751 research outputs found
Longitudinal performance analysis of machine learning based Android malware detectors
This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples
Unsupervised Anomaly-based Malware Detection using Hardware Features
Recent works have shown promise in using microarchitectural execution
patterns to detect malware programs. These detectors belong to a class of
detectors known as signature-based detectors as they catch malware by comparing
a program's execution pattern (signature) to execution patterns of known
malware programs. In this work, we propose a new class of detectors -
anomaly-based hardware malware detectors - that do not require signatures for
malware detection, and thus can catch a wider range of malware including
potentially novel ones. We use unsupervised machine learning to build profiles
of normal program execution based on data from performance counters, and use
these profiles to detect significant deviations in program behavior that occur
as a result of malware exploitation. We show that real-world exploitation of
popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can
be detected with nearly perfect certainty. We also examine the limits and
challenges in implementing this approach in face of a sophisticated adversary
attempting to evade anomaly-based detection. The proposed detector is
complementary to previously proposed signature-based detectors and can be used
together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4,
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