Abstract
No public abstract is available from the verified source records.
Silvia Lucia Sanna, Leonardo Regano, Davide Maiorca, Giorgio Giacinto
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
ror
Confidence 99%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Limits of static analysis for malware detection
2007
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2014
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Yes, machine learning can be more secure! a case study on android malware detection
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Andrubis – 1,000,000 apps later: a view on current android malware behaviors
openalex
Confidence 95%
datacite
Confidence 0%
2014
Android malware classification using optimum feature selection and ensemble machine learning
10.1016/j.iotcps.2023.03.001 · 2023
Light up that droid! on the effectiveness of static analysis features against app obfuscation for android malware detection
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2023
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10.1109/tetc.2019.2910086 · 2021
Robustness of image-based malware classification models trained with generative adversarial networks
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Adversarial examples for semantic segmentation and object detection
2017
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2019
Universal physical camouflage attacks on object detectors
2020
Adversarial EXEmples: a survey and experimental evaluation of practical attacks on machine learning for windows malware detection
10.1145/3473039 · 2021
Adversarial example attacks in the physical world
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From image to code: executable adversarial examples of android applications
10.1145/3404555.3404574 · 2020
A survey on practical adversarial examples for malware classifiers
2021
Adversarial malware binaries: evading deep learning for malware detection in executables
2018
Arms race in adversarial malware detection: a survey
10.1145/3484491 · 2021
Androzoo: collecting millions of android apps for the research community
10.1145/2901739.2903508 · 2016
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
A systematic literature review of android malware detection using static analysis
10.1109/access.2020.3002842 · 2020
Enhancing android malware detection explainability through function call graph APIs
2024
DroidScribe: classifying android malware based on runtime behavior
2016
Towards explainable CNNs for android malware detection
10.1016/j.procs.2021.03.118 · doi-reference
Applying NLP techniques to malware detection in a practical environment
10.1007/s10207-021-00553-8 · doi-reference
Android authorship attribution through string analysis
10.1145/3230833.3230849 · doi-reference
Generalizing from a few examples: a survey on few-shot learning
10.1145/3386252 · doi-reference
Deep learning
10.1038/nature14539 · doi-reference
The curvelet transform for image denoising
10.1109/tip.2002.1014998 · doi-reference
Visual pattern recognition by moment invariants
10.1109/tit.1962.1057692 · doi-reference
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
10.1109/tpami.2002.1017623 · doi-reference
Textural features for image classification
10.1109/tsmc.1973.4309314 · doi-reference
Color indexing
10.1007/bf00130487 · doi-reference
Index for rating diagnostic tests
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Malvis: large-scale bytecode visualization framework for explainable android malware detection
10.3390/jcp5040109 · doi-reference
A guide to deep learning in healthcare
10.1038/s41591-018-0316-z · doi-reference
A survey on deep learning in medical image analysis
10.1016/j.media.2017.07.005 · doi-reference
Vedrando: a novel way to reveal stealthy attack steps on android through memory forensics
10.3390/jcp3030019 · doi-reference
Responding to targeted stealthy attacks on android using timely-captured memory dumps
10.1109/access.2022.3160531 · doi-reference
Real-time triggering of android memory dumps for stealthy attack investigation
10.1007/978-3-030-70852-8_2 · doi-reference
Volmemdroid-investigating android malware insights with volatile memory artifacts
10.1016/j.eswa.2024.124347 · doi-reference
A comparison of graph neural networks for malware classification
10.1007/s11416-023-00493-y · doi-reference
SAMADroid: a novel 3-level hybrid malware detection model for android operating system
10.1109/access.2018.2792941 · doi-reference
Droiddetector: android malware characterization and detection using deep learning
10.1109/tst.2016.7399288 · doi-reference
CANDYMAN: classifying android malware families by modelling dynamic traces with Markov chains
10.1016/j.engappai.2018.06.006 · doi-reference
A systematic literature review of android malware detection using static analysis
10.1109/access.2020.3002842 · doi-reference
Androzoo: collecting millions of android apps for the research community
10.1145/2901739.2903508 · doi-reference
Arms race in adversarial malware detection: a survey
10.1145/3484491 · doi-reference
From image to code: executable adversarial examples of android applications
10.1145/3404555.3404574 · doi-reference
Adversarial example attacks in the physical world
10.1007/978-3-030-62460-6_51 · doi-reference
10.5220/0010603600002998
10.5220/0010603600002998 · doi-reference
Adversarial EXEmples: a survey and experimental evaluation of practical attacks on machine learning for windows malware detection
10.1145/3473039 · doi-reference
Dexray: a simple, yet effective deep learning approach to android malware detection based on image representation of bytecode
10.1007/978-3-030-87839-9_4 · doi-reference
From malware samples to fractal images: a new paradigm for classification
10.1016/j.matcom.2023.11.032 · doi-reference
Robustness of image-based malware classification models trained with generative adversarial networks
10.1145/3590777.3590792 · doi-reference
Windows malware detector using convolutional neural network based on visualization images
10.1109/tetc.2019.2910086 · doi-reference
Malware images: visualization and automatic classification
10.1145/2016904.2016908 · doi-reference
Would you mind hiding my malware? Building malicious android apps with stegopack
10.1016/j.pmcj.2025.102060 · doi-reference
Analysis and detection of android stegomalware: the impact of the loading stage
10.1145/3733102.3733122 · doi-reference
Unmasking the veiled: a comprehensive analysis of android evasive malware
10.1145/3634737.3637658 · doi-reference
Light up that droid! on the effectiveness of static analysis features against app obfuscation for android malware detection
10.1016/j.jnca.2024.104094 · doi-reference
Android malware classification using optimum feature selection and ensemble machine learning
10.1016/j.iotcps.2023.03.001 · doi-reference
The evolution of android malware and android analysis techniques
10.1145/3017427 · doi-reference