0% Complete
Home
/
12th International Conference on Computer and Knowledge Engineering
Android Malware Detection using Supervised Deep Graph Representation Learning
Authors :
Fatemeh Deldar
1
Mahdi Abadi
2
Mohammad Ebrahimifard
3
1- Tarbiat Modares University
2- Tarbiat Modares University
3- Tarbiat Modares University
Keywords :
Android application،Attributed function call graph،Autoencoder،Graph neural network،Graph representation learning،Malware detection
Abstract :
Despite the continuous evolution and significant improvement of cybersecurity mechanisms, malware threats remain one of the most important concerns in cyberspace. Meanwhile, Android malware plays a big role in these ever-growing threats. In recent years, deep learning has become the dominant machine learning technique for malware detection and continues to make outstanding achievements. Deep graph representation learning is the task of embedding graph-structured data into a low-dimensional space using deep learning models. Recently, autoencoders have proven to be an effective way for deep representation learning. However, it is not straightforward to apply the idea of autoencoder to graph-structured data because of their irregular structure. In this paper, we present DroidMalGNN, a novel deep learning technique that combines autoencoders with graph neural networks (GNNs) to detect Android malware in an end-to-end manner. DroidMalGNN represents each Android application with an attributed function call graph (AFCG) that allows it to model complex relationships between data. For more efficiency, DroidMalGNN performs graph representation learning in a supervised manner where two autoencoders are trained with benign and malicious AFCGs separately. In this way, it generates two informative embedding vectors for each AFCG in a low-dimensional space and feeds them into a dense neural network to classify the AFCG as benign or malicious. Our experimental results show that DroidMalGNN can achieve good detection performance in terms of different evaluation measures.
Papers List
List of archived papers
Overview of Electric Vehicles Charging Stations in Smart Grids
Mohammed Wadi - Wisam Elmasry - Mohammed Jouda - Hossein Shahinzadeh - Gevork B. Gharehpetian
A Review on Secure Data Storage and Data Sharing Technics in Blockchain-based IoT Healthcare Systems
Seyedeh Somayeh Fatemi Nasab - Davoud Bahrepour - Seyed Reza Kamel Tabbakh
FAST: FPGA Acceleration of Neural Networks Training
Alireza Borhani - Mohammad Hossein Goharinejad - Hamid Reza Zarandi
Sotfware defined content popularity estimation for wireless D2D caching networks
Maede Rezaei - AhmadReza Montazerolghaem
Hybrid navigation based on GPS data and SIFT-based place recognition using Biologically-inspired SLAM
Sahar Salimpour Kasebi - Hadi Seyedarabi - Javad Musevi Niya
GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram
Davod Karimpour - Mohammad Ali Zare Chahooki - Ali Hashemi
Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction
Rojan Roshankar - Mohammad Reza Keyvanpour
Improvement of Credit Scoring by LSTM Autoencoder Model
Milad Sattari Maleki - Seyedeh Niusha Motevallian - Faezehsadat Hosseini - Mohammad Sabokrou - Hamidreza Soltanalizadeh Maleki
Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention
Siavash Zaravashan - Sajjad Torabi - Hesam Zaravashan
Damage Detection After the Earthquake Using Sentinel-1 and 2 Images and Machine Learning Algorithms (Case Study: Sarpol-e Zahab Earthquake)
Niloofar Alizadeh - Behnam Asghari Beirami - Mehdi Mokhtarzade
more
Samin Hamayesh - Version 41.5.3