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Malware analysis is the study or process of determining the functionality, origin and potential impact of a given malware sample such as a virus, worm, trojan horse, rootkit, or backdoor. [1] Malware or malicious software is any computer software intended to harm the host operating system or to steal sensitive data from users, organizations or ...
The deception is designed to lure the attacker in – the attacker may consider this a worthy asset and continue by injecting malware. Deception technology generally allows for automated static and dynamic analysis of this injected malware and provides these reports through automation to the security operations personnel.
Dynamic program analysis is the act of analyzing software that involves executing a program – as opposed to static program analysis, which does not execute it. Analysis can focus on different aspects of the software including but not limited to: behavior , test coverage , performance and security .
Static program analysis analyzes a program without actually executing it. This might lead to false positives where the tool reports problems with the program that do not actually exist. Fuzzing in combination with dynamic program analysis can be used to try to generate an input that actually witnesses the reported problem. [51]
Antivirus software typically uses two techniques to detect malware: (i) static analysis and (ii) dynamic/heuristic analysis. [60] Static analysis involves studying the software code of a potentially malicious program and producing a signature of that program. This information is then used to compare scanned files by an antivirus program.
Research in combining static and dynamic malware analysis techniques is also currently being conducted in an effort to minimize the shortcomings of both. Studies by researchers such as Islam et al. [13] are working to integrate static and dynamic techniques in order to better analyze and classify malware and malware variants.
A sandbox is implemented by executing the software in a restricted operating system environment, thus controlling the resources (e.g. file descriptors, memory, file system space, etc.) that a process may use. [4] Examples of sandbox implementations include the following: Linux application sandboxing, built on Seccomp, cgroups and Linux namespaces.
According to the company, SONAR 3 is fine-tuned to better detect fake antivirus software and is better integrated with the network component. They advise: "In SONAR 3 we have further enhanced our integration with the network component in order to classify, convict, and remediate malware on the basis of its malicious network activity.
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