netFound: Foundation Model for Network Security

• URL: http://arxiv.org/abs/2310.17025v2
• Date: Tue, 28 Nov 2023 01:44:32 GMT
• Title: netFound: Foundation Model for Network Security
• Authors: Satyandra Guthula, Navya Battula, Roman Beltiukov, Wenbo Guo, Arpit Gupta
• Abstract summary: We develop netFound, a foundational model for network security. Our experiments demonstrate netFound's superiority over existing state-of-the-art ML-based solutions.
• Score: 12.062547301932966
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In ML for network security, traditional workflows rely on high-quality labeled data and manual feature engineering, but limited datasets and human expertise hinder feature selection, leading to models struggling to capture crucial relationships and generalize effectively. Inspired by recent advancements in ML application domains like GPT-4 and Vision Transformers, we have developed netFound, a foundational model for network security. This model undergoes pre-training using self-supervised algorithms applied to readily available unlabeled network packet traces. netFound's design incorporates hierarchical and multi-modal attributes of network traffic, effectively capturing hidden networking contexts, including application logic, communication protocols, and network conditions. With this pre-trained foundation in place, we can fine-tune netFound for a wide array of downstream tasks, even when dealing with low-quality, limited, and noisy labeled data. Our experiments demonstrate netFound's superiority over existing state-of-the-art ML-based solutions across three distinct network downstream tasks: traffic classification, network intrusion detection, and APT detection. Furthermore, we emphasize netFound's robustness against noisy and missing labels, as well as its ability to generalize across temporal variations and diverse network environments. Finally, through a series of ablation studies, we provide comprehensive insights into how our design choices enable netFound to more effectively capture hidden networking contexts, further solidifying its performance and utility in network security applications.

Related papers

• These Are Not All the Features You Are Looking For: A Fundamental Bottleneck in Supervised Pretraining [10.749875317643031]
  Transfer learning is a cornerstone of modern machine learning, promising a way to adapt models pretrained on a broad mix of data to new tasks with minimal new data.<n>We evaluate model transfer from a pretraining mixture to each of its component tasks, assessing whether pretrained features can match the performance of task-specific direct training.<n>We identify a fundamental limitation in deep learning models, where networks fail to learn new features once they encode similar competing features during training.
  arXiv  Detail & Related papers  (2025-06-23T01:04:29Z)
• tn4ml: Tensor Network Training and Customization for Machine Learning [0.8799686507544172]
  tn4ml is a novel library designed to seamlessly integrate Networks into Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training.
  arXiv  Detail & Related papers  (2025-02-18T17:57:29Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks. We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications [17.707450193500698]
  Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA.
  arXiv  Detail & Related papers  (2024-11-15T03:01:23Z)
• Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning [79.46570165281084]
  We propose a Multi-Stage Knowledge Integration network (MulKI) to emulate the human learning process in distillation methods. MulKI achieves this through four stages, including Eliciting Ideas, Adding New Ideas, Distinguishing Ideas, and Making Connections. Our method demonstrates significant improvements in maintaining zero-shot capabilities while supporting continual learning across diverse downstream tasks.
  arXiv  Detail & Related papers  (2024-11-11T07:36:19Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks [7.291687946822538]
  Continual learning on sequential data is critical for many machine learning (ML) deployments. LSTM networks suffer from catastrophic forgetting and are limited in their ability to learn multiple tasks continually. We discover that catastrophic forgetting in LSTM networks can be overcome in two novel and readily-implementable ways.
  arXiv  Detail & Related papers  (2023-05-26T20:17:18Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Transfer Learning with Pre-trained Conditional Generative Models [29.43740987925133]
  We propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training and pseudo semi-supervised learning. Our experimental results indicate that our method can outperform baselines of scratch training and knowledge distillation.
  arXiv  Detail & Related papers  (2022-04-27T10:36:32Z)
• Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond [59.94347858883343]
  This tutorial covers the basic steps as well as more recent options to improve models. It can be particularly useful in datasets that are not as well-prepared as those in challenges.
  arXiv  Detail & Related papers  (2021-09-06T21:31:42Z)
• Federated Learning: A Signal Processing Perspective [144.63726413692876]
  Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
  arXiv  Detail & Related papers  (2021-03-31T15:14:39Z)
• Applying Graph-based Deep Learning To Realistic Network Scenarios [5.453745629140304]
  This paper presents a new Graph-based deep learning model able to estimate accurately the per-path mean delay in networks. The proposed model can generalize successfully over topologies, routing configurations, queue scheduling policies and traffic matrices unseen during the training phase.
  arXiv  Detail & Related papers  (2020-10-13T20:58:59Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.