Adversarial Samples on Android Malware Detection Systems for IoT Systems

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 testing framework for learning-based Android malware detection 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.

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Permission-based Android Malware Detection System Using Feature Selection with Genetic Algorithm

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194019500116 ◽

2019 ◽

Vol 29 (02) ◽

pp. 245-262 ◽

Cited By ~ 6

Author(s):

Oktay Yildiz ◽

Ibrahim Alper Doğru

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Detection System ◽

Malware Detection ◽

Support Vector ◽

Android Malware ◽

Detection Systems ◽

Android Malware Detection ◽

Vector Machines ◽

Accuracy Result

As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing Android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.

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DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks

2018 IEEE European Symposium on Security and Privacy (EuroS&P) ◽

10.1109/eurosp.2018.00040 ◽

2018 ◽

Cited By ~ 19

Author(s):

Ke Xu ◽

Yingjiu Li ◽

Robert H. Deng ◽

Kai Chen

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Detection System ◽

Malware Detection ◽

Android Malware ◽

Android Malware Detection

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An Android Malware Detection System Based on Feature Fusion

Chinese Journal of Electronics ◽

10.1049/cje.2018.09.008 ◽

2018 ◽

Vol 27 (6) ◽

pp. 1206-1213 ◽

Cited By ~ 4

Author(s):

Jian Li ◽

Zheng Wang ◽

Tao Wang ◽

Jinghao Tang ◽

Yuguang Yang ◽

...

Keyword(s):

Feature Fusion ◽

Detection System ◽

Malware Detection ◽

Android Malware ◽

Android Malware Detection

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Permission Sensitivity-Based Malicious Application Detection for Android

Security and Communication Networks ◽

10.1155/2021/6689486 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yubo Song ◽

Yijin Geng ◽

Junbo Wang ◽

Shang Gao ◽

Wei Shi

Keyword(s):

Detection Method ◽

Malware Detection ◽

Feature Selection Method ◽

Machine Learning Algorithms ◽

Detection Methods ◽

Detection Accuracy ◽

Android Malware ◽

Android Malware Detection ◽

Private Data ◽

Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

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