홈 › Papers

Android Malware Detection Using Machine Learning on Image Patterns

2019-01-27 · 2018 Cyber Resilience Conference (CRC) 2019 1 · Fauzi Mohd Darus, Noor Azurati Ahmad Salleh, Aswami Fadillah Mohd Ariffin

Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect them. Traditional malware detection techniques are no longer reliable to detect newly created malware in short period of time. In this paper, we use a different approach to detect Android malware. The Android malware will be visualised into gray scale images and their image features will be extracted using GIST descriptor. The detection will be done and compare using three different classifiers namely k-nearest neighbor (KNN), Random Forest (RF), and Decision Tree (DT)

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Android Malware DetectionMalware Detection

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

2024-02-05 · Jiahao Liu, Jun Zeng, Fabio Pierazzi, Lorenzo Cavallaro 외

Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability …

Android Malware Detection Feature Engineering Malware Detection

Android Malware Characterization using Metadata and Machine Learning Techniques

2017-12-12 · Ignacio Martín, José Alberto Hernández, Alfonso Muñoz, Antonio Guzmán

Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses in…

BIG-bench Machine Learning

Adversarial Patterns: Building Robust Android Malware Classifiers

2022-03-04 · Dipkamal Bhusal, Nidhi Rastogi

Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or …

Autonomous Vehicles BIG-bench Machine Learning Malware Detection

Android Malware Category and Family Detection and Identification using Machine Learning

2021-07-05 · Ahmed Hashem El Fiky, Ayman El Shenawy, Mohamed Ashraf Madkour

Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applicati…

BIG-bench Machine Learning Malware Family Detection

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

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

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-proc…

Android Malware Detection Graph Neural Network Malware Classification Malware Detection