wkend / android_malware_detection_model

Related projects: ⓘ

• a574824551 / Malware_detection_system_based_CNN
  本科毕业设计，参考LENET-5模型，将恶意软件转化为灰度图，通过卷积神经网络实现了恶意软件检测分类，准确率达98%。
  ☆19Updated 4 years ago
• eastmountyxz / Malicious-Code-Detection
  该资源为恶意代码检测与识别的相关链接汇总，希望对您有所帮助！
  ☆20Updated 4 years ago
• daivc96 / android-malware-classification
  ☆7Updated 7 months ago
• namk12 / Malware-Detection
  Deep Learning Based Android Malware Detection Framework
  ☆39Updated 3 years ago
• mahitaberrefae / Malware-Detection
  With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, …
  ☆21Updated 5 years ago
• vikram-mm / Android-Malware-Detection
  ☆59Updated 6 years ago
• RManLuo / ML_Malware_detect
  阿里云安全恶意程序检测比赛
  ☆113Updated 3 years ago
• BetaCollins / Malware-Detection
  XGBoost + CNN to detect malware using PE header
  ☆9Updated 4 years ago
• yuxiaorun / MalConv-Adversarial
  Reproduction of the paper//arxiv.org/pdf/1803.04173.pdf
  ☆18Updated 6 years ago
• Echo-Ws / UrlDetect
  a demo for detecting anomaly url
  ☆38Updated 7 years ago
• ylhao / malware-detection-based-on-cnn
  基于卷积神经网络的恶意软件检测方法
  ☆45Updated 5 years ago
• cmikke97 / Automatic-Malware-Signature-Generation
  Automatic Malware Signature Generation Tool
  ☆12Updated 2 years ago
• aditya5558 / Android-Malware-Detection
  Android Malware Detection using Deep Learning
  ☆50Updated 5 years ago
• PowerLZY / MalGAN
  主题为”基于GAN的恶意软件对抗样本生成“。首先介绍了恶意软件发展现状，引出基于模式匹配、特征空间和问题空间三种方式去检测恶意软件。然后介绍了如何生成对抗样本攻击恶意软件检测器，详细介绍了基于GAN的恶意软件对抗样本的MalGAN框架，并对实验结果进行了对比。最后总结了结构…
  ☆30Updated 3 years ago
• aup8497 / Android-Malware-Detection
  Android Malware detection using Deep Learning Hybrid Model.
  ☆23Updated 6 years ago
• scusec / Trending-Research-Topics
  Trending Research Topics In Cybersecurity
  ☆20Updated 4 years ago
• tomergill / Malware_Classification_Final_Project
  Yossi Mandil & Tomer Gill's Bachelor Degree Final Project under the BIU Cyber Center - Malware & Benign File Classification using Machin…
  ☆10Updated 6 years ago
• flame0409 / Android-Malware-Detection
  使用安卓Opcode字节码的N-gram序列特征进行恶意软件检测的完全步骤，使用算法RF，KNN
  ☆18Updated 4 years ago
• PowerLZY / MalConv-Pytorch
  基于深度学习的恶意软件检测研究；MalConv;
  ☆73Updated 2 years ago
• DQinYuan / AndroidMalwareWithN-gram
  提取安卓软件的n-gram特征
  ☆22Updated 6 years ago
• eastmountyxz / Paper-Malicious-Code-Detection
  该资源为恶意代码检测相关的论文或文章总结，包括作者撰写的恶意代码与机器学习、深度学习相关博客，希望对您有所帮助~
  ☆12Updated 4 years ago
• darkcodetop / AI-Security-Awesome
  收集了人工智能在网络安全领域的比赛、应用案例和博客。
  ☆20Updated 4 years ago
• 2015-10-10 / MalwareClassification
  利用机器学习检测恶意代码
  ☆20Updated 8 years ago
• Arun152k / Malware-Detection-using-N-Gram-Frequency
  An attempt to detect malware using Opcodes and Hexadecimal Instructions.
  ☆21Updated 3 years ago
• cyber-research / APTAttribution
  Code for Benchmarking two ML Approaches performing Authorship Attribution
  ☆34Updated 2 years ago
• czs108 / Microsoft-Malware-Classification
  🔍 "2015 Microsoft Malware Classification Challenge" - Using machine learning to classify malware into different families based on Window…
  ☆16Updated 7 months ago
• Chauncy-lab / Deep-learning-of-DGA
  基于深度学习对dga恶意域名检测研究
  ☆49Updated last year
• kericwy1337 / Datacon2019-Malicious-Code-DataSet-Stage1
  ☆19Updated 5 years ago
• eastmountyxz / AI-for-Malware-Analysis-
  这是作者恶意代码分析、网络安全、系统安全等系列教程，主要是通过机器学习、人工智能和深度学习来分析恶意代码的在线笔记。希望对您有所帮助，学无止境，一起加油。
  ☆93Updated 4 years ago
• zwq0320 / malicious_dynamic_behavior_detection_by_cnn
  ☆173Updated 6 years ago