A Malicious Android Malware Detection System based on Implicit Relationship Mining

2021 ◽  
Author(s):  
Zijun Xu ◽  
Meng Li ◽  
Yiming Hei ◽  
Peiran Li ◽  
Jianwei Liu
Keyword(s):  
Detection System ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

An Android Malware Detection System Based on Feature Fusion

2018 ◽  
Vol 27 (6) ◽  
pp. 1206-1213 ◽  
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

scholarly journals Adversarial Samples on Android Malware Detection Systems for IoT Systems

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 974 ◽  
Author(s):  
Xiaolei Liu ◽  
Xiaojiang Du ◽  
Xiaosong Zhang ◽  
Qingxin Zhu ◽  
Hao Wang ◽  
...  
Keyword(s):  
Detection System ◽  
Fitness Function ◽  
Malware Detection ◽  
Security Analysis ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Testing Framework ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Iot Devices

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.

APK Auditor: Permission-based Android malware detection system

2015 ◽  
Vol 13 ◽  
pp. 1-14 ◽  
Author(s):  
Kabakus Abdullah Talha ◽  
Dogru Ibrahim Alper ◽  
Cetin Aydin
Keyword(s):  
Detection System ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals FNN and Auto Encoder Deep Learning-Based Algorithm for Android Cyber Security

2020 ◽  
Vol 8 (5) ◽  
pp. 3292-3296
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Detection System ◽  
Malware Detection ◽  
Cyber Attacks ◽  
Open Architecture ◽  
Android Malware ◽  
Android Malware Detection ◽  
Fully Connected

Android is susceptible to malware attacks due to its open architecture, large user base and access to its code. Mobile or android malware attacks are increasing from last year. These are common threats for every internet-accessible device. From Researchers Point of view 50% increase in cyber-attacks targeting Android Mobile phones since last year. Malware attackers increasingly turning their attention to attacking smartphones with credential-theft, surveillance, and malicious advertising. Security investigation in the android mobile system has relied on analysis for malware or threat detection using binary samples or system calls with behavior profile for malicious applications is generated and then analyzed. The resulting report is then used to detect android application malware or threats using manual features. To dispose of malicious applications in the mobile device, we propose an Android malware detection system using deep learning techniques which gives security for mobile or android. FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder algorithm from deep learning provide Extensive experiments on a real-world dataset that reaches to an accuracy of 95 %. These papers explain Deep learning FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder approach for android malware detection.

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

2019 ◽  
Vol 29 (02) ◽  
pp. 245-262 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document