Use the attached template (APA style) with essential to identify all citations from all sources material used

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The term malware stands for malicious software. It is a program installed on a system without the knowledge of the system’s owner. The third party installs it to steal some private data from the system. One of the significant and severe threats on the Internet today is malicious software, often referred to as malware. The malware being designed by attackers is polymorphic and metamorphic, altering its code as it propagates.
Moreover, the diversity of variants severely undermines the effectiveness of traditional defenses that typically use signature-based techniques and cannot detect the previously unknown malicious executable (Zero-day). The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify Zero-day attack malware into their known families using machine learning techniques. This research provides an overview of techniques for analyzing and classifying the Zero-day malware and how machine learning supports identifying and compacting the attack before it occurs.
This project is a simulation-based project which needs the writer to perform simulation of dataset via isolated environment by applying machine learning algorithms to identify the behavior of infected files.
please check the attached files for more details
there are several reference sources already attached which requires to use at least 25 sources from well-known journals like IEEE, ACM, Springer, and so on.
it’s important to identify the research methodology with a chart to clarify the workflow.
Use the attached template (APA style) with essential to identify all citations from all sources material used.
the paper must have tables and figures to make the topic very clear to the reader.
Make sure to check for plagiarism before submitting the report and must be less than 10%.

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