Evaluating Convolutional Neural Network for Effective Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

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Deep Neural Network Based on Android Mobile Malware Detection System Using Opcode Sequences

2018 IEEE 18th International Conference on Communication Technology (ICCT) ◽

10.1109/icct.2018.8600052 ◽

2018 ◽

Author(s):

Lichao Zhao ◽

Dan Li ◽

Guangcong Zheng ◽

Wenbo Shi

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Detection System ◽

Malware Detection ◽

Mobile Malware ◽

Mobile Malware Detection

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Mobile malware detection using deep neural network

2017 25th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2017.7960568 ◽

2017 ◽

Cited By ~ 2

Author(s):

Irfan Bulut ◽

A. Gokhan Yavuz

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Malware Detection ◽

Mobile Malware ◽

Mobile Malware Detection

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Experiences of landing machine learning onto market-scale mobile malware detection

Proceedings of the Fifteenth European Conference on Computer Systems ◽

10.1145/3342195.3387530 ◽

2020 ◽

Author(s):

Liangyi Gong ◽

Zhenhua Li ◽

Feng Qian ◽

Zifan Zhang ◽

Qi Alfred Chen ◽

...

Keyword(s):

Machine Learning ◽

Malware Detection ◽

Mobile Malware ◽

Mobile Malware Detection

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A behavior-based mobile malware detection model in software-defined networking

2017 International Conference on Information Science and Communications Technologies (ICISCT) ◽

10.1109/icisct.2017.8188590 ◽

2017 ◽

Cited By ~ 1

Author(s):

Tri-Hai Nguyen ◽

Myungsik Yoo

Keyword(s):

Malware Detection ◽

Software Defined Networking ◽

Detection Model ◽

Mobile Malware ◽

Behavior Based ◽

Mobile Malware Detection

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A survey on dynamic mobile malware detection

Software Quality Journal ◽

10.1007/s11219-017-9368-4 ◽

2017 ◽

Vol 26 (3) ◽

pp. 891-919 ◽

Cited By ~ 26

Author(s):

Ping Yan ◽

Zheng Yan

Keyword(s):

Malware Detection ◽

Mobile Malware ◽

Mobile Malware Detection

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Dynamic detection of mobile malware using smartphone data and machine learning

Digital Threats: Research and Practice ◽

10.1145/3484246 ◽

2021 ◽

Author(s):

Sebastian Panman de Wit ◽

Doina Bucur ◽

Jeroen van der Ham

Keyword(s):

Machine Learning ◽

Malware Detection ◽

Real Life ◽

Detection Methods ◽

Nearest Neighbour ◽

Dynamic Features ◽

Machine Learning Classifiers ◽

Cpu Usage ◽

Mobile Malware ◽

Mobile Malware Detection

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. Detection methods for mobile malware exist but are still limited. In this paper, we propose dynamic malware-detection methods that use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System (OS). We use a real-life sensor dataset containing device and malware data from 47 users for a year (2016) to create multiple mobile malware detection methods. We examine which features, i.e. aspects, of a device, are most important to monitor to detect (subtypes of) Mobile Trojans. The focus of this paper is on dynamic hardware features. Using these dynamic features we apply the following machine learning classifiers: Random Forest, K-Nearest Neighbour, and AdaBoost.

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