Related references
Note: Only part of the references are listed.A Survey of Android Malware Detection with Deep Neural Models
Junyang Qiu et al.
ACM COMPUTING SURVEYS (2021)
GDroid: Android malware detection and classification with graph convolutional network
Han Gao et al.
COMPUTERS & SECURITY (2021)
Android Malware Detection via Graph Representation Learning
Pengbin Feng et al.
MOBILE INFORMATION SYSTEMS (2021)
SDG: A Simplified and Dynamic Graph Neural Network
Dongqi Fu et al.
SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2021)
Opcode sequence analysis of Android malware by a convolutional neural network
Dan Li et al.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2020)
Learning to detect Android malware via opcode sequences
Abdurrahman Pektas et al.
NEUROCOMPUTING (2020)
Assessing and Improving Malware Detection Sustainability through App Evolution Studies
Haipeng Cai
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY (2020)
DL-Droid: Deep learning based android malware detection using real devices
Mohammed K. Alzaylaee et al.
COMPUTERS & SECURITY (2020)
A study of run-time behavioral evolution of benign versus malicious apps in android
Haipeng Cai et al.
INFORMATION AND SOFTWARE TECHNOLOGY (2020)
Android Malware Detection Based on a Hybrid Deep Learning Model
Tianliang Lu et al.
SECURITY AND COMMUNICATION NETWORKS (2020)
On the Deterioration of Learning-Based Malware Detectors for Android
Xiaoqin Fu et al.
2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2019) (2019)
DroidEvolver: Self-Evolving Android Malware Detection System
Ke Xu et al.
2019 4TH IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY (EUROS&P) (2019)
Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network
Jiaqi Yan et al.
2019 49TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2019) (2019)
A Transparent and Multimodal Malware Detection Method for Android Apps
Dali Zhu et al.
MSWIM'19: PROCEEDINGS OF THE 22ND INTERNATIONAL ACM CONFERENCE ON MODELING, ANALYSIS AND SIMULATION OF WIRELESS AND MOBILE SYSTEMS (2019)
Poster: Towards Sustainable Android Malware Detection
Haipeng Cai et al.
PROCEEDINGS 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING - COMPANION (ICSE-COMPANION (2018)
HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network
Shifu Hou et al.
KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2017)
DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocks
Shifu Hou et al.
WEB-AGE INFORMATION MANAGEMENT (2016)
AndroZoo: Collecting Millions of Android Apps for the Research Community
Kevin Allix et al.
13TH WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR 2016) (2016)
Composite Constant Propagation: Application to Android Inter-Component Communication Analysis
Damien Octeau et al.
2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 1 (2015)
Mining Apps for Abnormal Usage of Sensitive Data
Vitalii Avdiienko et al.
2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 1 (2015)
Information-Flow Analysis of Android Applications in DroidSafe
Michael Gordon et al.
22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015) (2015)
EdgeMiner: Automatically Detecting Implicit Control Flow Transitions through the Android Framework
Yinzhi Cao et al.
22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015) (2015)
FlowDroid: Precise Context, Flow, Field, Object-sensitive and Lifecycle-aware Taint Analysis for Android Apps
Steven Arzt et al.
ACM SIGPLAN NOTICES (2014)
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Daniel Arp et al.
21ST ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2014) (2014)
Share Paper
