# Malware Detection Edit

17 papers with code · Knowledge Base

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, 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

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# Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset.

468

# DeepXplore: Automated Whitebox Testing of Deep Learning Systems

18 May 2017peikexin9/deepxplore

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

297

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

20 Feb 2017yanminglai/Malware-GAN

This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models.

56

# Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

22 Aug 2017xiaojunxu/dnn-binary-code-similarity

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.

47

# Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

23 Jun 2020max-andr/square-attack

A large body of research has focused on adversarial attacks which require to modify all input features with small $l_2$- or $l_\infty$-norms.

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# Efficient Formal Safety Analysis of Neural Networks

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.

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

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.

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# Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection

This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other.

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# Transfer Learning for Image-Based Malware Classification

21 Jan 2019pratikpv/malware_classification

In this paper, we consider the problem of malware detection and classification based on image analysis.

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# NetML: A Challenge for Network Traffic Analytics

25 Apr 2020ACANETS/NetML-Competition2020

Classifying network traffic is the basis for important network applications.

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