Investigating Dynamic Malware Analysis Tool - Case Study Example

It has been identified that the sandbox tools are effective in terms of updating the malware signature databases and understanding the behavioral patterns of such codes. Introduction Malware is identified as a form of malicious software that can cause tremendous level of damage to a standalone platform or to an entire organizational setup if ignored. One of the major issues being faced by the online security companies is regarding identification of the continuous evolutionary pattern of the malware programs.

The functionality of the malware programs gradually depends on their types, which further does have multiple counts. Popular examples of malware programs includes ‘Ransomware Trojans’, ‘Keyloggers’, ‘Rootkit virus’ and multiple more. Within the drastic evolution in the areas of software fields and data execution patterns, multiple unauthorized individuals have started using these kinds of malicious contents for attaining illegal access to those systems from where they can gather crucial data for sufficing their needs (Malware Help.

Org, 2014). Taking into consideration of the loss caused by such malicious programs, multiple static and dynamic malware detection tools have also been developed that often prove effective in mitigating the risk associated with malware attacks on crucial data storage and execution systems. Static and dynamic malware detection analysis are few such tools that are provided by the software vendors, which are capable of identifying existing malware codes within a system and blocking or terminating them at the same instance (Egele et al.,2010). The entire concept of dynamic malware analysis mainly depends on the signature identification technique.

When a new malware detection tool comes under the supervision of an analyst, he / she continuously updates the database of that tool with all the possible malware signatures, which the system is previously subjected. With the help of an updated database, the malware tool is able to identify all the possible malware attacks, so that threats related to malware attacks are mitigated (Egele et al., 2010).