4.6 Article

Why an Android App Is Classified as Malware: Toward Malware Classification Interpretation

Related references

Note: Only part of the references are listed.
Article Computer Science, Theory & Methods

A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

Ruitao Feng et al.

Summary: The current approach for Android malware detection relies on server-side scanning, yet a final defense line on mobile devices is still necessary. This paper introduces an effective real-time detection system on mobile devices, evaluating the impact of different parameters on detection performance.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2021)

Article Computer Science, Theory & Methods

A Survey of Methods for Explaining Black Box Models

Riccardo Guidotti et al.

ACM COMPUTING SURVEYS (2019)

Article Computer Science, Artificial Intelligence

Explanation in artificial intelligence: Insights from the social sciences

Tim Miller

ARTIFICIAL INTELLIGENCE (2019)

Article Computer Science, Theory & Methods

A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

TaeGuen Kim et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2019)

Proceedings Paper Computer Science, Software Engineering

MobiDroid: A Performance-Sensitive Malware Detection System on Mobile Platform

Ruitao Feng et al.

2019 24TH INTERNATIONAL CONFERENCE ON ENGINEERING OF COMPLEX COMPUTER SYSTEMS (ICECCS 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

How Can We Craft Large-Scale Android Malware? An Automated Poisoning Attack

Sen Chen et al.

2019 IEEE 1ST INTERNATIONAL WORKSHOP ON ARTIFICIAL INTELLIGENCE FOR MOBILE (AI4MOBILE '19) (2019)

Proceedings Paper Computer Science, Software Engineering

A Large-Scale Empirical Study on Industrial Fake Apps

Chongbin Tang et al.

2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP 2019) (2019)

Article Computer Science, Software Engineering

Lightweight, Obfuscation-Resilient Detection and Family Identification of Android Malware

Joshua Garcia et al.

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY (2018)

Article Computer Science, Information Systems

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

Sen Chen et al.

COMPUTERS & SECURITY (2018)

Article Engineering, Electrical & Electronic

Methods for interpreting and understanding deep neural networks

Gregoire Montavon et al.

DIGITAL SIGNAL PROCESSING (2018)

Article Automation & Control Systems

Significant Permission Identification for Machine-Learning-Based Android Malware Detection

Jin Li et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Proceedings Paper Computer Science, Theory & Methods

LEMNA: Explaining Deep Learning based Security Applications

Wenbo Guo et al.

PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18) (2018)

Proceedings Paper Computer Science, Software Engineering

Are Mobile Banking Apps Secure? What Can Be Improved?

Sen Chen et al.

ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2018)

Article Multidisciplinary Sciences

What is relevant in a text document?: An interpretable machine learning approach

Leila Arras et al.

PLOS ONE (2017)

Article Computer Science, Information Systems

Effective detection of android malware based on the usage of data flow APIs and machine learning

Songyang Wu et al.

INFORMATION AND SOFTWARE TECHNOLOGY (2016)

Proceedings Paper Computer Science, Software Engineering

Grounded Theory in Software Engineering Research: A Critical Review and Guidelines

Klaas-Jan Stol et al.

2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE) (2016)

Proceedings Paper Computer Science, Information Systems

POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble Pruning

Lingling Fan et al.

CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2016)

Proceedings Paper Computer Science, Information Systems

StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware

Sen Chen et al.

ASIA CCS'16: PROCEEDINGS OF THE 11TH ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2016)

Proceedings Paper Computer Science, Software Engineering

IccTA: Detecting Inter-Component Privacy Leaks in Android Apps

Li Li et al.

2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 1 (2015)

Article Computer Science, Software Engineering

INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data

Josua Krause et al.

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2014)

Proceedings Paper Computer Science, Information Systems

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)

Proceedings Paper Computer Science, Hardware & Architecture

A New Android Malware Detection Approach Using Bayesian Classification

Suleiman Y. Yerima et al.

2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA) (2013)