A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework

• URL: http://arxiv.org/abs/2310.17127v1
• Date: Thu, 26 Oct 2023 03:56:40 GMT
• Title: A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework
• Authors: Loc Gia Nguyen, Kohei Watabe,
• Abstract summary: This research aims to explore the possibility of using sequences of flows to improve the domain adaptation capability of network intrusion detection systems. Our proposal employs natural language processing techniques and Bidirectional Representations from Transformers framework. Early empirical results show that our approach has improved domain adaptation capability compared to previous approaches.
• Score: 0.9208007322096533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A Network Intrusion Detection System (NIDS) is a tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as solutions to detect intrusions efficiently. However, conventional ML-based classifiers have not seen widespread adoption in the real world due to their poor domain adaptation capability. In this research, our goal is to explore the possibility of using sequences of flows to improve the domain adaptation capability of network intrusion detection systems. Our proposal employs natural language processing techniques and Bidirectional Encoder Representations from Transformers framework, which is an effective technique for modeling data with respect to its context. Early empirical results show that our approach has improved domain adaptation capability compared to previous approaches. The proposed approach provides a new research method for building a robust intrusion detection system.

Related papers

• Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
  Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
  arXiv  Detail & Related papers  (2024-07-08T09:18:59Z)
• Few-Shot API Attack Detection: Overcoming Data Scarcity with GAN-Inspired Learning [9.035212370386846]
  This paper proposes a novel few-shot detection approach motivated by Natural Language Processing (NLP) and advanced Generative Adrialversa Network (GAN)-inspired techniques. Our method enhances the contextual understanding of API requests, leading to improved anomaly detection compared to traditional methods.
  arXiv  Detail & Related papers  (2024-05-18T11:10:45Z)
• Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation [79.22678026708134]
  In this paper, we propose an inherently interpretable method, named Transferable Prototype Learning ( TCPL) To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
  arXiv  Detail & Related papers  (2023-10-12T06:36:41Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Orthogonal variance-based feature selection for intrusion detection systems [0.0]
  We apply a fusion machine learning method to construct an automatic intrusion detection system. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks.
  arXiv  Detail & Related papers  (2021-10-25T04:07:53Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• A cognitive based Intrusion detection system [0.0]
  Intrusion detection is one of the important mechanisms that provide computer networks security. This paper proposes a new approach based on Deep Neural Network ans Support vector machine classifier. The proposed model predicts the attacks with better accuracy for intrusion detection rather similar methods.
  arXiv  Detail & Related papers  (2020-05-19T13:30:30Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure [0.0]
  The smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks.
  arXiv  Detail & Related papers  (2019-12-31T21:06:20Z)

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.