sharpened-cosine-similarity VS DeepMalwareDetector

Compare sharpened-cosine-similarity vs DeepMalwareDetector and see what are their differences.

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sharpened-cosine-similarity DeepMalwareDetector
2 1
254 73
0.0% -
0.0 0.0
11 months ago almost 2 years ago
Python Python
MIT License -
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sharpened-cosine-similarity

Posts with mentions or reviews of sharpened-cosine-similarity. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-22.

DeepMalwareDetector

Posts with mentions or reviews of DeepMalwareDetector. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-26.
  • Looking for insight on labelling portable executable (PE) malware files using a VirusTotal API response report.
    3 projects | /r/Malware | 26 Aug 2022
    What brought me to this research was these studies [1] [2], which demonstrates how image-based malware classification can be done using a CNN (convolutional neural network). Since I had a bit of a background with malware, and I recently completed a CNN model, I figured I would try to do something similar. It was only after investigating different materials I hit a bit of a roadblock. I found this one dataset, malimg [3], which is made up of PE files that have been converted into images already. I didn't want to just use the images, I wanted to demonstrate how to get them, only the method used to classify them turned out to be a bit out of my depth, kind of like this whole project, it's discussed in Section 4.2 of this paper [4] . There's also this set [5], which contains the pixel content for each file record. And as for the static disassembly you mention, I think you are right, the training data might not exist. During my investigation the best I could find was this study [6].

What are some alternatives?

When comparing sharpened-cosine-similarity and DeepMalwareDetector you can also consider the following projects:

convolution-vision-transformers - PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers

easyesn - Python library for Reservoir Computing using Echo State Networks

hub - A library for transfer learning by reusing parts of TensorFlow models.

SparK - [ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"

Convolution-From-Scratch - Implementation of the generalized 2D convolution with dilation from scratch in Python and NumPy

strelka - Real-time, container-based file scanning at enterprise scale

albumentations - Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

Unredactor - In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files. These files are used to buils corpora for finding tfidf score. Few files are used to train and in these files names are redacted and written into redacted folder. These redacted files are used for testing and different classification models are built to predict the probabilies of each class. Top 5 classes i.e names similar to the test features are written at the end of text in unreddacted foleder.

SCS-CCT - CCT but using Sharpened Cosine Similarity

MDML - Malware Detection using Machine Learning (MDML)

tinysleepnet - TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College London respectively

avclass - AVClass malware labeling tool

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Nutrient - The #1 PDF SDK Library
Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
nutrient.io
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