Graph Neural Network-based Android Malware Classification with Jumping Knowledge

2022-01-19 · Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann

This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.

📄 PDF Abstract BibTeX arXiv:2201.07537

Code (0)

등록된 구현이 없습니다.

Tasks

Android Malware DetectionGraph Neural Network Malware Classification Malware Detection

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense

2025-12-23 · Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang arxiv

Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to …

Graph Neural Network Adversarial Defense Malware Detection

N-opcode Analysis for Android Malware Classification and Categorization

2016-07-27 · BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly…

Classification General Classification Malware Classification Malware Detection

Ontology-driven Knowledge Graph for Android Malware

2021-09-03 · Ryan Christian, Sharmishtha Dutta, Youngja Park, Nidhi Rastogi

We present MalONT2.0 -- an ontology for malware threat intelligence \cite{rastogi2020malont}. New classes (attack patterns, infrastructural resources to enable attacks, malware analysis to incorporate static analysis, an…

Articles Malware Analysis

Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification

2025-12-24 · Jakir Hossain, Gurvinder Singh, Lukasz Ziarek, Ahmet Erdem Sarıyüce arxiv

Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures. Their utility in traditional program analysis …

Malware Classification Malware Detection

A Dynamic Weighted Federated Learning for Android Malware Classification

2022-11-23 · Ayushi Chaudhuri, Arijit Nandi, Buddhadeb Pradhan

Android malware attacks are increasing daily at a tremendous volume, making Android users more vulnerable to cyber-attacks. Researchers have developed many machine learning (ML)/ deep learning (DL) techniques to detect a…

Classification Federated Learning Malware Classification