A recent report showed that a water utility [ 20 ] was attacked which might cause its industrial control application to be paused and even crashed. Even worse, the attack may be hardly stopped once the related web code is executed in the background. A recent report [ 4 ] showed attackers could even continue mining with the help of service worker after closing the infected web page. Therefore, a defense solution that can provide protection on all various devices and eliminate the threats of mining attacks is expected. Recently, a variety of detection solutions [ 10 , 12 , 13 , 17 , 32 ] have been proposed.
However, these approaches do not meet the requirements. First, they are not scalable. Most of them [ 13 , 17 , 32 ] require the modification of web browser engines to collect runtime features, such as the usage of CPU, memory, and network activities. The above solutions not only bring considerable additional overhead to the browser, but also make it difficult to deploy the defense. Second, in case users access infected websites, the mining code should be immediately stopped.
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However, prior approaches [ 10 , 12 ] does not meet the requirements. Third, the user experience should not be significantly influenced. However, prior tools may introduce high overhead. Furthermore, prior approaches may face high false positives and negatives. To identify mining code, they either use a blacklist to block the access of infected websites, or leverage heuristic features to detect mining code.
For the blacklist-based tools e. For the heuristic features, these features mainly include 1 the usage of CPU, memory, and network, 2 CPU cache events, and 3 cryptographic instructions.
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In our test, we find it is challenging for existing approaches to distinguish between benign CPU-intensive code and mining code. Second, different from prior approaches, which rely on the analysis on collected runtime features, MinerGate mainly focuses on understanding the semantics e. Through data-driven feature selection, MinerGate determines and extracts semantic-related features and forwards these features to a machine learning engine for determining the existence of mining code.
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Last, once mining code is found, MinerGate notifies the victim device i. For this purpose, we first address the challenge there is still not a reliable labeled dataset of cryptojacking mining payloads. We will open up this dataset for the follow-up research. This dataset should be helpful for a better understanding of mining attacks. Based on the dataset, we compare MinerGate and prior tools. We find MinerGate achieves better accuracy and coverage i.
MinerGate obtains high accuracy by extracting and applying semantic-based features with help of call graph CG and control flow graph CFG. We compare MinerGate and existing related approaches, and show MinerGate is scalable, effective and accurate. Cryptocurrencies are digital assets designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets [ 34 ]. The cryptocurrency uses a distributed database, blockchain, to store the transactions in units of blocks.
Each block mainly includes a unique ID, the ID of the preceding block, the timestamp, the nonce, the difficulty, and transaction records. A valid block contains a solution to a cryptographic puzzle involving the hash of the previous block, the hash of the transactions in the current block, and a cryptocurrency address which is to be credited with a reward for solving the cryptographic puzzle.
The specific cryptographic puzzle is to find a block of data whose hash value is smaller than a set value which is decided by the difficulty. Most data of the block are known, and the miner should find the unknown part in a limited time. Once the pronumeral, typically is the nonce, is found, the miner will submit it to get profit. This process is called cryptocurrency mining [ 7 ]. Cryptojacking, the unauthorized use of hardware of others to mine cryptocurrency, has become the biggest cyber threat in many parts of the world.
Using crypto-mining malware, criminals have mined earning up to 56 million USD in There are many reasons why cryptojacking is overgrowing. One of the most important reasons is the simplicity of deployment.
Cryptojacking definition
This allows the attackers to deploy mining payloads to victim websites without actual control because of XSS or other vulnerabilities. The simplicity of cryptojacking leads to the threat of cryptojacking attacks as long as the cryptocurrency exists. There is no correlation between the existence of such an attack and whether or not a service is alive.
In past years, web techniques made tremendous progress, which makes it feasible to launch mining attacks using web code. For example, the worker mechanisms provide the possibility of running web code in parallel and the background. In order to apply asm. WebAssembly [ 26 ] is an abstraction over modern hardware, making it language-, hardware-, and platform-independent, with use cases beyond just the Web.
WebAssembly is a binary instruction format bytecode for a stack-based virtual machine which is different from a text form of asm. Moreover, WebAssembly is committed to getting the speed closer to the native code, and it is faster than asm. Currently, WebAssembly can be only be loaded and executed by JavaScript, JavaScript calls WebAssembly in three steps: 1 loading WebAssembly bytecode, 2 compiling bytecode, and 3 instantiating and executing compiled code.
For example, they are both statically typed assembly-like languages, and they have similar instruction sets, which makes it possible for them to convert between each other. The earnings of cryptojacking attackers are strongly related to the mining speed, so the attackers implement the core logic of mining with WebAssembly and asm.
Diverse platforms and devices. Nowadays, many different devices, such as PC, mobile devices and infrastructure devices, are connected to the Internet. They all are potentially affected by mining attacks. Considering these devices usually have their own operating systems, it is challenging to offer general protection. To address it, we design and implement MinerGate as an extension of a network proxy e.
MinerGate can protect all devices behind it. In practice, once a mining attack occurs, MinerGate can enforcedly stop the attack code and alert network administrators. Obfuscated web code. Web code, especially the code injected by adversaries, is frequently obfuscated in practice.
To address the problem, MinerGate instruments the web code and hijack crucial global JavaScript APIs, which are helpful to extract the parameters related to mining code. However, due to the natural flexibility of JavaScript, adversaries may still bypass the above solution. To deal with this issue, we introduce a self-calling anonymous function to protect instrumented web code, and carefully handle the creation of new JavaScript contexts. Unknown mining semantics. As introduced in Sect. However, up to known, their inside semantics are still unclear, especially considering there are already many variants of the existing mining code.
This may significantly reduce the detection accuracy.
Although CG and CFG are basic things for program analysis, automatically generating CG and CFG is still not an easy task, especially considering indirect-call instructions are frequently used. Difficulty of mining code determination. It is difficult to distinguish between them accurately. In this work, we address this issue by applying machine learning. However, although existing work discovered a variety of features available for machine learning, they may cause high false positives.
Instead, we extract features from mining semantics e. However, it is challenging to apply graph-based features in machine learning, which cause performance issues and affect scalability.
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To handle it, we analyze the code in units of semantic modules instead of functions or files to break the solid lines in the analysis. Difficulty of stopping mining code. Once mining attacks occur, hardware resources e. This poses challenges to stopping the corresponding malicious code in time. To deal with this problem, we stop the execution of the mining thread through the function hijacking beforehand and cut off the source of malicious code. As shown in Fig. For each module, more details are presented in the following sections.
Please also keep in mind that the stub. In the next subsections, we explain how stub. Furthermore, we also present our protection, which prevents adversaries bypass or destroy stub.
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Let us use WebAssembly. Because JavaScript is a dynamic language, all objects can be replaced so that we can forge a WebAssembly. In this fake function, we first use a WebSocket connection to send the function parameter the WebAssembly payload asynchronously to the gateway and continue to execute the original code.
No matter how the mining code is saved and how the code is obfuscated, the mining code will be identified and sent to MinerGate. In addition, the payload is sent asynchronously, without blocking code execution and increasing overhead. For asm. Since attacker can dynamically invoke the asm. We need to hijack any API that will trigger code compilation. This principle offers hints to extract asm.
More specifically, we first do syntax analysis on the parameter of eval to build the AST. Next, we scan the AST to identify all functions. Finally, in addition to the asm.
Locating original APIs. Considering if adversaries can find and access that variable, adversaries may still normally and freely use the hijacked APIs.