

The increase reflects two shifts. Organizations have continued to outsource more operations to specialized vendors. Attackers have learned that compromising one well-positioned vendor can cascade access to dozens or hundreds of downstream organizations.

Proxyrack Internal Data: KYC Enforcement Record for Calendar 2025

Proxyrack operated a Know Your Customer (KYC) verification process throughout calendar 2025 as one layer of its anti-abuse stack. The dataset below captures all account suspensions executed under that process during the full 2025 calendar year. Total suspensions: 76 accounts.

The United States accounted for the largest single share at 26.32% of suspensions. A second tier of six countries (Brazil, Hong Kong, Indonesia, Cambodia, Nigeria, Poland) each accounted for 5.26%. A third tier of four countries (India, Netherlands, Pakistan, Vietnam) each accounted for 2.63%. The remaining 24 countries each accounted for 1.32%.

Important caveats. The figures represent share of total suspensions by country, not in-country suspension rates. The dataset is small (76 total accounts) and reflects activity on the Proxyrack platform during one calendar year. It is not a measurement of global cybercrime distribution.

Proxyrack's KYC verification process was retired in late 2025 as the company consolidated its anti-abuse stack around its 

, which provides ongoing real-time enforcement at the device, network, and session layers.

What This Means for Security Teams in 2026

The defining ransomware shift of 2025 is not in attack count or payment dollars. It is in the supply chain. Initial Access Brokers, AI-enabled small operators like FunkSec, and the splintering of LockBit-class groups into a long tail of low-skill teams have changed the threat model. The old defensive playbook assumed a small number of well-resourced attackers. The 2026 reality is a much larger pool of lower-skill operators working at significantly higher volume, supported by increasingly accessible credential and access markets.

- Katy Salgado, Operations Manager, Proxyrack

Treat the Initial Access Broker market as a leading indicator, not a downstream artifact. When IAB listings spike in your sector, ransomware activity in the same sector spikes 30 days later. Build that into your threat intelligence cadence.

Build vendor security review into the same priority tier as identity and access. Third-party breaches at 30% of all breaches mean the supply chain is now a primary attack surface, not a secondary concern.

Design defensive architecture for the high-volume low-skill AI-enabled attacker, not just for the sophisticated operator. Detection logic optimized for nation-state TTPs will miss the FunkSec-class segment because there is nothing distinctive to detect.

The 2026 cybercrime cost forecast is produced by Proxyrack's internal model using Cybersecurity Ventures historical baseline data only and growth-rate inputs derived from current Verizon DBIR, IBM, and Chainalysis data. The country rankings methodology is unchanged from the 2025 Proxyrack report to preserve year-over-year comparability. The KYC suspension dataset reflects Proxyrack platform enforcement activity for calendar 2025 only.

Primary external sources cited in this article and the full report include the Chainalysis 2026 Crypto Crime Report, Verizon DBIR 2025, Sophos State of Ransomware 2025, IBM Cost of a Data Breach Report 2025, Halcyon 2026 Power Rankings, IPinfo + AbuseIPDB joint research at RSA Conference 2026, GreyNoise 2026 Invisible Army Report, Google Cloud Threat Intelligence IPIDEA Disruption (January 2026), the Basel AML Index 2025, the e-Governance Academy National Cyber Security Index, the ITU Global Cybersecurity Index v5, and others. The complete source list is in the full report.

This article summarizes findings from the Global Cybercrime Report 2026 (8,700 words, 5 charts, 24 primary sources). The full report covers 11 sections including phishing economics, cryptojacking trends, government action, NIS2 enforcement, and our complete methodology.

Global Cybercrime Report 2026: Key Findings

A beginner-friendly guide — no programming knowledge needed. Just follow the steps.

 as backup when the website blocks automated requests.

sudo apt update
sudo apt install python3.12 python3.12-venv


 Open a terminal (Command Prompt on Windows, Terminal on Mac/Linux) and type:

 If you install Brave via the terminal, note where it's installed. The default is usually 

. If it's somewhere else, you'll update one line of code later.

 is a fast tool that installs Python libraries. We use it instead of 

powershell -c "irm <https://astral.sh/uv/install.ps1> | iex"


curl -LsSf <https://astral.sh/uv/install.sh> | sh


Step 4: Create the Project Folder & Files

Open your terminal and run these commands 

# Create the main folder
mkdir realcommercial-scraper
cd realcommercial-scraper

# Create subfolders
mkdir browser
mkdir models


[project]
name = "realcommercial-scraper"
version = "0.1.0"
description = "Scraper for realcommercial.com.au"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "httpx",
    "pydantic",
    "psutil",
    "websocket-client",
]

[tool.ruff.lint]
fixable = ["ALL"]
select = ["I", "B", "E"]

[tool.pyright]
typeCheckingMode = "strict"
venvPath = "."
venv = ".venv"


from pathlib import Path

HOME = Path.home()
BASE_DIR = Path(__file__).parent

# Browser location — CHANGE THIS if your browser is elsewhere
DEBUG_BROWSER_PATH = "/usr/bin/brave-browser"

# Profile folder (cookies & settings are saved here)
USER_PROFILE_DIR = BASE_DIR / "browser" / "browser_profile"

# Browser window size
WIN_W = 720
WIN_H = 760


 Change the browser path to something like:

DEBUG_BROWSER_PATH = "C:\\\\Program Files\\\\BraveSoftware\\\\Brave-Browser\\\\Application\\\\brave.exe"

DEBUG_BROWSER_PATH = "/Applications/Brave Browser.app/Contents/MacOS/Brave Browser"

import httpx

from browser.browser import render_html
from listing_parse import parse_listing
from models.browser import Browser
from models.listing import PropertyDetailResponse
from models.request import SearchPayload
from models.search import SearchResponse

class RealCommercialClient:
    def __init__(self) -> None:
        self.client: httpx.Client = httpx.Client(
            headers={
                "Accept": "*/*",
                "Accept-Language": "en-US,en;q=0.6",
                "Content-Type": "application/json",
                "Origin": "<https://www.realcommercial.com.au>",
                "Referer": "<https://www.realcommercial.com.au/>",
                "User-Agent": (
                    "Mozilla/5.0 (X11; Linux x86_64) "
                    "AppleWebKit/537.36 (KHTML, like Gecko) "
                    "Chrome/146.0.0.0 Safari/537.36"
                ),
            },
            timeout=30.0,
        )

    def __enter__(self) -> "RealCommercialClient":
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.close()

    def search(self, request: SearchPayload) -> SearchResponse:
        response = self.client.post(
            "<https://api.realcommercial.com.au/listing-ui/searches>",
            json=request.model_dump(
                by_alias=True, exclude_none=True, exclude_unset=True
            ),
        )
        response.raise_for_status()
        return SearchResponse.model_validate(response.json())

    def update_headers(self) -> None:
        self.client.headers.update({
            "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8",
            "accept-encoding": "utf-8",
            "accept-language": "en-US,en;q=0.9",
            "cache-control": "no-cache",
            "dnt": "1",
            "pragma": "no-cache",
            "priority": "u=0, i",
            "referer": "<https://www.realcommercial.com.au/leased/property-678-high-street-thornbury-vic-3071-505055376>",
            "sec-ch-ua": '"Chromium";v="146", "Not-A.Brand";v="24", "Brave";v="146"',
            "sec-ch-ua-mobile": "?0",
            "sec-ch-ua-platform": '"Linux"',
            "sec-fetch-dest": "document",
            "sec-fetch-mode": "navigate",
            "sec-fetch-site": "same-origin",
            "sec-gpc": "1",
            "upgrade-insecure-requests": "1",
            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36",
        })

    def listing_details(
        self, pdp_url: str, browser: Browser, timeout: int = 5
    ) -> PropertyDetailResponse:
        self.update_headers()
        if pdp_url.startswith("/"):
            pdp_url = pdp_url[1:]
        full_url = f"<https://www.realcommercial.com.au/{pdp_url}>"
        response = self.client.get(full_url)
        if response.status_code == 429:
            html = render_html(browser, full_url, timeout)
            return parse_listing(html)
        response.raise_for_status()
        return parse_listing(response.text)

    def close(self) -> None:
        self.client.close()


import json

from models.listing import PropertyDetailResponse

def isolate_data_json(html_content: str) -> str:
    first_part = "  REA.pageData = "
    second_part = ";</script>"
    chopped = html_content.split(first_part)[-1]
    chopped = chopped.split(second_part)[0]
    return chopped

def parse_listing(html: str) -> PropertyDetailResponse:
    json_data = isolate_data_json(html)
    return PropertyDetailResponse(**json.loads(json_data))

if __name__ == "__main__":
    with open("index.html", "r") as f:
        html_content = f.read()
    result = parse_listing(html_content)
    print(f"Title: {result.listing.title}")
    print(f"Description: {result.listing.description[:200]}...")


from api import RealCommercialClient
from browser.browser import get_browser_instance
from models.listing import DemographicData
from models.request import SearchPayload as SearchRequest
from models.request import SimpleFilters as SearchFilters
from models.search import SearchListing

def print_separator(char: str = "=", length: int = 10) -> None:
    print(char * length)

def print_listing_summary(listing: SearchListing) -> None:
    print(f"{listing.title}")
    print(f"   Address: {listing.address.suburb_address}")
    area = getattr(listing.attributes, "area", "N/A")
    agent_name = listing.agents[0].name if listing.agents else "N/A"
    print(f"   Area: {area}")
    print(f"   Agent: {agent_name}")
    print(f"   URL: {listing.pdp_url}")

def print_demographics(demographic_data: DemographicData) -> None:
    print("  Demographics:")
    for insight in demographic_data.insights:
        print(f"    • {insight.label}: {insight.value}")

def main() -> None:
    search_config = SearchRequest(
        channel="leased",
        filters=SearchFilters(
            within_radius="includesurrounding",
            surrounding_suburbs=True,
        ),
        page=1,
        page_size=100,
    )
    browser = get_browser_instance(9999)

    with RealCommercialClient() as client:
        response = client.search(search_config)
        print(f"Available results: {response.available_results}")
        print(f"Returned listings: {len(response.listings)}")
        print_separator()

        for listing in response.listings:
            print_listing_summary(listing)
            print_separator("-")
            data = client.listing_details(listing.pdp_url, browser)
            description = data.listing.description
            print("Details:")
            print(f"{description[:300]}...")
            if data.demographic_data:
                print_demographics(data.demographic_data)
            break

if __name__ == "__main__":
    main()


These files make the browser and data models work

Create each file below in its correct folder.

from pydantic import BaseModel

class SimpleFilters(BaseModel):
    within_radius: str = "includesurrounding"
    surrounding_suburbs: bool = True

class SearchPayload(BaseModel):
    channel: str
    filters: SimpleFilters
    page: int = 1
    page_size: int = 100


from typing import Any

from pydantic import BaseModel, Field, HttpUrl

class BrandingLogo(BaseModel):
    alt: str
    url: HttpUrl
    url_template: HttpUrl = Field(alias="urlTemplate")

class Branding(BaseModel):
    color: str
    logo: BrandingLogo

class Address(BaseModel):
    street_address: str = Field(alias="streetAddress")
    suburb_address: str = Field(alias="suburbAddress")

class Phone(BaseModel):
    display: str
    dial: str

class Photo(BaseModel):
    alt: str
    url: HttpUrl
    url_template: HttpUrl | None = Field(None, alias="urlTemplate")

class Agent(BaseModel):
    id: str
    name: str
    image_path: HttpUrl | None = Field(None, alias="imagePath")
    image_url_template: HttpUrl | None = Field(None, alias="imageUrlTemplate")
    enquiry_uri: str | None = Field(None, alias="enquiryUri")
    phone: Phone | None = None

class Agency(BaseModel):
    id: str
    name: str
    additional_branding: bool = Field(alias="additionalBranding")
    branding: Branding
    phone: Phone | None = None
    salespeople: list[Agent] = []

class ListingAttributes(BaseModel):
    area: str | None = None

class ConjunctionalAgency(BaseModel):
    pass

class Omniture(BaseModel):
    pass

class ListingDetails(BaseModel):
    pass

class Advertising(BaseModel):
    pass

class SearchListing(BaseModel):
    highlights: list[str] = []
    agencies: list[Agency] = []
    days_active: int = Field(alias="daysActive")
    agents: list[Agent] = []
    attributes: ListingAttributes
    id: str
    has_tour: bool = Field(alias="hasTour")
    pdp_url: str = Field(alias="pdpUrl")
    title: str
    conjunctional_agencies: list[ConjunctionalAgency] = Field(
        default_factory=list, alias="conjunctionalAgencies"
    )
    product: str
    other_agencies: list[str] = Field(default_factory=list, alias="otherAgencies")
    omniture: Omniture = Field(default_factory=Omniture)
    status: str
    address: Address
    details: ListingDetails = Field(default_factory=ListingDetails)
    branding: Branding
    photos: list[Photo] = []

class SearchResponse(BaseModel):
    listings: list[SearchListing]
    surrounding_suburb_listings: list[Any] = Field(alias="surroundingSuburbListings")
    resolved_locations: list[Any] = Field(alias="resolvedLocations")
    available_results: int = Field(alias="availableResults")
    advertising: Advertising = Field(default_factory=Advertising)


from datetime import datetime
from typing import Any

from pydantic import BaseModel, Field, HttpUrl

class FullAddress(BaseModel):
    street_address: str = Field(alias="streetAddress")
    suburb_address: str = Field(alias="suburbAddress")
    state: str
    postcode: str
    suburb: str
    marketing_region: str | None = Field(None, alias="marketingRegion")
    marketing_suburb: str | None = Field(None, alias="marketingSuburb")

class PriceInfo(BaseModel):
    display: str
    is_price_hidden: bool = Field(alias="isPriceHidden")

class ListingPrice(BaseModel):
    leased: PriceInfo | None = None

class Photo(BaseModel):
    alt: str
    url: HttpUrl
    url_template: HttpUrl | None = Field(None, alias="urlTemplate")

class FloorPlan(BaseModel):
    alt: str
    url: HttpUrl
    url_template: HttpUrl | None = Field(None, alias="urlTemplate")

class Attribute(BaseModel):
    id: str
    label: str
    value: str

class MapData(BaseModel):
    zoom_level: int = Field(alias="zoomLevel")
    thumbnail: HttpUrl
    lat: float
    lng: float
    precision: str

class PropertyType(BaseModel):
    id: str
    url: str
    old_url: str = Field(alias="oldUrl")
    agency_url: str = Field(alias="agencyUrl")
    marketing: str
    display_text: str = Field(alias="displayText")
    long_display_text: str = Field(alias="longDisplayText")
    sentence_display_text: str = Field(alias="sentenceDisplayText")
    pdp_title: str = Field(alias="pdpTitle")
    icon_name: str | None = Field(None, alias="iconName")

class TenureType(BaseModel):
    key: str
    omniture: str
    display_text: str = Field(alias="displayText")

class AvailableChannel(BaseModel):
    id: str
    price: str
    omniture: str
    tealium: str
    campaign: str
    krux: str
    url: str
    ad_area: str = Field(alias="adArea")
    short_human_readable: str = Field(alias="shortHumanReadable")
    display_in_select: str = Field(alias="displayInSelect")
    human_readable: str = Field(alias="humanReadable")
    title_variant: str = Field(alias="titleVariant")
    title: str

class DescriptionMetadata(BaseModel):
    phone_numbers: list[str] = Field(default_factory=list, alias="phoneNumbers")

class SimilarListing(BaseModel):
    id: str
    title: str
    pdp_url: str = Field(alias="pdpUrl")
    address: Any
    area: str | None = None
    price: ListingPrice
    main_photo: Photo = Field(alias="mainPhoto")
    branding: Any
    product: str
    property_type_objects: list[PropertyType] = Field(alias="propertyTypeObjects")

class DemographicInsightItem(BaseModel):
    icon: str
    label: str
    value: str
    typename: str = Field(alias="__typename")

class DemographicData(BaseModel):
    summary: str
    insights: list[DemographicInsightItem]

class Listing(BaseModel):
    id: str
    title: str
    description: str
    canonical_path: str = Field(alias="canonicalPath")
    product: str
    status: str | None = None
    days_active: int = Field(alias="daysActive")
    last_updated_at: datetime = Field(alias="lastUpdatedAt")
    address: FullAddress
    price: ListingPrice
    photos: list[Photo] = []
    floor_plans: list[FloorPlan] = Field(alias="floorPlans")
    main_photo: Photo | None = None
    attributes: list[Attribute] = []
    highlights: list[str] = []
    map: MapData
    agencies: list[Any] = []
    branding: Any
    property_type_objects: list[PropertyType] = Field(alias="propertyTypeObjects")
    tenure_type_object: TenureType = Field(alias="tenureTypeObject")
    available_channel_objects: list[AvailableChannel] = Field(alias="availableChannelObjects")
    similar_listings: list[SimilarListing] = Field(alias="similarListings")
    description_metadata: DescriptionMetadata = Field(default_factory=DescriptionMetadata, alias="descriptionMetadata")
    high_quality_listing: bool = Field(alias="highQualityListing")
    multiple_properties: bool = Field(alias="multipleProperties")
    tours: list[Any] = []
    websites: list[Any] = []

class PropertyDetailResponse(BaseModel):
    listing: Listing
    demographic_data: DemographicData | None = Field(None, alias="demographicData")


from pydantic import BaseModel
from websocket import WebSocket

class DebuggerInfo(BaseModel):
    last_updates: int = -1

class Browser(BaseModel):
    ws: WebSocket = WebSocket()
    process_id: int = 0
    process: Any = None
    debugger_info: DebuggerInfo = DebuggerInfo()

    class Config:
        arbitrary_types_allowed = True

    def connect(self) -> None:
        if not self.ws.connected:
            self.ws.connect(self.ws.url)


import subprocess
import json
import urllib.request

from constants import DEBUG_BROWSER_PATH, USER_PROFILE_DIR, WIN_W, WIN_H

def spawn_debug_browser(debug_port: int, headless: bool = False):
    cmd = [
        DEBUG_BROWSER_PATH,
        f"--remote-debugging-port={debug_port}",
        f"--user-data-dir={USER_PROFILE_DIR}",
        f"--window-size={WIN_W},{WIN_H}",
        "--no-first-run",
        "--no-default-browser-check",
    ]
    if headless:
        cmd.append("--headless")
    process = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return process.pid, process

def attach_debugger(browser, debug_port: int):
    try:
        resp = urllib.request.urlopen(f"<http://localhost>:{debug_port}/json/version")
        data = json.loads(resp.read())
        ws_url = data.get("webSocketDebuggerUrl")
        if ws_url:
            browser.ws.url = ws_url
            browser.ws.connect(ws_url)
            browser.debugger_info.last_updates = 1
    except Exception:
        browser.debugger_info.last_updates = -1


import json
import uuid

def _send(browser, method: str, params: dict | None = None):
    msg = {"id": uuid.uuid4().int % 1000000, "method": method}
    if params:
        msg["params"] = params
    browser.ws.send(json.dumps(msg))
    return json.loads(browser.ws.recv())

def browser_init_domains(browser):
    _send(browser, "Page.enable")
    _send(browser, "DOM.enable")
    _send(browser, "Runtime.enable")
    _send(browser, "Network.enable")

def browser_open_url(browser, url: str):
    _send(browser, "Page.navigate", {"url": url})
    # Wait for page to load
    while True:
        resp = _send(browser, "Runtime.evaluate", {"expression": "document.readyState"})
        state = resp.get("result", {}).get("result", {}).get("value", "")
        if state == "complete":
            break

def dom_element_html(browser, selector: str, outer_html: bool = True):
    doc = _send(browser, "DOM.getDocument")
    root = doc["result"]["root"]["nodeId"]
    node = _send(browser, "DOM.querySelector", {"nodeId": root, "selector": selector})
    node_id = node["result"]["nodeId"]
    if outer_html:
        html = _send(browser, "DOM.getOuterHTML", {"nodeId": node_id})
    else:
        html = _send(browser, "DOM.getInnerHTML", {"nodeId": node_id})
    return html["result"]["outerHTML"] if outer_html else html["result"]["innerHTML"]


 followed by installation messages. If you see errors, double-check that you're in the right folder.

Available results: 2437
Returned listings: 100
==========
Shop & Retail Premises • 148m²
   Address: Thornbury, VIC 3071
   Area: 148m²
   Agent: John Smith
   URL: /leased/property-678-high-street-thornbury-vic-3071-505055376
----------
Details:
Prime retail space located on High Street, Thornbury...

  Demographics:
    • Total Population: 19,200
    • Median Age: 38
    • Average Household Income: $95,400/yr


How to Scrape RealCommercial.com.au

If you’re searching for an alternative to Oxylabs, you’re not alone.

Oxylabs is one of the most recognized names in the proxy space—but for many developers, startups, and data teams, it’s not always the best fit. Whether it’s pricing, flexibility, or complexity, users often start looking for solutions that better match their actual needs.

Oxylabs is powerful—but that doesn’t mean it’s practical for everyone.

Oxylabs is built for enterprise use, which often means:

For many users, this creates unnecessary overhead.

With advanced tools and infrastructure comes complexity:

If your goal is simply to collect data reliably, this can slow you down.

Some users find Oxylabs restrictive when it comes to:

What to Look for in an Oxylabs Alternative

Before choosing a provider, focus on what actually matters:

1. Proxyrack — Best for Flexibility & Real-World Use

Proxyrack is designed for users who want powerful proxies without enterprise friction.

2. Bright Data (formerly Luminati) — Enterprise Powerhouse

Bright Data is one of the largest proxy networks available.

Large enterprises with complex data needs

3. Proxycurl — API-Based Data Extraction (Not a Full Proxy Solution)

Proxycurl is often compared to proxy providers, but it serves a different role.

Instead of giving you raw proxy access, it provides 

, especially for platforms like LinkedIn.

“VIP Proxy Server Download” — Why You Should Be Careful

You might come across search results like 

Always use trusted proxy providers instead of downloading unknown tools.

Oxylabs vs Alternatives: Quick Comparison

Proxycurl vs Proxies: What’s the Difference?

The tradeoff is flexibility vs simplicity.

A provider that balances performance, cost, and ease of use

Oxylabs is a strong player—but it’s not the only option.

Today, the proxy landscape offers more flexibility than ever. You no longer need to commit to expensive, complex setups just to get reliable data.

Choosing the right provider comes down to one thing: 

Answer these, and the right choice becomes obvious.

If you want to take this further, you can:

You don’t need the biggest provider—you need the right one.

Oxylabs Alternatives in 2026

Better, Cheaper & More Flexible Proxy Providers

Oxylabs Alternatives in 2026: Better, Cheaper & More Flexible Proxy Providers

IP rotation is a fundamental concept in networking, cybersecurity, and data collection. It refers to the process of changing the IP address used for outgoing internet requests, either automatically or at defined intervals.

Whether you're accessing online services, managing automation workflows, or collecting data at scale, understanding how IP rotation works can significantly improve reliability, performance, and access.

IP rotation is the practice of assigning a different IP address to each request or session instead of using a single, static IP.

When a device or application sends repeated requests from the same IP address, it can be flagged as suspicious. IP rotation prevents this by distributing requests across multiple IP addresses, making traffic appear more natural.

Web servers and online platforms monitor incoming traffic to prevent abuse. When too many requests come from a single IP, systems may respond by:

IP rotation reduces these risks by spreading requests across multiple IPs, improving success rates and minimizing disruptions.

IP rotation is typically enabled through proxy networks.

This process can be configured in different ways depending on the use case.

The same IP is maintained for a session before rotating.

IP addresses rotate after a fixed time interval.

One of the most common applications of IP rotation is web scraping.

When extracting data from websites, sending too many requests from a single IP often leads to blocks or incomplete data. IP rotation helps distribute requests, making them appear as if they are coming from multiple users.

IP rotation is powered by proxy infrastructure.

Choosing the right proxy type depends on your goals, scale, and target platforms.

Both IPv4 and IPv6 can be used for IP rotation, but they differ in availability and compatibility.

In practice, many systems rely on IPv4 for reliability and IPv6 for scale where possible.

Without IP rotation, repeated requests from a single IP can quickly lead to:

This makes consistent data access difficult, especially at scale.

Some tools route traffic through a single proxy or a limited number of IPs.

IP rotation is a critical technique for maintaining reliable and scalable internet operations. By distributing requests across multiple IP addresses, it helps reduce detection, prevent blocks, and improve overall performance.

While it plays a key role in web scraping and automation, its benefits extend to many areas where consistent and uninterrupted access is required. Understanding and implementing IP rotation is essential for anyone working with modern data-driven systems.

What Is IP Rotation? How It Works and Why It Matters

Understanding the difference between IPv4 and IPv6 is essential for anyone working with networking, web scraping, or proxy infrastructure. As the internet continues to grow, the limitations of IPv4 and the advantages of IPv6 have become increasingly important.

, explore real-world use cases, and explain how concepts like 

 fit into modern data access and scraping workflows.

An IP (Internet Protocol) address is a unique identifier assigned to each device connected to a network. It allows devices to communicate with each other over the internet.

IPv4 is the most widely used IP format and has been the backbone of the internet since its early days.

IPv6 was introduced to solve the limitations of IPv4, especially the shortage of available IP addresses.

IP rotation is the process of automatically changing the IP address used for outgoing requests.

Instead of sending all requests from a single IP, rotation distributes them across multiple IPs, making traffic appear more natural.

If you send 1,000 requests from one IP → likely blocked

If you distribute across 100 IPs → much higher success rate

For businesses relying on data extraction, IP rotation is not optional — it’s essential.

When scraping websites, the choice between IPv4 and IPv6 can impact success rates.

Most large-scale scraping operations still rely heavily on 

Node Unblocker is a proxy-based solution built on Node.js that allows users to bypass restrictions and access blocked websites.

While Node Unblocker can help bypass basic restrictions, it has limitations:

For serious scraping or data collection, proxy infrastructure is significantly more reliable.

How Proxies Fit Into IPv4 and IPv6 Strategies

Modern proxy providers offer both IPv4 and IPv6 options, often combined with rotation.

Understanding the difference between IPv4 and IPv6 is more than a networking concept — it directly impacts how you build scalable, reliable systems for data access and web scraping.

While IPv6 represents the future of the internet, IPv4 remains the dominant standard for compatibility and real-world applications. Combining both, along with strategies like IP rotation and proxy usage, gives you the flexibility and performance needed to operate at scale.

IPv4 vs IPv6: What Is the Difference? (Complete Guide)

cURL is one of the most powerful and widely used tools for making HTTP requests. Whether you're testing APIs, scraping data, or debugging network issues, understanding how to use cURL effectively is essential.

In this guide, you’ll learn how to use cURL for GET and POST requests, handle authentication, follow redirects, and even convert cURL commands into Python.

cURL (Client URL) is a command-line tool used to transfer data between a client and a server using various protocols, most commonly HTTP and HTTPS.

GET requests are used to retrieve data from a server.

curl -X GET https://api.example.com/users


curl "https://api.example.com/users?limit=10&page=1"

POST requests are used to send data to a server.

curl -X POST https://api.example.com/users

To send JSON data, you need to include headers and a payload.

curl -X POST https://api.example.com/users \
-H "Content-Type: application/json" \
-d '{"name": "John", "email": "john@example.com"}'

curl -X POST https://api.example.com/login \
-d "username=user&password=pass"

curl -H "Authorization: Bearer YOUR_TOKEN" https://api.example.com/data

curl -u username:password https://api.example.com/protected

By default, cURL does not follow redirects.

curl -o myfile.zip https://example.com/file.zip

You can convert cURL commands into Python using the 

curl -X GET https://api.example.com/users

import requests

response = requests.get("https://api.example.com/users")
print(response.json())


cURL is frequently used in scraping workflows to:

However, many websites implement anti-bot protections that block repeated or automated requests.

To avoid this, developers often combine cURL with:

If you're working on scraping at scale, using a proxy network helps prevent rate limits and blocks while ensuring consistent data access.

While cURL is ideal for simple HTTP requests, more complex scraping tasks often require browser automation tools that can render JavaScript and simulate user behavior. If you're moving beyond basic requests, check out our comparison of 

 to understand which tool fits your needs.

You can route cURL requests through a proxy:

curl -x http://proxy-server:8080 https://example.com

IP rotation improves scraping success rates

For practical scraping use cases, such as extracting product data, pricing, or reviews, you can explore our 

, which walks through real-world implementation strategies.

cURL remains a fundamental tool for developers working with APIs and web scraping. Mastering GET and POST requests, authentication, redirects, and conversions to Python gives you full control over HTTP interactions.

As your projects scale, combining cURL with proxy infrastructure ensures reliability, performance, and access to data without interruptions.

Complete cURL Guide: GET, POST, Auth, Redirects (With Examples)

Scraping Amazon at scale is one of the most challenging tasks in data extraction today. With advanced anti-bot systems, aggressive rate limiting, and dynamic content delivery, even experienced developers struggle to maintain stable scraping operations.

Whether you're building an amazon scraper, an amazon review scraper, or a full amazon 

, success depends on one critical factor: staying undetected.

Megaproxy Demonstration with Scrapebox in 2019

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