Adversarial Samples on Android Malware Detection Systems for IoT Systems

Abstract

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a \textbf{t}esting framework for \textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection systems(TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android Application with a success rate of nearly 100\% and can perform black-box testing on the system.

References in corpus (4)

• Intriguing properties of neural networks
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• Poisoning Attacks against Support Vector Machines
• Generative Poisoning Attack Method Against Neural Networks

Cited by in corpus (8)

• Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
• EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
• Weighted-Sampling Audio Adversarial Example Attack
• MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
• A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
• "How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
• PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
• When the Guard failed the Droid: A case study of Android malware