Hence, conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT. In this paper, we proposed the nove
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...
DL-AMDet consists of two main detection models the first one uses CNN-BiLSTM deep learning method for detecting malware using static analysis. The other model utilizes deep Autoencoders as an anomaly detection model to identify the malware based on dynamic analysis. The performance of the DL-...
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 learnin...
Some deep learning approaches also suffer from low efficiency. This paper introduces a lightweight and interpretable Android malware detection system called “FEdroid.” By focusing on code segments that utilize sensitive APIs, the system simplifies the analysis process and extracts key information, ...
Android malware application detection using deep learning SU Zhida ,ZHU Yuefei,LIU Long Abstract: The traditional Android malware detection algorithms have low detection accuracy, which can not successfully identify the Android malware by using the technologies of repacking and code obfuscation. In order...
malware sample.md Update malware sample.md Nov 23, 2018 paper.md update paper.md Dec 30, 2019 third markets.md 添加论文和工具 Apr 15, 2018 tools.md 添加论文和工具 Apr 15, 2018 Repository files navigation README License DroidCC Android malware detection using deep learning, contains malware ...
We propose a novel malware detection and classification approach, named ACAMA, that uses APIs-based features. To develop this, we learn APIs extracted from our dataset using the CNN deep learning algorithm. (ii) We develop the predicted model of ACAMA and evaluate it. Also, to show the ...
End-edge coordinated inference for real-time BYOD malware detection using deep learning 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE (2020), pp. 1-6 CrossrefGoogle Scholar [25] P. Gronát, J.A. Aldana-Iuit, M. Bálek Maxnet: neural network architecture for continu...
A multimodal deep learning method for android malware detection using various features IEEE Trans Inf Forensics Secur, 14 (3) (2019), pp. 773-788, 10.1109/TIFS.2018.2866319 View in ScopusGoogle Scholar [17] Arp D., Spreitzenbarth M., Hübner M., Gascon H., Rieck K. DREBIN: Effective ...