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advance_python

Employee mangement

android-malware-detection

Android Malware detection using Deep Learning Hybrid Model.

android-malware-detection-1

android-malware-detection-2

Android Malware Detection using Deep Learning

android-malware-detection-using-a-machine-learning-approach

Extracted a number of features from Packaged Android Applications (APKs) to train a One class Support Vector Machine using the Scikit-learn framework and detected malware on Android devices.

android_malwareanalysis

CNN based android malware detection system

binary-search-tree

blog-mean-stack

blogpost

blood-management-system

blood_bank

A simple blood bank project.

blood_bank1

blood_bank_internshala

bloodbank

code_snippets

deep-android-malware-detection

Code for Deep Android Malware Detection paper

deepnet-framework-for-malware-analysis

drebin-malwares

Malware detection using the Drebin dataset

hashinclude

internshala

internshala1

ip-project

malware-analysis

using Drebin dataset to distinguish between malwares and not malwares

malware-analysis-1

Binary Classification of Malwares from the Drebin dataset.

malware-detection

With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. We propose an android malware detection system using a deep learning technique called graph convolutional neural networks. The network works on android applications. We extract some information about the application and add it to the windows event log of the server containing the network. This is the main server which is a part of a local area network that contains a number of computers that send their windows event logs to Elastic Stack. We then use the Stack to visualize this data on a browser to do some further analysis.

malware-detection-using-deep-learning

Firstly, we generate images from benign and malware executable files. Secondly, by using deep learning, we train a model to detect malware files. Then, by the trained model, we try to classify a file as malware or not. By using malware images and deep learning, we can detect malware fast since we do not need any static analysis or dynamic analysis.

meanblog

permission-based-android-malware-detection

Implementation of Permission Based Android Malware Detection

spark_machine_learning

wine_quality_prediction