Running the code is not necessary for simple static analysis. Instead, the static analysis looks for indications of harmful intent in the file. Identifying malicious infrastructure, libraries, or packaged files may be valuable.
Technical indications such as file names, hashes, strings including IP addresses and domain names, and file header data may be employed to detect whether a file is malicious. To learn more about how the virus works, monitoring it without executing it using tools like network analyzers and disassemblers is possible.
In a secure sandbox environment, suspected dangerous code is executed during dynamic malware analysis. Security experts may see the virus in operation thanks to this closed system without worrying about it getting on their computers or leaking into the company network. Deeper visibility made possible by dynamic analysis gives threat researchers and incident responders the ability to identify a threat’s genuine nature. Automated sandboxing also saves time by avoiding the need to reverse engineer a file to find dangerous code.
Complex malicious code may sometimes evade detection by sandbox technology, and simple static analysis is not a reliable method of doing so. The hybrid analysis combines static and dynamic analysis techniques and gives security teams the best of both worlds. This is because it can find malicious code trying to hide and then extract many indicators of compromise (IOCs) by statically analyzing previously unknown code. Even the most complex malware threats may be found through hybrid analysis.
Adversaries are using more advanced methods to elude existing detection systems. Threats may be identified more successfully using comprehensive behavioral analysis and detecting standard code, malicious functionality, or infrastructure. Extraction of IOCs is another result of malware investigation. To help teams be alerted to relevant risks in the future, the IOCs may subsequently be fed into SEIMs, threat intelligence platforms (TIPs), and security orchestration tools.
Strings encoded in malicious code, header information, hashes, metadata, embedded resources, etc., are examples of static attributes. There is no requirement to execute the application to see this kind of data, making it possible that it is all that is required to generate IOCs. A further study utilizing more thorough methods may be required, and the next course of action may be determined based on the knowledge gained during the static analysis.
A malware sample operating in a lab is observed and interacted with using behavioral analysis. Analysts aim to comprehend the operations of the sample’s registry, file system, processes, and networks. They could also do memory forensics to understand how the virus consumes memory. The analysts may build a simulation to verify their hypothesis if they believe the virus has a particular capability. A creative analyst with exceptional abilities is needed for behavioral analysis. Without automated technologies, lengthy and complex procedures cannot be completed successfully.
The automatic analysis evaluates suspicious files fast and efficiently. The research may identify possible consequences if the virus were to penetrate the network and then provide a report that is simple to read and offers quick solutions for security professionals. The most efficient approach to analyzing malware at scale is fully automated analysis.
During this phase, analysts use debuggers, disassemblers, compilers, and other specialized tools to reverse-engineer code to decrypt encrypted data, ascertain the reasoning behind the malware algorithm, and comprehend any hidden capabilities that the virus has not yet shown. Code reversals need a lot of time to complete and require unique talent. Due to these factors, malware investigations often skip this phase and omit important information on the virus’s makeup.
Lead Instructor qualified in CISSP, CCIE, and MCT with 25 years of training experience in Toronto.
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