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Deep Learning-Based Attack Detection and Classification in Android Devices
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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MtNet: A Multi-Task Neural Network for Dynamic Malware Classification
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Deep Ground Truth Analysis of Current Android Malware
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Sample Selection Bias Correction Theory
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Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder
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Hunting for metamorphic engines
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Android malware detection: state of the art
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Survey of machine learning techniques for malware analysis
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DL-Droid: Deep learning based android malware detection using real devices
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Mining permission patterns for contrasting clean and malicious android applications
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Cyber security challenges for IoT-based smart grid networks
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Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey
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A Review of Android Malware Detection Approaches Based on Machine Learning
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A study of android malware detection technology evolution
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Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification
inferredcites →https://doi.org/10.1109/ccst.2019.8888430openalex-snapshot
Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls
inferredcites →https://doi.org/10.1109/comst.2018.2847722openalex-snapshot
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
inferredcites →https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00094openalex-snapshot
Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning
inferredcites →https://doi.org/10.1109/dicta.2016.7797091openalex-snapshot
Understanding Data Augmentation for Classification: When to Warp?
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Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense
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Evaluating Explanation Methods for Deep Learning in Security
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Large-scale malware classification using random projections and neural networks
inferredcites →https://doi.org/10.1109/iccs45141.2019.9065765openalex-snapshot
Static Analysis of Android Malware Detection using Deep Learning
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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DLGraph: Malware Detection Using Deep Learning and Graph Embedding
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A survey on heuristic malware detection techniques
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A Comprehensive Survey on Transfer Learning
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Deep neural network based malware detection using two dimensional binary program features
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Variant: a malware similarity testing framework
inferredcites →https://doi.org/10.1109/secpri.2001.924286openalex-snapshot
Data mining methods for detection of new malicious executables
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Exploring Adversarial Examples in Malware Detection
inferredcites →https://doi.org/10.1109/tcyb.2017.2777960openalex-snapshot
DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection
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A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples
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TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder
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Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids
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DDoSNet: A Deep-Learning Model for Detecting Network Attacks
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Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
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Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
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Adversary Resistant Deep Neural Networks with an Application to Malware Detection
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A Survey of Android Malware Detection with Deep Neural Models
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Accurate mobile malware detection and classification in the cloud
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
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Sanity Checks for Saliency Metrics
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Static Analysis of Executables to Detect Malicious Patterns
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Model Proposal for Malware Detection Using Deep Learning on Cell Phones with Android Operating System
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PRAU-GIN: GIN-based android malware classification with traffic refinement and node augmentation
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From code to security: machine learning approaches in android vulnerability detection
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