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# Malware Detection Edit

11 papers with code · Knowledge Base

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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.

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# 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.

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# 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.

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# 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.

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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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# Robust Neural Malware Detection Models for Emulation Sequence Learning

28 Jun 2018tychen5/sportslottery

These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences.

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# DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification

21 Nov 2017tychen5/sportslottery

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.

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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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# How to 0wn NAS in Your Spare Time

17 Feb 2020Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time

This provides an incentive for adversaries to steal these novel architectures; when used in the cloud, to provide Machine Learning as a Service, the adversaries also have an opportunity to reconstruct the architectures by exploiting a range of hardware side channels.

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