The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are develope
The Android operating system ranks first in the market share due to the system’s smooth handling and many other features that it provides to Android users, which has attracted cyber criminals. Traditional Android malware detection methods, such as signature-based methods or methods monitoring battery...
题目:Deep Android Malware Detection 作者:Niall McLaughlin, Jesus Martinez del Rincon, BooJoong Kang 年份:2017 会议:CODASPY 2.解决的问题 之前的方法需要对程序进行分析然后提取具有识别能力的特征用于恶意软件的分类。在本文中应用卷积神经网络来对恶意软件进行分类,该方法是受到基于n-gram的恶意软件检测的启发,但...
Deep Android Malware Detection小结 题目:Deep Android Malware Detection 作者:Niall McLaughlin, Jesus Martinez del Rincon, BooJoong Kang 年份:2017 会议:CODASPY 2.解决的问题 之前的方法需要对程序进行分析然后提取具有识别能力的特征用于恶意软件的分类。在本文中应用卷积神经网络来对恶意软件进行分类,该方法是受到...
Deep Android Malware Detection This repository contains the code for the paper "Deep Android Malware Detection" (pdf download) | (citation) We use a convolutional neural network (CNN) for android malware classification. Malware classification is performed based on static analysis of the raw opcode ...
To the best of our knowledge, this is the first time that a convolutional neural network model is trained based on this type of features and used in the android malware detection domain. Furthermore, two classical 2D-convolutional layers-based neural network models have been proposed and two ...
The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learning...
Furthermore, our analysis also indicates that CNNs trained on image-based Android malware and benign data outperform various Android malware detection techniques proposed in the literature.Springer, ChamInternational Conference on Global Security, Safety, and SustainabilityUdeze, SomadinaEdge Hill University...
Among the mobile malware, Android malware occupies the largest proportion of mobile malware as Android has the largest share in the mobile application market. An attacker abuses Android’s open market policy to inflict damage such as personal information leaks or financial loss to users. Therefore,...
This work presents, DL-AMDet a deep learning-based architecture for off-device Android malware detection uses both static and dynamic applications analysis to extract the features from the Android applications. A hybrid deep learning model using CNN-BiLSTM is considered for static analysis detection....