Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity 2023, IEEE Access A Deep Learning Based Android Malware Detection System with Static Analysis 2022, HORA 2022 - 4th
using machine learning techniques on network traffic to specifically detect android malware. The CICMalAnal2017 dataset is one of the only datasets containing real, up-to-date network traffic from malicious and benign android applications. The goal of this project is to employ deep learning ...
🤖Android malware detection using deep learning, contains android malware samples, papers, tools etc.🐛 - maoqyhz/DroidCC
As far as we know, this is the first approach to ever try to combine hand-crafted features defined by experts with features automatically learned through deep learning for the task of malware detection and classification. The main idea behind is to fuse both the deep and hand-crafted features...
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, ...
Deep learning techniques are also applied to Trojan detection19,28 in conventional cyber attacks. To the best of our knowledge exploiting the Buffer overflow vulnerability in DNA sequencing pipeline using a specially designed DNA was first demonstrated in15. To detect DNA sequences containing the ...
Object Detection方法汇总 object class+background)。 4. 位置精修:boudningbox regression。 其他方法: 端到端(end-to-end)的物体检测,例如YOLO(you only... objectdetectors上图即来自于Google的论文。一个比较粗略的结论是:第一类框架(Faster R-CNN)的性能会更好,但是速度较慢;而第二类框架(SSD)的性能略微...
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I have never touched image classification before in any class or personal project, so I had to start from scratch in terms of learning how this is done. Additionally, this is my first time using Keras pre-trained models for transfer learning, and I am incredibly impressed at how simple the...
deep learning (DL) models. Using such models would enable the detection of newly-released malware through mathematical generalization. That is, finding the relationship between a given malwarexand its corresponding malware familyy,f : x → y. To accomplish this feat, we used the Malimg dataset[...