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Quantifying Malware Evolution through
"... Dynamic analysis of malware allows us to examine malware samples, and then group those sam-ples into families based on observed behavior. Using Boolean variables to represent the presence or absence of a range of malware behavior, we create a bitstring that represents each malware behaviorally, and ..."
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Dynamic analysis of malware allows us to examine malware samples, and then group those sam-ples into families based on observed behavior. Using Boolean variables to represent the presence or absence of a range of malware behavior, we create a bitstring that represents each malware behaviorally, and then group samples into the same class if they exhibit the same behavior. Com-bining class definitions with malware discovery dates, we can construct a timeline of showing the emergence date of each class, in order to examine prevalence, complexity, and longevity of each class. We find that certain behavior classes are more prevalent than others, following a frequency power law. Some classes have had lower longevity, indicating that their attack profile is no longer manifested by new variants of malware, while others of greater longevity, continue to affect new computer systems. We verify for the first time commonly held intuitions on malware evolution, showing quantitatively from the archaeological record that over 80 % of the time, classes of higher malware complexity emerged later than classes of lower complexity. In addition to providing his-torical perspective on malware evolution, the methods described in this paper may aid malware
Efficient detection of zero-day Android Malware using Normalized Bernoulli Naive Bayes
"... Abstract—According to a recent F-Secure report, 97 % of mobile malware is designed for the Android platform which has a growing number of consumers. In order to protect consumers from downloading malicious applications, there should be an effective system of malware classification that can detect pr ..."
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Abstract—According to a recent F-Secure report, 97 % of mobile malware is designed for the Android platform which has a growing number of consumers. In order to protect consumers from downloading malicious applications, there should be an effective system of malware classification that can detect previously unseen viruses. In this paper, we present a scalable and highly accurate method for malware classification based on features extracted from Android application package (APK) files. We explored several techniques for tackling independence assumptions in Naive Bayes and proposed Normalized Bernoulli Naive Bayes classifier that resulted in an improved class sepa-ration and higher accuracy. We conducted a set of experiments on an up-to-date large dataset of APKs provided by F-Secure and achieved 0.1 % false positive rate with overall accuracy of
Detecting and Classifying Morphed Malwares: A Survey
"... In this era, most of the antivirus companies are facing immense difficulty in detecting morphed malwares as they conceal themselves from detection. Malwares use various techniques to camouflage themselves so as to increase their lifetime. These obscure methods cannot completely impede analysis, but ..."
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In this era, most of the antivirus companies are facing immense difficulty in detecting morphed malwares as they conceal themselves from detection. Malwares use various techniques to camouflage themselves so as to increase their lifetime. These obscure methods cannot completely impede analysis, but it prolongs the process of analysis and detection. This paper presents a review on malware detection systems and the progress made in detecting advanced malwares which will serve as a reference to researchers interested in working on advance malware detection systems.
