The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.īy its definition, malware, also known as “malicious software”, is a software created by an attacker to compromise the security of a system or privacy of a victim. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process.
Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. These countermeasures are mainly based on dynamic and statistical analysis. reloc Entropy 4.Data-driven public security networking and computer systems are always under threat from malicious codes known as malware therefore, a large amount of research and development is taking place to find effective countermeasures.
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