HamDroid: permission-based harmful android anti-malware detection using neural networks

2022 ◽  
Author(s):  
Saeed Seraj ◽  
Siavash Khodambashi ◽  
Michalis Pavlidis ◽  
Nikolaos Polatidis
Keyword(s):  
Neural Networks ◽  
Malware Detection

Machine Learning-Based Malware Detection using Recurrent Neural Networks

2019 ◽  
Author(s):  
Miguel Oyler-Castrillo ◽  
Nicolas Bohm Agostini ◽  
Gadiel Sznaier ◽  
David Kaeli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
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.

scholarly journals Behavioral Malware Detection Using Deep Graph Convolutional Neural Networks

2019 ◽  
Author(s):  
Angelo Schranko de Oliveira ◽  
Renato José Sassi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Malware Detection ◽  
Detection Methods ◽  
New Public ◽  
Classification Tasks ◽  
Long Short Term Memory ◽  
Similar Area ◽  
Source Of Information

<div>Malware behavioral graphs provide a rich source of information that can be leveraged for detection and classification tasks. In this paper, we propose a novel behavioral malware detection method based on Deep Graph Convolutional Neural Networks (DGCNNs) to learn directly from API call sequences and their associated behavioral graphs. In order to train and evaluate the models, we created a new public domain dataset of more than 40,000 API call sequences resulting from the execution of malware and goodware instances in a sandboxed environment. Experimental results show that our models achieve similar Area Under the ROC Curve (AUC-ROC) and F1-Score to Long-Short Term Memory (LSTM) networks, widely used as the base architecture for behavioral malware detection methods, thus indicating that the models can effectively learn to distinguish between malicious and benign temporal patterns through convolution operations on graphs. To the best of our knowledge, this is the first paper that investigates the applicability of DGCNN to behavioral malware detection using API call sequences.</div>

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