Towards Deep Federated Defenses Against Malware in Cloud Ecosystems

• URL: http://arxiv.org/abs/1912.12370v1
• Date: Fri, 27 Dec 2019 23:46:06 GMT
• Title: Towards Deep Federated Defenses Against Malware in Cloud Ecosystems
• Authors: Josh Payne and Ashish Kundu
• Abstract summary: In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. We introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language.
• Score: 0.24366811507669117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.

Related papers

• How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure [0.0]
  Combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data. Traditional security technologies are unable to cope with the rapid data explosion in cloud platforms. Machine learning powered security solutions and data visualization techniques are playing instrumental roles in detecting security threat, data breaches, and automatic finding software vulnerabilities.
  arXiv  Detail & Related papers  (2024-05-19T16:10:03Z)
• Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange [50.45953583802282]
  We introduce a novel self-supervised learning (SSL) strategy for point cloud scene understanding. Our approach leverages both object patterns and contextual cues to produce robust features. Our experiments demonstrate the superiority of our method over existing SSL techniques.
  arXiv  Detail & Related papers  (2024-04-11T06:39:53Z)
• An Approach to Abstract Multi-stage Cyberattack Data Generation for ML-Based IDS in Smart Grids [2.5655761752240505]
  We propose a method to generate synthetic data using a graph-based approach for training machine learning models in smart grids. We use an abstract form of multi-stage cyberattacks defined via graph formulations and simulate the propagation behavior of attacks in the network.
  arXiv  Detail & Related papers  (2023-12-21T11:07:51Z)
• Discovering Malicious Signatures in Software from Structural Interactions [7.06449725392051]
  We propose a novel malware detection approach that leverages deep learning, mathematical techniques, and network science. Our approach focuses on static and dynamic analysis and utilizes the Low-Level Virtual Machine (LLVM) to profile applications within a complex network. Our approach marks a substantial improvement in malware detection, providing a notably more accurate and efficient solution.
  arXiv  Detail & Related papers  (2023-12-19T23:42:20Z)
• Nebula: Self-Attention for Dynamic Malware Analysis [14.710331873072146]
  We introduce Nebula, a versatile, self-attention Transformer-based neural architecture that generalizes across different behavioral representations and formats. We perform experiments on both malware detection and classification tasks, using three datasets acquired from different dynamic analyses platforms. We showcase how self-supervised learning pre-training matches the performance of fully-supervised models with only 20% of training data.
  arXiv  Detail & Related papers  (2023-09-19T09:24:36Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Analyzing Machine Learning Approaches for Online Malware Detection in Cloud [0.0]
  We present online malware detection based on process level performance metrics and analyze the effectiveness of different machine learning models. Our analysis conclude that neural network models can most accurately detect the malware that have on the process level features of virtual machines in the cloud.
  arXiv  Detail & Related papers  (2021-05-19T17:28:12Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

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.