scholarly journals A Mobile Malware Detection Method Based on Malicious Subgraphs Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yao Du ◽  
Mengtian Cui ◽  
Xiaochun Cheng
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Malware Detection ◽  
Structural Features ◽  
Personal Privacy ◽  
Rapid Improvement ◽  
Android Malware ◽  
Mobile Malware ◽  
Heavy Burden ◽  
Mobile Malware Detection

As mobile phone is widely used in social network communication, it attracts numerous malicious attacks, which seriously threaten users’ personal privacy and data security. To improve the resilience to attack technologies, structural information analysis has been widely applied in mobile malware detection. However, the rapid improvement of mobile applications has brought an impressive growth of their internal structure in scale and attack technologies. It makes the timely analysis of structural information and malicious feature generation a heavy burden. In this paper, we propose a new Android malware identification approach based on malicious subgraph mining to improve the detection performance of large-scale graph structure analysis. Firstly, function call graphs (FCGs), sensitive permissions, and application programming interfaces (APIs) are generated from the decompiled files of malware. Secondly, two kinds of malicious subgraphs are generated from malware’s decompiled files and put into the feature set. At last, test applications’ safety can be automatically identified and classified into malware families by matching their FCGs with malicious structural features. To evaluate our approach, a dataset of 11,520 malware and benign applications is established. Experimental results indicate that our approach has better performance than three previous works and Androguard.

scholarly journals A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 185
Author(s):  
Vasileios Kouliaridis ◽  
Georgios Kambourakis
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Mobile Malware ◽  
Comprehensive Survey ◽  
Mobile Malware Detection

Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

A survey on dynamic mobile malware detection

2017 ◽  
Vol 26 (3) ◽  
pp. 891-919 ◽  
Author(s):  
Ping Yan ◽  
Zheng Yan
Keyword(s):  
Malware Detection ◽  
Mobile Malware ◽  
Mobile Malware Detection

scholarly journals Dynamic detection of mobile malware using smartphone data and machine learning

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.

Towards Deep Learning-Based Approach for Detecting Android Malware

2021 ◽  
pp. 2193-2219
Author(s):  
Jarrett Booz ◽  
Josh McGiff ◽  
William G. Hatcher ◽  
Wei Yu ◽  
James Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Environment ◽  
Malware Detection ◽  
Extensive Study ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Mobile Malware Detection ◽  
Optimal Settings

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.

Intelligent mobile malware detection using permission requests and API calls

2020 ◽  
Vol 107 ◽  
pp. 509-521 ◽  
Author(s):  
Moutaz Alazab ◽  
Mamoun Alazab ◽  
Andrii Shalaginov ◽  
Abdelwadood Mesleh ◽  
Albara Awajan
Keyword(s):  
Malware Detection ◽  
Mobile Malware ◽  
Mobile Malware Detection

Structural Investigation of a Surfactant-Templated Silica Aerogel by Small-angle Scattering

1998 ◽  
Vol 520 ◽  
Author(s):  
Thomas P. Riekerlt ◽  
Mark T. Anderson ◽  
Patricia S. Sawyer ◽  
Shrish Rane ◽  
Gregory Beaucage
Keyword(s):  
Light Scattering ◽  
Small Angle ◽  
Silica Aerogel ◽  
Large Scale ◽  
Structural Investigation ◽  
Structural Information ◽  
Structural Features ◽  
Length Scales ◽  
X Ray ◽  
Angle Scattering

ABSTRACTThe structure of a surfactant-templated silica aerogel is studied by small-angle x-ray and light scattering. By combining the two techniques, we obtain structural information on length scales from Ångstroms to 0.1 millimeters. For this sample, we find five structural features, including the morphology of large scale aggregates.

Sign in / Sign up

Export Citation Format

Share Document