Representation learning with function call graph transformations for malware open set recognition

• URL: http://arxiv.org/abs/2205.06918v1
• Date: Fri, 13 May 2022 22:40:14 GMT
• Title: Representation learning with function call graph transformations for malware open set recognition
• Authors: Jingyun Jia, Philip K. Chan
• Abstract summary: Open set recognition problem has been a challenge in many machine learning (ML) applications, such as security. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while sensitive to the unknown class. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification. We propose two transformations for the function call graph (FCG) based malware representations to facilitate the pretext task. Also, we present a statistical thresholding approach to find the optimal threshold for the unknown class. Moreover, the experiment results indicate that our proposed pre-training process can improve different performances of different downstream loss functions for the OSR problem.

Related papers

• Improving Malware Detection with Adversarial Domain Adaptation and Control Flow Graphs [10.352741619176383]
  Existing solutions to combat concept drift use active learning. We propose a method that learns retained information in malware control flow graphs post-drift by leveraging graph neural network. Our approach demonstrates a significant enhancement in predicting unseen malware family in a binary classification task and predicting drifted malware families in a multi-class setting.
  arXiv  Detail & Related papers  (2024-07-18T22:06:20Z)
• Class-Incremental Learning: A Survey [84.30083092434938]
  Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally. CIL tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. We provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms.
  arXiv  Detail & Related papers  (2023-02-07T17:59:05Z)
• PatchMix Augmentation to Identify Causal Features in Few-shot Learning [55.64873998196191]
  Few-shot learning aims to transfer knowledge learned from base with sufficient categories labelled data to novel categories with scarce known information. We propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency. We show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
  arXiv  Detail & Related papers  (2022-11-29T08:41:29Z)
• MERLIN -- Malware Evasion with Reinforcement LearnINg [26.500149465292246]
  We propose a method using reinforcement learning with DQN and REINFORCE algorithms to challenge two state-of-the-art malware detection engines. Our method combines several actions, modifying a Windows portable execution file without breaking its functionalities. We demonstrate that REINFORCE achieves very good evasion rates even on a commercial AV with limited available information.
  arXiv  Detail & Related papers  (2022-03-24T10:58:47Z)
• MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning [65.52675802289775]
  We show that an uncertainty aware classifier can solve challenging reinforcement learning problems. We propose a novel method for computing the normalized maximum likelihood (NML) distribution. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions.
  arXiv  Detail & Related papers  (2021-07-15T08:19:57Z)
• SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption [72.35532598131176]
  We propose SCARF, a technique for contrastive learning, where views are formed by corrupting a random subset of features. We show that SCARF complements existing strategies and outperforms alternatives like autoencoders.
  arXiv  Detail & Related papers  (2021-06-29T08:08:33Z)
• Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition [0.0]
  We propose a self-supervision method, Detransformation Autoencoder (DTAE) for the Open set recognition problem. Our proposed self-supervision method achieves significant gains in detecting the unknown class and classifying the known classes.
  arXiv  Detail & Related papers  (2021-05-28T02:45:57Z)
• Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
  Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks. We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI) In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
  arXiv  Detail & Related papers  (2020-12-31T08:21:37Z)
• Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition [0.0]
  We propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM. Our method can use unsupervised operational data to enhance recognition. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-12-17T13:28:13Z)
• MMF: A loss extension for feature learning in open set recognition [0.0]
  We propose an add-on extension for loss functions in neural networks to address the open set recognition problem. Our loss extension leverages the neural network to find polar representations for the known classes so that the representations of the known and the unknown classes become more effectively separable.
  arXiv  Detail & Related papers  (2020-06-26T17:22:11Z)
• Few-Shot Open-Set Recognition using Meta-Learning [72.15940446408824]
  The problem of open-set recognition is considered. A new oPen sEt mEta LEaRning (PEELER) algorithm is introduced.
  arXiv  Detail & Related papers  (2020-05-27T23:49:26Z)
• Deep Learning and Open Set Malware Classification: A Survey [0.0]
  Recent machine learning works have shed light on Open Set Recognition (OSR) problem in machine learning. OSR system should not only correctly classify the known classes, but also recognize the unknown class. This survey provides an overview of different deep learning techniques, a discussion of OSR and graph representation solutions and an introduction of malware classification systems.
  arXiv  Detail & Related papers  (2020-04-08T21:36:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.