Advancing Obfuscation Strategies to Counter China’s Great Firewall: A Technical and Policy Perspective

By definition: ”A VPN is a communications environment in which access is controlled to permit peer connections only within a defined community of interest, and is constructed through some form of partitioning of a common underlying communications medium, where this underlying communications medium provides services to the network on a nonexclusive basis” [9]. In simple words, virtual Private Networks (VPNs) are tools that users widely use to protect personal data while enhancing online privacy for communication. This process includes two key parts, creating encrypted tunnels that hide user data from unauthorized access [10, 11, 12]:

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  Encryption: VPNs use robust encryption algorithms, such as AES and RSA, to protect data during transmission. This ensures confidentiality and prevents eavesdropping.

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  Tunneling Protocols: These protocols, such as OpenVPN and L2TP/IPSec, encapsulate data packets within encrypted tunnels, hiding their contents from network monitoring systems.

VPNs offer several benefits that are particularly relevant in censored environments like China:

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  Masking IP Addresses: VPNs route user traffic through servers in different locations, making it appear as if the user is accessing the internet from another region.

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  Bypassing Restrictions: Encrypted traffic can often evade simple blocking mechanisms, enabling access to censored content.

The initial strategies of the GFW to detect and block VPNs were relatively straightforward. Techniques included port-based filtering, where traffic on commonly used VPN ports was blocked, and IP address blacklisting, which targeted known VPN server addresses. To counteract these measures, early VPN providers implemented basic obfuscation strategies, such as:

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  Port Hopping: Running VPN traffic over ports commonly used by standard web traffic, such as port 443 for HTTPS.

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  SSL/TLS Encryption: Making VPN traffic appear indistinguishable from regular encrypted web traffic.

While these methods were effective in the short term, they proved insufficient against the GFW’s continuous advancements.

The DNS converts human-readable domain names into machine-readable IP addresses. By controlling DNS resolution, the GFW can efficiently restrict access to websites. The GFW uses two methods: DNS pollution and DNS hijacking.

DNS pollution involves injecting invalid DNS data into the resolver’s cache, which breaks the normal DNS resolution process and can result in receiving false or un-reachable IP addresses when trying to visit blocked domain names.

DNS hijacking redirects legitimate DNS queries to malicious or unintended servers. Unlike DNS pollution, which mostly impacts resolver caches, DNS hijacking can forever change the direction of DNS traffic.

IP block and IP range block block users from certain network resources by only allowing them to access certain IP addresses or IP ranges. The GFW leverages this method to successfully monitor and control internet traffic.

IP address blocking restricts or blocks access to individual IP addresses. IP range blocking involves blocking a number of consecutive IP addresses, usually by defining IP address ranges (e.g., subnets).

Keyword filtering and URL detection are some of the most important technical mechanisms by which the GFW implements internet censorship. The GFW is able to identify and block access to certain content by parsing keywords and URL paths within network requests.

Keyword filtering works by examining the network traffic for specified sensitive keywords. When these keywords are detected, the system will block or disrupt the related traffic.

URL filtering refers to the process of sifting through URLs in user requests to prevent access to blocked sites or pages.

Deep Packet Inspection (DPI) is a network packet filter that is able to examine the content of data packets. DPI enables the GFW to discover, classify, and monitor certain network traffic to allow internet content to be censored.

The core of DPI is network data packet analysis, usually in the form of packet capture and parsing, content analysis and recognition, and decision-making and processing.

Active probing and interference are technologically sophisticated techniques by which the GFW enacts internet censorship. Unlike passive monitoring, active probing and interference involves sending specific data packets or requests in order to find, and disrupt tools and services that bypass censorship mechanisms.

Active probing and interference accomplish this primarily by reading and determining target traffic, sending probe requests, and enabling interference.

Protocol obfuscation is the process of obscuring or masking the features of network communication protocols to avoid intercepting, recognizing or censoring traffic. It is primarily intended to oppose the GFW’s network censorship and traffic monitoring by presenting communication as normal, legitimate traffic to evade censorship and blocking.

This approach can change the structure, pattern, or contents of data packets — it deviates from standard protocol characteristics — and this makes it hard for detection systems to discern particular protocols. This solution effectively defeats protocol-based blocking and interference.

Additionally, protocol obfuscation can disguise traffic into generic protocols (HTTPS, HTTP) and make it look like ordinary network calls. The disguise technique enables obfuscated traffic to disguise itself among web traffic, greatly reducing the chances of being detected and stopped.

To increase concealment, protocol obfuscation uses heavy encryption and randomness to mask the true nature and structure of the traffic. This not only increases communication security but also significantly increases the resistance to DPI.

Traffic obfuscation is a technique that processes network traffic to make it difficult to detect using DPI. This involves using third-party obfuscation, which are part of VPN services, to alter the appearance of the traffic so that it resembles common network protocols like HTTPS or HTTP/2. In this way, VPN traffic blends better with internet traffic, reducing the risk of detection.

Through dynamic and random ports, VPN services randomly pick communication ports rather than common VPN ports. VPN traffic can also travel across multiple ports. This strategy of port diversification makes monitoring and blocking even harder.

By dividing data and routing it through different routes, multi-path VPN ensures that VPN traffic is routed over various network paths and is less likely to be detected by a single monitoring station. Split tunneling, on the other hand, means that you can only send part of the traffic through the VPN tunnel and then connect it directly to the internet. This reduces the overall characteristics of VPN traffic, and thus decreases the detection risk.

Anti-censorship policies are geared toward censorship dissent. These tools are highly customization and can be customized for multiple obfuscation modes, which makes their traffic difficult to detect and filter. For instance, obfuscation layers may lead to detection difficulties.

With third-party large-scale Content Delivery Networks (CDNs) for domains and infrastructure, domain fronting methods enable VPNs to disguise their actual server addresses so that traffic flows are seen as normal CDN traffic. Dynamic DNS involves frequent updates of server domain names and IP addresses, so it is not easy for static lists-based blocking techniques to prevail.

By using high-quality TLS encryption protocols, VPN traffic is not only content-encrypted, but conceals the connection’s presence and state behind the encryption layer, allowing DPI to see very little about what type of traffic is being sent. Using newer versions of TLS provides additional security and prevents man-in-the-middle attacks, as well as improving traffic masking.

VPN providers use frequent updates and protocol change strategies. They dynamically switch the protocols and ports used by monitoring applications so that monitoring systems do not develop fixed detection and blocking habits. Further, VPN providers leverage automated tools that continuously update obfuscation technology and protocols in response to the latest detection techniques of the censorship system.

The Great Firewall of China (GFW) represents a comprehensive system of internet censorship, combining advanced technical measures with legal and administrative controls. It has evolved significantly since its inception, posing continuous challenges to circumvention tools like Tor. Tor (The Onion Router)[15] is designed to facilitate anonymity and bypass censorship by routing user traffic through a distributed network of volunteer-operated nodes. The ongoing interaction between the GFW and Tor exemplifies the broader conflict between state-controlled internet censorship and tools that champion free access to information[16, 17].

Before 2011, the GFW relied primarily on basic techniques such as keyword filtering and IP blacklisting. However, the introduction of more advanced strategies marked a turning point in its effectiveness. The GFW employs multiple techniques, including Deep Packet Inspection (DPI), active probing, and rapid detection mechanisms, to identify and block Tor traffic. DPI allows the GFW to analyze both headers and payloads of data packets, identifying traffic patterns specific to Tor protocols. Active probing further enables the GFW to block Tor nodes by sending probes and analyzing responses. Since 2011, these advancements have allowed the GFW to detect and block private Tor bridges within minutes of deployment [13, 14].

Figure 2 illustrates how the GFW uses DPI boxes to initiate scanners that systematically probe suspected Tor servers. By sending specific connection attempts across multiple protocols (e.g., VPN, SSH), the GFW can determine the nature of the service and block access accordingly. This process highlights the systematic and multi-layered approach of the GFW in identifying and disrupting Tor operations.

In response to the GFW’s increasingly sophisticated detection mechanisms, the Tor Project and its community have continually refined their suite of countermeasures. As illustrated in Figure 1, a central strategy has long been the use of unpublished (bridge) relays—entry nodes to the Tor network not publicly listed in the main Tor consensus. The logic behind bridges is that, by remaining unknown to automated censorship systems, they are less susceptible to large-scale IP blocking. However, studies have shown that as early as 2011, the GFW adapted by pairing Deep Packet Inspection (DPI) with active scanning to detect these covert entry points, a tactic that drastically reduces the anonymity and usefulness of unpublished bridges over time.

When a user in China initiates a connection to what appears to be an unpublished Tor bridge, DPI modules within the GFW identify Tor-like signatures. Immediately following detection, active scanners—distributed across numerous IP addresses within China—attempt to confirm whether the suspected server is indeed running Tor. If the scanner receives a responsive handshake consistent with Tor, the GFW quickly blacklists the bridge’s IP address, often within minutes. Notably, recent measurements indicate that such IP-level blocks can persist for roughly 12 hours, after which the GFW periodically re-probes to determine if the server is still offering Tor connections. This approach significantly constrains the long-term viability of unpublished bridges, as even a single detection can temporarily render a bridge inaccessible to Chinese users.

To combat these evolving tactics, Tor developers have introduced pluggable transports—modular systems like Obfs4, ScrambleSuit, and Meek—that reshape or conceal Tor traffic patterns. Obfs4 randomizes traffic characteristics to evade simple DPI-based fingerprinting, while Meek employs domain fronting to route Tor traffic through reputable CDNs, forcing the GFW to risk collateral damage if it attempts to block it. Although these transports remain effective, their long-term sustainability has been challenged by the GFW’s pressure on CDNs and ongoing improvements in machine learning-based detection [20].

Moreover, recent findings suggest that server-side strategies can further frustrate active scanning attempts. For instance, a bridge operator can selectively drop packets from known scanner signatures—distinguishable by certain TCP packet options and MSS values—instead of responding in a manner consistent with Tor. By refusing to acknowledge scanner probes, the bridge avoids triggering long-duration IP blacklisting, effectively leveraging the GFW’s resource-sensitive scanning infrastructure against itself. Such techniques, combined with traditional pluggable transports, represent another layer of defense, enabling Tor bridges to remain operational under the GFW’s stringent surveillance regime.

In summary, the interplay between the GFW and Tor’s countermeasures has evolved from basic IP hiding to a complex arms race involving DPI, active probing, and adaptive transport obfuscation. While unpublished bridges, pluggable transports, and domain fronting have each offered periods of reprieve, the GFW’s active scanning capabilities and IP-based blocking strategies demand continual refinement of circumvention techniques. As research [18] indicates, practical mitigations—such as traffic dropping and improved distribution of unpublished addresses—are critical components of Tor’s evolving toolkit in resisting state-level censorship. [19]

The iterative nature of the technological competition between the GFW and Tor highlights the dynamic and high-stakes conflict between censorship tools and anti-censorship efforts. This section outlines key phases of this evolution.

The conflict between the GFW and Tor has significant global implications. Techniques pioneered by the GFW, such as DPI and active probing, have been adopted by other nations to implement their censorship systems. Conversely, innovations developed by Tor have advanced privacy tools, benefiting users worldwide. However, this conflict also raises ethical questions regarding the balance between internet freedom and the potential misuse of anonymity tools. Both the GFW’s surveillance practices and the misuse of Tor highlight the complexities of this technological arms race.

Detecting VPN traffic at the scale and sophistication of the Great Firewall (GFW) presents substantial technical and economic challenges. While DPI and machine learning-based traffic analysis have advanced considerably, applying these techniques across the GFW’s vast user base and diverse traffic patterns imposes enormous computational overhead. For instance, distinguishing obfuscated VPN traffic from regular encrypted HTTPS sessions requires continuous updates to detection heuristics. This complexity stems not only from the sheer volume of traffic but also from the need to accurately identify increasingly subtle fingerprinting signals.

Moreover, the GFW faces a delicate balance in its detection strategies. Aggressive filtering that mistakenly classifies legitimate encrypted communications as VPN traffic leads to false positives. Such overblocking can disrupt e-commerce transactions, banking services, and secure corporate communications, ultimately diminishing trust in domestic internet infrastructure. Thus, the GFW must manage the trade-off between refining its detection algorithms to minimize circumvention tools and maintaining a low false positive rate that preserves normal network functionality.

From the perspective of VPN providers, advancing obfuscation techniques to outpace GFW detection brings its own set of hurdles. While services such as NordVPN, Surfshark, and ExpressVPN have adopted protocol masking, domain fronting, and randomized traffic patterns, these strategies can degrade performance and reliability. For example, sophisticated obfuscation designed to evade the GFW’s DPI might induce higher latency or packet loss, impairing user experience within China and making the service less appealing for everyday activities that require stable, low-latency connections.

In addition, not all environments tolerate these obfuscation methods gracefully. Corporate firewalls or legacy systems may conflict with obfuscation layers designed for the Chinese censorship context, reducing compatibility in other scenarios. This fragmentation means VPN providers must continually refine their approaches, seeking the delicate equilibrium between robust anti-censorship capabilities and consistent, user-friendly performance[22, 23, 24].

These challenges underscore the need for strategic innovation in VPN design, where modular, easily updatable obfuscation layers can adapt quickly to the GFW’s evolving detection techniques.

The escalating arms race between VPN providers and the GFW’s network detection apparatus is not purely technical—it carries profound privacy and ethical implications. On one side, VPNs champion user autonomy, allowing individuals to bypass state-imposed restrictions and access information freely. In environments like China, where the GFW enforces stringent content controls, VPNs serve as a lifeline for journalists, researchers, and citizens seeking unbiased news and external perspectives. Yet, the very encryption and obfuscation that protect user anonymity and freedom also facilitate illicit activities. Bad actors might exploit these same tools for criminal conduct or to evade legitimate law enforcement, complicating the ethical assessment of VPN usage.

Conversely, the GFW’s increasingly intrusive detection measures raise concerns about overreach and disproportionate surveillance. Overly broad monitoring schemes risk infringing on users’ fundamental rights to privacy and freedom of expression, and may lead to collateral damage affecting ordinary citizens and businesses. Navigating these ethical dilemmas calls for nuanced policies and international dialogue, aiming to maintain security and stability without sacrificing the digital liberties that VPNs—despite their complications—ultimately strive to uphold.