A Malware Detection System, MDS, comprising: a processor; a feature selector, coupled to the processor, to determine a first subset of features from amongst multiple features of a binary file to be utilized for detecting malware, wherein the binary file is indicative of a code and the first subset of features is selected based on applicability for detecting malware; a malware analyzer, coupled to the processor, to: analyze the binary file based on a machine learning model and the first subset of features; and determine one of a first category of malware and a second category of malware for the binary file based on detectability of malware within the binary file, wherein the first category and the second category of malware are determined based on a Shannon entropy of the binary file, the Shannon entropy being indicative of repetition of bit patterns within the binary file, and wherein the first category of malware has a lower detectability than the second category of malware; and a malware classifier, coupled to the processor, to: create a model to analyze the first category of malware; perform analysis of the first category of malware based on a second subset of features, the second subset of features being extracted from amongst the multiple features of the binary file by the feature selector and is different from the first subset of features; and generate a confidence score for determining malware, wherein malware is determined in response to the confidence score exceeding a threshold value. a processor; a feature selector, coupled to the processor, to determine a first subset of features from amongst multiple features of a binary file to be utilized for detecting malware, wherein the binary file is indicative of a code and the first subset of features is selected based on applicability for detecting malware; a malware analyzer, coupled to the processor, to: analyze the binary file based on a machine learning model and the first subset of features; and determine one of a first category of malware and a second category of malware for the binary file based on detectability of malware within the binary file, wherein the first category and the second category of malware are determined based on a Shannon entropy of the binary file, the Shannon entropy being indicative of repetition of bit patterns within the binary file, and wherein the first category of malware has a lower detectability than the second category of malware; and analyze the binary file based on a machine learning model and the first subset of features; and determine one of a first category of malware and a second category of malware for the binary file based on detectability of malware within the binary file, wherein the first category and the second category of malware are determined based on a Shannon entropy of the binary file, the Shannon entropy being indicative of repetition of bit patterns within the binary file, and wherein the first category of malware has a lower detectability than the second category of malware; and a malware classifier, coupled to the processor, to: create a model to analyze the first category of malware; perform analysis of the first category of malware based on a second subset of features, the second subset of features being extracted from amongst the multiple features of the binary file by the feature selector and is different from the first subset of features; and generate a confidence score for determining malware, wherein malware is determined in response to the confidence score exceeding a threshold value. create a model to analyze the first category of malware; perform analysis of the first category of malware based on a second subset of features, the second subset of features being extracted from amongst the multiple features of the binary file by the feature selector and is different from the first subset of features; and generate a confidence score for determining malware, wherein malware is determined in response to the confidence score exceeding a threshold value.