1,643 research outputs found
Machine-Learning based analysis and classification of Android malware signatures
Multi-scanner Antivirus (AV) systems are often used for detecting Android malware since the same piece of software can be checked against multiple different AV engines. However, in many cases the same software application is flagged as malware by few AV engines, and often the signatures provided contradict each other, showing a clear lack of consensus between different AV engines. This work analyzes more than 80 thousand Android applications flagged as malware by at least one AV engine, with a total of almost 260 thousand malware signatures. In the analysis, we identify 41 different malware families, we study their relationships and the relationships between the AV engines involved in such detections, showing that most malware cases belong to either Adware abuse or really dangerous Harmful applications, but some others are unspecified (or Unknown). With the help of Machine Learning and Graph Community Algorithms, we can further combine the different AV detections to classify such Unknown apps into either Adware or Harmful risks, reaching F1-score above 0.84.The authors would like to acknowledge the support of the national project TEXEO (TEC2016-80339-R), funded by the Ministerio de Economia y Competitividad of SPAIN through, and the EU-funded H2020 SMOOTH project, Spain (grant no. H2020-786741). Similarly, the authors would like to remark the support provided by the Tacyt system (https://www.elevenpaths.com/es/te cnologia/tacyt/index.html) for the collection and labeling of AV information. Finally, Ignacio Martin would like to acknowledge the support granted by the Spanish Ministry of education through the FPU scholarship he holds (FPU15/03518)
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey
Malwares are big threat to digital world and evolving with high complexity.
It can penetrate networks, steal confidential information from computers, bring
down servers and can cripple infrastructures etc. To combat the threat/attacks
from the malwares, anti- malwares have been developed. The existing
anti-malwares are mostly based on the assumption that the malware structure
does not changes appreciably. But the recent advancement in second generation
malwares can create variants and hence posed a challenge to anti-malwares
developers. To combat the threat/attacks from the second generation malwares
with low false alarm we present our survey on malwares and its detection
techniques.Comment: 5 Page
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Machine learning based solutions have been successfully employed for
automatic detection of malware in Android applications. However, machine
learning models are known to lack robustness against inputs crafted by an
adversary. So far, the adversarial examples can only deceive Android malware
detectors that rely on syntactic features, and the perturbations can only be
implemented by simply modifying Android manifest. While recent Android malware
detectors rely more on semantic features from Dalvik bytecode rather than
manifest, existing attacking/defending methods are no longer effective. In this
paper, we introduce a new highly-effective attack that generates adversarial
examples of Android malware and evades being detected by the current models. To
this end, we propose a method of applying optimal perturbations onto Android
APK using a substitute model. Based on the transferability concept, the
perturbations that successfully deceive the substitute model are likely to
deceive the original models as well. We develop an automated tool to generate
the adversarial examples without human intervention to apply the attacks. In
contrast to existing works, the adversarial examples crafted by our method can
also deceive recent machine learning based detectors that rely on semantic
features such as control-flow-graph. The perturbations can also be implemented
directly onto APK's Dalvik bytecode rather than Android manifest to evade from
recent detectors. We evaluated the proposed manipulation methods for
adversarial examples by using the same datasets that Drebin and MaMadroid (5879
malware samples) used. Our results show that, the malware detection rates
decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just
a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure
Automatically combining static malware detection techniques
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone
- …