Welcome back, fellow hackers and pen testers! In this fourth installment of our advanced memory forensics series, we will explore malware detection and analysis. This article is intended for a technical intermediate understanding of security and hacking. We’ll give the audience plenty of real-world examples and demonstrations of tool execution to ensure this article is as practical and informative as possible.

As we all know, malware has become a pervasive threat, constantly evolving and adapting to evade traditional detection mechanisms. Memory forensics is an invaluable skill in the battle against these advanced threats, as it enables us to uncover and dissect the inner workings of malicious code. In this article, we will explore the following topics:

Advanced Memory Acquisition Techniques

We first need to acquire a memory dump before detecting and analyzing malware in memory. Advanced memory acquisition techniques can help us capture the most relevant and accurate data. Let’s discuss a few of them.

Hardware-based Memory Acquisition

Hardware-based memory acquisition involves using specialized hardware devices to read and capture the contents of a system’s memory. This technique is considered the most reliable method, as it does not rely on potentially compromised software. Examples of hardware-based memory acquisition devices include:

  • Xilinx ChipWhisperer: A device that connects to the target system’s memory bus and records memory data.
  • FireWire: A high-speed data transfer interface that can directly access a system’s memory.

Software-based Memory Acquisition

Software-based memory acquisition tools can capture memory contents in cases where hardware-based solutions are not feasible. Some popular software-based memory acquisition tools include:

  • Rekall: An open-source memory analysis framework that supports memory acquisition on Windows, Linux, and macOS systems.

  • WinPmem: A Windows-based memory acquisition tool part of the Rekall framework.

    Here’s a quick example of using Rekall to acquire a memory dump on a Windows system:

rekall -f "C:\memory.dmp" -p "C:\Program Files\Rekall\profile.json" acquire

This command will save the memory dump to the specified file, using the provided Rekall profile.

Malware Detection in Memory Dumps

Once we have obtained a memory dump, the next step is identifying signs of malware infection. There are several techniques for detecting malware in memory dumps, which we will discuss below.

Signature-based Detection

Signature-based detection involves scanning the memory dump for known patterns or signatures that are associated with specific malware families. This method can be effective in detecting known malware variants, but it may not be able to identify new or unknown threats. Tools such as Volatility and Rekall can be used for signature-based detection.

Here’s an example of using Volatility to search for the infamous ZeuS banking Trojan in a memory dump:

volatility -f memory.dmp --profile=Win7SP1x64 zeusscan

If ZeuS is detected, the output will display information about the infected process and the location of the malware in memory.

Anomaly-based Detection

Anomaly-based detection involves searching for abnormal behavior or patterns in the memory dump that may indicate the presence of malware. Some examples of anomalies include:

  • Unusual process connections or communications
  • Uncommon process injections or modifications
  • Suspicious API calls or system calls

The Yara tool is commonly used to perform anomaly-based detection. Yara allows you to create custom rules to search for specific patterns, strings, or byte sequences in the memory dump. For instance, here’s a Yara rule that searches for the presence of a known Cobalt Strike beacon:

rule CobaltStrike_Beacon {
    meta:
        description = "Cobalt Strike Beacon detection"
    strings:
        $beacon_magic = { 0x42 0x65 0x61 0x63 0x6f 0x6e 0x20 }
    condition:
        $beacon_magic
}

To use this rule with Volatility, save the rule in a file called “cobalt_strike.yar” and then run the following command:

volatility -f memory.dmp --profile=Win7SP1x64 yarascan -Y cobalt_strike.yar

If a Cobalt Strike beacon is detected, the output will show information about the infected process and the location of the beacon in memory.

In-depth Malware Analysis

Once malware has been detected in the memory dump, the next step is to perform a detailed analysis to understand its behavior, capabilities, and potential impact. We will discuss some key malware analysis techniques below.

Code Disassembly and Debugging

Disassembling the malware’s code and analyzing it using a debugger can provide valuable insights into its functionality and behavior. Tools such as IDA Pro, Ghidra, and OllyDbg are commonly used for this purpose.

For instance, you can use IDA Pro to disassemble the code of a detected malware sample, identify its functions, and analyze the flow of execution. You can also set breakpoints and step through the code to understand its behavior in real-time.

Dynamic Analysis

Dynamic analysis involves executing the malware in a controlled environment (such as a sandbox or a virtual machine) and observing its behavior. This can help identify its communication patterns, persistence mechanisms, and other runtime characteristics. Tools such as Cuckoo Sandbox, FireEye’s FLARE VM, and Joe Sandbox can be used for dynamic analysis.

For example, you can use Cuckoo Sandbox to automatically execute a malware sample and generate a detailed report of its behavior, including network traffic, file system activity, and system calls.

Reverse Engineering

Reverse engineering is the process of analyzing the malware’s code and data structures to understand its functionality and purpose. This can be a complex and time-consuming task, but it is essential for gaining a deep understanding of the malware’s capabilities and potential impact. Tools such as IDA Pro, Ghidra, and Radare2 can be used for reverse engineering.

Real-World Examples and Case Studies

Now that we have covered the various techniques for advanced memory forensics, let’s explore some real-world examples and case studies of malware detection and analysis using these techniques.

Stuxnet

Stuxnet is a notorious state-sponsored malware that targeted Iranian nuclear facilities in 2010. Memory forensics played a crucial role in uncovering the malware’s sophisticated techniques, such as its use of zero-day exploits and rootkit functionality.

Using Volatility, researchers were able to analyze the memory dump of an infected system and identify several key components of Stuxnet, such as its process injection mechanism and the infamous “resource 207” that contained the zero-day exploits.

WannaCry

WannaCry is a ransomware that caused widespread damage in 2017, encrypting the files of hundreds of thousands of systems worldwide. Memory forensics played a key role in understanding the malware’s encryption process and identifying potential weaknesses that could be exploited for decryption.

By analyzing a memory dump of an infected system, researchers were able to identify the ransomware’s encryption key generation process and discover that the keys were not securely wiped from memory after use. This finding enabled the development of decryption tools such as WannaKey and WanaKiwi, which could recover the encryption keys from memory and help some victims restore their files without paying the ransom.

NotPetya

NotPetya is a destructive malware that masqueraded as ransomware and caused significant disruptions in 2017. While it initially appeared to be a variant of the Petya ransomware, memory forensics helped reveal that it was, in fact, a wiper malware designed to cause irreparable damage.

By analyzing the memory dump of an infected system, researchers discovered that NotPetya overwrote the Master Boot Record (MBR) and encrypted the Master File Table (MFT), rendering the system unbootable and the files unrecoverable. This discovery helped security professionals understand the true nature of the attack and develop appropriate countermeasures.

Conclusion

Advanced memory forensics is a powerful tool in the fight against sophisticated malware threats. By acquiring accurate memory dumps, detecting malware using signature-based and anomaly-based techniques, and performing in-depth analysis through disassembly, debugging, dynamic analysis, and reverse engineering, we can uncover the inner workings of malicious code and develop effective countermeasures.

By studying real-world examples and case studies, such as Stuxnet, WannaCry, and NotPetya, we can learn valuable lessons about the evolving threat landscape and the importance of staying ahead of the curve. As professional hackers and pen testers, it is our responsibility to continuously hone our skills and stay informed about the latest developments in memory forensics, malware detection, and analysis.

In this article, we have provided a general overview of advanced memory forensics techniques for malware detection and analysis. Armed with this knowledge, you are now better equipped to tackle the ever-evolving challenges posed by advanced malware threats. Keep experimenting, keep learning, and most importantly, keep hacking!