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An AI Approach to Malware Similarity Analysis: Mapping the Malware Genome With a Deep Neural Network
ICAASE 2020 | Android Malware Detection using Convolutional Deep Neural Networks
Deep Learning for Mobile Malware Detection
Advanced Android malware attacks against ML detection systems
[FSE 2022] On the Impact of Sample Duplication in Machine Learning based Android Malware Detection
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image
AndroOBFS: Time-tagged Obfuscated Android Malware Dataset with Family Information
Android Malware Detection and Prevention Using Deep Learning (LSTM) | Django | Final Year Project
Graph Neural Networks for Android Malware Detection and Robustness
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Last Updated: October 27, 2026
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Summary
Prof. V.S. Subrahmanian, Professor of Computer Science, University of Maryland. ElMouatez Billah Karbab discusses his work at DFRWS EU 2018. Hi guys, In this video I am doing a room called by Konstantin Berlin In recent years, cyber defenders protecting enterprise networks have started incorporating The 4th Edition of the International Conference on Advanced Aspects of Software Engineering (ICAASE'20) Fatima Bourabaa and ... NOW to Queen's University Belfast: bit.ly/1Y24vux MORE from Queen's University Belfast: Queen's University ... UCL Information Security Research Seminar on 12.05.22 Abstract: A growing number of For the presentation of FSE 2022. DexRay: A Simple, yet Effective Presentation held at MSR 2022. Paper by Saurabh Kumar, Debadatta Mishra, Biswabandan Panda and Sandeep Kumar Shukla. This episode examines XGCN-GAT, a hybrid graph
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