Malware Detection

81 papers with code • 2 benchmarks • 4 datasets

Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware

Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Most implemented papers

Malware Detection by Eating a Whole EXE

endgameinc/malware_evasion_competition 25 Oct 2017

In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community.

Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

CyberForce/Pesidious 20 Feb 2017

A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector.

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

fiona-lxd/AdvKnn 13 Mar 2018

However, deep learning is often criticized for its lack of robustness in adversarial settings (e. g., vulnerability to adversarial inputs) and general inability to rationalize its predictions.

subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs

mldroid/subgraph2vec_gensim 29 Jun 2016

Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

peikexin9/deepxplore 18 May 2017

First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.

A learning model to detect maliciousness of portable executable using integrated feature set

urwithajit9/ClaMP journal 2017

In the experiments conducted on the novel test data set the accuracy was observed as 89. 23% for the integrated feature set which is 15% improvement on accuracy achieved with raw-feature set alone.

Learning the PE Header, Malware Detection with Minimal Domain Knowledge

jaketae/pytorch-malware-detection 5 Sep 2017

Many efforts have been made to use various forms of domain knowledge in malware detection.

DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification

tychen5/sportslottery 21 Nov 2017

While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants.

Efficient Formal Safety Analysis of Neural Networks

tcwangshiqi-columbia/ReluVal NeurIPS 2018

Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.

Automatic Malware Description via Attribute Tagging and Similarity Embedding

sophos-ai/SOREL-20M 15 May 2019

With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.