Less is More: A privacy-respecting Android malware classifier using Federated Learning

• URL: http://arxiv.org/abs/2007.08319v3
• Date: Wed, 16 Jun 2021 09:41:05 GMT
• Title: Less is More: A privacy-respecting Android malware classifier using Federated Learning
• Authors: Rafa G\'alvez, Veelasha Moonsamy, Claudia Diaz
• Abstract summary: LiM is a malware classification framework that leverages Federated Learning to detect and classify malicious apps. Information about newly installed apps is kept locally on users' devices, so that the provider cannot infer which apps were installed by users. We show that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server.
• Score: 2.1301190271783312
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper we present LiM ("Less is More"), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users' devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in >100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with MaMaDroid's dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.

Related papers

• Security Analysis of Top-Ranked mHealth Fitness Apps: An Empirical Study [0.32885740436059047]
  We investigate the security vulnerabilities of ten top-ranked Android health and fitness apps, a set that accounts for 237 million downloads. Our findings revealed many vulnerabilities, such as insecure coding, hardcoded sensitive information, over-privileged permissions, misconfiguration, and excessive communication with third-party domains.
  arXiv  Detail & Related papers  (2024-09-27T08:11:45Z)
• Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks [48.70867241987739]
  InferGuard is a novel Byzantine-robust aggregation rule aimed at defending against client-side training data distribution inference attacks. The results of our experiments indicate that our defense mechanism is highly effective in protecting against client-side training data distribution inference attacks.
  arXiv  Detail & Related papers  (2024-03-05T17:41:35Z)
• FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks [1.689369173057502]
  Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights.
  arXiv  Detail & Related papers  (2023-12-04T21:37:50Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning [1.9374282535132377]
  We study client-side detectability of malicious server (MS) attacks for the first time. We propose SEER, a novel attack framework that satisfies these requirements. We show that SEER can steal user data from gradients of realistic networks, even for large batch sizes of up to 512.
  arXiv  Detail & Related papers  (2023-06-05T16:29:54Z)
• FLCert: Provably Secure Federated Learning against Poisoning Attacks [67.8846134295194]
  We propose FLCert, an ensemble federated learning framework that is provably secure against poisoning attacks. Our experiments show that the label predicted by our FLCert for a test input is provably unaffected by a bounded number of malicious clients.
  arXiv  Detail & Related papers  (2022-10-02T17:50:04Z)
• FLVoogd: Robust And Privacy Preserving Federated Learning [12.568409209047505]
  We proposeoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. Servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring sensitive personal information. Our framework is automatic and adaptive that servers/clients don't need to tune the parameters during the training.
  arXiv  Detail & Related papers  (2022-06-24T08:48:15Z)
• Dynamic detection of mobile malware using smartphone data and machine learning [0.0]
  Mobile malware are malicious programs that target mobile devices. Number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. In this paper, we provide an overview of the performance of machine learning (ML) techniques to detect malware on Android, without using privileged access.
  arXiv  Detail & Related papers  (2021-07-23T12:33:14Z)
• Federated Learning-based Active Authentication on Mobile Devices [98.23904302910022]
  User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. We propose a novel user active authentication training, termed as Federated Active Authentication (FAA) We show that existing FL/SL methods are suboptimal for FAA as they rely on the data to be distributed homogeneously.
  arXiv  Detail & Related papers  (2021-04-14T22:59:08Z)
• Towards Bidirectional Protection in Federated Learning [70.36925233356335]
  F2ED-LEARNING offers bidirectional defense against malicious centralized server and Byzantine malicious clients. F2ED-LEARNING securely aggregates each shard's update and launches FilterL2 on updates from different shards. evaluation shows that F2ED-LEARNING consistently achieves optimal or close-to-optimal performance.
  arXiv  Detail & Related papers  (2020-10-02T19:37:02Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)

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.