1
SeqMobile: An Efficient Sequence-Based Malware Detection System Using RNN on Mobile Devices
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A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
3
Advanced evasion attacks and mitigations on practical ML‐based phishing website classifiers
4
GUI-Squatting Attack: Automated Generation of Android Phishing Apps
5
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
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MobiDroid: A Performance-Sensitive Malware Detection System on Mobile Platform
7
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers
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A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
9
A Large-Scale Empirical Study on Industrial Fake Apps
10
How Can We Craft Large-Scale Android Malware? An Automated Poisoning Attack
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Are mobile banking apps secure? what can be improved?
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LEMNA: Explaining Deep Learning based Security Applications
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Interpretable Basis Decomposition for Visual Explanation
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Efficiently Manifesting Asynchronous Programming Errors in Android Apps
15
An Empirical Assessment of Security Risks of Global Android Banking Apps
16
DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks
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Explaining Black-box Android Malware Detection
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A Survey of Methods for Explaining Black Box Models
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Significant Permission Identification for Machine-Learning-Based Android Malware Detection
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[Journal First] Lightweight, Obfuscation-Resilient Detection and Family Identification of Android Malware
21
What does Attention in Neural Machine Translation Pay Attention to?
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Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
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Methods for interpreting and understanding deep neural networks
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Explanation in Artificial Intelligence: Insights from the Social Sciences
25
Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach
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Attention is All you Need
27
A Unified Approach to Interpreting Model Predictions
28
Deep Android Malware Detection
29
Towards A Rigorous Science of Interpretable Machine Learning
30
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
31
"What is relevant in a text document?": An interpretable machine learning approach
32
ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications
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POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble Pruning
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Towards adversarial detection of mobile malware: poster
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Effective detection of android malware based on the usage of data flow APIs and machine learning
36
Hierarchical Attention Networks for Document Classification
37
The mythos of model interpretability
38
StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware
39
Grounded Theory in Software Engineering Research: A Critical Review and Guidelines
40
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
41
DroidDetector: Android Malware Characterization and Detection Using Deep Learning
42
Long Short-Term Memory-Networks for Machine Reading
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Effective Approaches to Attention-based Neural Machine Translation
44
Needles in a Haystack: Mining Information from Public Dynamic Analysis Sandboxes for Malware Intelligence
45
IccTA: Detecting Inter-Component Privacy Leaks in Android Apps
46
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data
48
Neural Machine Translation by Jointly Learning to Align and Translate
49
FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps
50
Machine Learning for Android Malware Detection Using Permission and API Calls
51
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
52
DroidChameleon: evaluating Android anti-malware against transformation attacks
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A New Android Malware Detection Approach Using Bayesian Classification
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Fast, scalable detection of "Piggybacked" mobile applications
55
DroidMat: Android Malware Detection through Manifest and API Calls Tracing
56
DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis
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Dissecting Android Malware: Characterization and Evolution
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BaobabView: Interactive construction and analysis of decision trees
59
Visual Explanation of Evidence with Additive Classifiers
60
Obfuscation of executable code to improve resistance to static disassembly
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Documentation for app developers
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Attention? Attention! lilianweng
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Global mobile OS market share in sales to end users from 1st quarter 2009 to 1st quarter 2016
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Information Flow Analysis of Android Applications in DroidSafe
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CopperDroid: Automatic Reconstruction of Android Malware Behaviors
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DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket
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AirBag: Boosting Smartphone Resistance to Malware Infection
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Towards Neural Network Based Malware Detection on Android Mobile Devices
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Androguard-reverse engineering, malware and goodware analysis of Android applications
70
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets
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Soundcomber: A Stealthy and Context-Aware Sound Trojan for Smartphones
72
Basics of qualitative research: Techniques and procedures for developing grounded theory, 3rd ed.