How do you analyze malware that is hard to understand?

Conducting a comprehensive static analysis of a malware sample is a crucial initial step in triage. Various tools are available to perform this analysis, allowing flexibility in tool selection based on personal preference. Noteworthy tools for this purpose include DIE (Detect It Easy), PEStudio, CFF Explorer, PEBear, and others.Through the utilization of these tools, analysts can gain insights into the fundamental file structure, providing a foundation for subsequent stages, such as dynamic analysis. Static analysis aids in uncovering potential indicators, including suspicious file formats, strings, api calls & functions, linked libraries and other noteworthy attributes that contribute to a more comprehensive threat assessment.

Malware analysis is a process of studying a malicious sample. During the study, a researcher's goal is to understand a malicious program's type, functions, code, and potential dangers. Stages of Malware Analysis: ? Static Properties Analysis ? Interactive Behaviour Analysis ? Fully Automated Analysis ? Manual Code Reversing There are several open-source tools that can help security analysts reverse engineer malware samples: .PeStudio, Process Hacker , Process Monitor (ProcMon), ProcDot, Autoruns, Fiddler, Wireshark, x64dbg, Ghidra, and Cuckoo Sandbox

Analyzing malware that is hard to understand often involves utilizing static analysis techniques. Static analysis examines the characteristics of malware without executing its code. This method includes inspecting file structures, examining code snippets, and identifying patterns and anomalies. By dissecting the malware in a controlled environment without activating its harmful functionalities, analysts can gain insights into its structure, potential behaviors, and evasion techniques.

1. Static sleuthing: Don't run it! Use file analyzers, disassemblers to inspect code snippets, metadata, strings for clues without execution. 2. Sandbox smarts: Isolate the malware in a controlled environment like Cuckoo Sandbox. Observe network activity, file changes, process creation for insights into its malicious behavior. 3. Community collaboration: Check online malware analysis sandboxes. They might already have analyzed it, providing valuable reports and indicators of compromise (IOCs). 4. Dynamic duo: Combine static analysis with dynamic (sandbox) to get a holistic view. Static analysis helps target specific areas in dynamic analysis for deeper understanding. 5. Patience & persistence: Complex malware takes time and expertise.

I analyze complex malware by breaking down its code into smaller components, examining patterns, and using specialized tools to identify malicious behaviors. I then leverage my expertise to interpret the findings, allowing for a comprehensive understanding of the intricate aspects of the malware.

Dynamic analysis in malware investigation is like watching a play unfold from the front row. Here, we take the malware for a test run in a controlled setting, using tools like virtual machines – our security stage. As the malware takes center stage, we closely observe its every move, monitoring how it interacts with the surroundings. It's akin to tracking its steps in a network, file system, or registry – understanding its impact on the digital landscape. Yet, there's a cat-and-mouse game at play. Some malware are clever actors; they can sense when they're being watched, like performers aware of the audience.

Dynamic analysis takes the analysis a step further by executing the malware in a controlled environment and closely monitoring its behavior. Virtual machines, sandboxes, and debuggers isolate the malware, enabling the observation of network, file system, and registry activities. This approach provides crucial insights into the malware's impact, communication, and persistence mechanisms. Nevertheless, dynamic analysis may encounter obstacles if the malware detects the analysis environment or deploys anti-debugging techniques to thwart inspection.

Dynamic analysis is running a malware sample in an isolated environment and observing its activities, interactions, and effects on the system. Process Monitor (Microsoft Sysinternals), Regshot, and Wireshark are some of the most popular tools for performing dynamic analysis.

Dynamic analysis is the next stage of malware examination. Here, we run the malware in a controlled setup and observe how it behaves. Tools like virtual machines, sandboxes, or debuggers are employed to contain the malware and watch its actions in areas like the network, file system, or registry. Dynamic analysis is beneficial for understanding the malware's effects, communication methods, or how it maintains a presence. Keep in mind, though, that dynamic analysis may fail if the malware recognizes the analysis environment or deploys anti-debugging methods to avoid scrutiny.

Understanding Dynamic Analysis: - It's like setting a trap for malware in a safe lab to watch its moves. - Use tools like virtual machines and sandboxes to keep it contained. - Observe how it behaves – what it does to networks, files, or systems. - Helps understand its impact and tricks, but smart malware might sense the trap and hide its true nature. - A crucial step for unravelling malware mysteries, yet always stay a step ahead of their anti-detection games.

Code analysis is essential in malware analysis for understanding functionality, detecting anti-analysis techniques, and identifying indicators of compromise (IoCs). It aids in reconstructing attack vectors, detecting vulnerabilities and exploits, and supports dynamic analysis. By revealing patterns and coding styles, code analysis contributes to attribution and intelligence gathering, enhancing cybersecurity defenses. Analysts use the insights gained from code analysis to create customized signatures, rules, and YARA rules for security systems. Beyond detection, code analysis is crucial for legal and forensic purposes, providing evidence for legal proceedings and assessing the impact of cyber incidents

Code analysis in malware investigation is like decoding a secret language – we delve into the malware's inner workings to unveil its logic and functions. Using tools like decompilers and disassemblers, we transform the cryptic binary code into a language we can understand, like deciphering a puzzle. It's a journey into the malware's mind, revealing its algorithms, encryption keys, and concealed commands. Yet, it's not always a straightforward narrative. Some malware are master storytellers, weaving complex tales through obfuscation and encryption techniques, making our code analysis akin to deciphering an intricate code.

Code analysis constitutes the third phase, involving the reverse engineering of the malware's code to comprehend its logic and functionality. Decompilers, disassemblers, and hex editors transform binary code into a readable form, such as assembly or high-level language. Code analysis reveals the malware's algorithms, encryption keys, and hidden commands. However, the complexity of obfuscation or encryption techniques employed by the malware can present challenges, rendering the code unreadable or incomprehensible.

Code analysis is the third phase of examining malware, involving the reverse engineering of its code to grasp its logic and functions. Tools like decompilers, disassemblers, or hex editors are utilized to transform the malware's binary code into a more understandable format, such as assembly or high-level language. Code analysis aids in recognizing the malware's algorithms, encryption keys, or concealed commands. However, it may pose challenges if the malware employs intricate obfuscation or encryption techniques to render the code unreadable or perplexing.

?? Code Analysis in Malware basics - Like decrypting a villain's secret code to understand its plot. - Tools like decompilers and disassemblers turn complex code into something we can study. - Reveals hidden tricks and strategies of the malware. - Tricky part? Malware often wears a disguise with encryption to avoid detection.

Memory analysis in malware investigation is like exploring the hidden corners of the mind – in this case, the malware's memory. Using tools like memory dumpers and forensic techniques, we capture a snapshot of the malware's thoughts, residing in its memory processes. It's akin to searching for clues in a maze of digital footprints. Memory analysis unveils the configuration secrets, data treasures, and even the malware's decryption routines, giving us a deeper understanding of its inner workings. We're essentially peering into the malware's thoughts and memories, revealing its digital secrets. Some malware are adept at covering their tracks, employing anti-forensic techniques to wipe away or alter their memory imprints.

Memory analysis constitutes the fourth phase of malware analysis, involving the scrutiny of the malware's memory footprint and artifacts. Tools like memory dumpers, memory forensics, or volatility frameworks are employed to capture and analyze the malware's memory image or processes. This approach aids in recovering the malware's configuration, data, or decryption routines. However, memory analysis may present challenges if the malware implements anti-forensics techniques to eliminate or alter its memory traces.

Advanced memory forensics can reveal more than just the immediate footprint of malware. By employing cross-view diffing techniques, analysts can compare memory images over time to identify subtle changes or anomalies that standard analysis might overlook. This is particularly useful in detecting sophisticated malware that operates at a low level within the system.

Memory analysis scrutinizes the malware's memory footprint and artifacts. Memory dumpers, forensic tools, and volatility frameworks capture and analyze the malware's memory image or processes. Memory analysis is instrumental in recovering the malware's configuration, data, or decryption routines. Nevertheless, it may face difficulties when the malware implements anti-forensics techniques to erase or modify its memory traces.

In addition, Memory analysis stands as a crucial element in the malware analysis process, providing a unique window into the malware's hidden workings. By combining memory capture, artifact analysis, and anti-forensic techniques, analysts can effectively extract valuable insights that could otherwise remain elusive. This information empowers defenders to neutralize malware threats and protect systems from harm.

Behavioral analysis in malware investigation is like observing a creature in its natural habitat – we study how the malware behaves in its digital surroundings. Using tools like network analyzers and simulators, we create scenarios to understand how the malware reacts and interacts. It's a bit like orchestrating a play, putting the malware on a stage and watching its performance. Behavioral analysis unveils the triggers that set it in motion, exposes its payloads, and sheds light on its evasion tactics. It's essentially decoding the malware's behavioral language, learning its responses to different situations.

In the fifth step of malware analysis, called behavioral analysis, we study how the malware behaves in its surroundings and with other components. We use tools like network analyzers or process monitors to imitate the malware's actions and see what happens. This helps us figure out the triggers, payloads, or evasion tactics of the malware. But sometimes, we may not get the full picture if the malware needs specific conditions to show its features.

Behavioral analysis, delves into the malware's interaction with its environment and other components. Network analyzers, process monitors, and malware simulators emulate execution scenarios to observe the malware's responses. This approach aids in uncovering triggers, payloads, and evasion tactics. However, behavioral analysis may be incomplete if the malware requires specific conditions or inputs to activate its features.

In my view, Behavioral analysis stands as a critical tool in the malware analysis arsenal, enabling analysts to gain insights that static and dynamic analysis alone cannot provide. By observing the malware's interactions and behavior, analysts can effectively identify its triggers, payloads, and evasion tactics, empowering them to neutralize these threats and protect their systems.

This is a broader approach that looks at how the malware behaves over time. It includes analyzing system changes, network traffic, and file modifications. Behavioral analysis can reveal the intent of the malware, such as whether it's designed for data exfiltration, system damage, or espionage.

Analyzing complex and obscure malware involves advanced intelligence techniques. Firstly, dynamic analysis in a controlled environment helps observe its behavior. Reverse engineering uncovers its code, employing tools like disassemblers. Behavioral analysis identifies patterns, communication channels, and potential targets. Threat intelligence feeds offer context on known malware campaigns. Collaboration with cybersecurity communities and leveraging machine learning aids in anomaly detection. Continuous monitoring and adapting strategies are crucial for keeping pace with evolving threats.

???♂? In intelligence analysis, the final step of malware analysis, it's like piecing together a cyber puzzle by connecting the malware's details with wider data sources. Using tools like threat intelligence platforms, for example Nucleon Cyber AGTI intelligence platform. we can match the malware's characteristics against known threats, helping to uncover its origins and objectives. But beware, as malware can use deceptive tactics like false flags, making this detective work both challenging and crucial for understanding the broader context of cyber threats.

Intelligence analysis, involves correlating the malware's information with other sources and data sets. Threat intelligence platforms, malware databases, and online scanners are employed to compare and enrich attributes, signatures, and relationships. Intelligence analysis is pivotal for determining the malware's origin, attribution, or campaign. Yet, it may face inaccuracies if the malware strategically employs false flags, spoofing, or polymorphism to mislead or confuse analysts.

The artifacts obtained from the malware analysis phase need to be compared using intelligence analysis. A good example of comparing attributes to identify the origin, malware variants, and threat actors behind the malware is to use threat intelligence platforms like VirusTotal.

????Think of intelligence analysis in malware analysis as piecing together a puzzle to understand not just what a piece of malware does, but who made it and why. It's like being a digital detective where you use various tools and databases to gather clues and connect the dots. However, malware can be deceptive, changing its appearance or hiding its tracks, which makes the detective work challenging. The goal is to not only catch the malware but to understand its origins and intentions, helping protect against future attacks. ?? !Two tips! 1. Diversify your sources: Use a mix of tools and databases to get a well-rounded view. 2. Stay updated: Keep abreast of the latest cyber threats and trends to spot new malware more easily.

To overcome the limitations posed by malware that can detect analysis environments, sophisticated virtualization and sandboxing techniques are essential. This includes the use of bare-metal analysis environments that are harder for malware to identify, thus allowing a more genuine observation of malware behavior. Moreover, implementing automated behavioral analysis systems can streamline the process, enabling the handling of larger volumes of malware with increased efficiency.

You don't start an analysis without a goal. Knowing if the malware specifically targetted the company ? Knowing generally how the malware attacks and to know how to fight it. Knowing what the malware attacks in order to know what has or could have been compromised. Determining a characteristic signature of the malware to detect it quicker in the future. etc

Get someone else to check it. Often deep-level technical analysis can lead to analyst bias. Once you bring in a second (or third) set of eyes, then you may find that your diagnosis wasn’t completely correct after all. Drop the ego, we’re human, we may be wrong sometimes.

?? In the ever-evolving world of malware analysis, it's crucial to remember that the techniques effective today might not work tomorrow, as malware continuously adapts. Collaboration plays a vital role; sharing insights and experiences across different organizations can unravel even the most complex malware schemes. While diving into this field, it's important to tread carefully, respecting privacy and legal boundaries. Despite the rise of automation in analyzing threats, the human element – with its intuition and expertise – remains indispensable. Additionally, studying past cybersecurity breaches is invaluable.

Presuming nothing, here's a couple of important points: [1] Don't do these things on your own personal laptop that you love so much. Use a dedicated or throw away platform that you don't care about. [2] Carefully consider the objectives. Are you trying to learn? Is this your job? Are you just curious? [3] What network access do you have? How are you protecting other assets while performing this work? [4] If doing this at work, is this activity within your role and do you have permission?