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How do you measure the performance and accuracy of dynamic analysis frameworks?

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Dynamic analysis frameworks are tools that execute malware samples in a controlled environment and monitor their behavior, such as network traffic, file operations, registry changes, and system calls. They are widely used by malware analysts to quickly identify the functionality and purpose of malicious code, as well as to extract indicators of compromise (IOCs) and signatures. However, not all dynamic analysis frameworks are equally reliable and efficient. How do you measure the performance and accuracy of dynamic analysis frameworks?

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1 Challenges of dynamic analysis

One of the main challenges of dynamic analysis is that malware samples may detect and evade the analysis environment, such as by checking for the presence of virtual machines, debuggers, or sandbox artifacts. This can result in incomplete or misleading analysis results, or even trigger malicious actions that harm the host system or the network. Therefore, it is important to evaluate how well a dynamic analysis framework can handle evasion techniques and provide accurate and comprehensive behavior reports.

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Dr. Paul de Souza

Founder President at Cyber Security Forum Initiative (CSFI.US) National Security Professional | Advisor | University Professor
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One of the hurdles I find challenging to deal with is encrypted or obfuscated code. Malware authors often use encryption or obfuscation techniques to hide the true nature of their code, making it more difficult to analyze. While dynamic analysis can provide insights into the malware's behavior, it may not be able to reveal or understand the underlying code structure and logic in its entirety.

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Joseph Edwards
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In addition to sandbox and virtual machine detection, malware can often be encrypted with a key which may be specific to the targeted endpoint or domain (this can frequently be the case with implants) and not included in the malware itself. However, most dynamic analysis solutions don't declare the sample a "dud" or attempt to determine the key.

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2 Performance metrics

When performing dynamic analysis, it can be time-consuming and resource-intensive, especially with large-scale or complex malware samples. To evaluate the efficiency of a dynamic analysis framework, it’s important to measure its performance metrics such as throughput (number of malware samples analyzed per unit of time), latency (time required to start, execute, and finish the analysis of a malware sample), overhead (amount of CPU, memory, disk, and network resources consumed by the analysis framework), reliability (percentage of malware samples that can be successfully analyzed without errors or crashes), and scalability (ability of the analysis framework to handle increasing volumes or complexity of malware samples).

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Joseph Edwards
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Many organizations use cloud-based sandbox solutions which reduce the overhead associated with starting and re-starting virtual machines. However, this still does not scale as well as emulation or smaller virtual machines without a full operating system. The downsides of not having a full operating system include the inability to execute malware that depends on particular software, and malware that exploits features of the operating system to snoop or persist (rootkits, bootkits).

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3 Accuracy metrics

The ultimate goal of dynamic analysis is to provide accurate and useful information about the behavior and impact of malware samples. To evaluate the correctness and completeness of a dynamic analysis framework, several accuracy metrics can be used. These include coverage, which measures the percentage of malware behavior that can be observed and recorded; precision, which is the ratio of true positives to false positives; recall, which is the ratio of true positives to false negatives; F1-score, which is the harmonic mean of precision and recall; and quality, which evaluates the level of detail, clarity, and consistency in behavior reports.

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Joseph Edwards
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On the malware analysis side of cyber security, we don't always discuss the same research topics or have the same priorities as offensive researchers. But reverse engineers of both fields have probably heard of the terms "symbolic execution" and perhaps even "code coverage." While malware analysts don't tend to fuzz malware for code coverage, perhaps we should learn to integrate this into our processes. It would be a great way to measure the efficacy of sandboxes against emulators and other frameworks, and determine which malware samples are protected with a key.

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4 Benchmarking methods

Malware analysts can use various benchmarking methods to measure the performance and accuracy of dynamic analysis frameworks. This includes comparing the results of dynamic analysis frameworks with those of static analysis tools or manual analysis methods, ground truth datasets with known and verified behavior characteristics, and feedback mechanisms or validation methods such as honeypots, network sensors, or reverse engineering tools. This can provide a baseline or reference for expected malware behavior as well as additional or complementary information about the malware behavior.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you measure the performance and accuracy of dynamic analysis frameworks?

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1 Challenges of dynamic analysis

2 Performance metrics

3 Accuracy metrics

4 Benchmarking methods

5 Here’s what else to consider

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