An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks

• URL: http://arxiv.org/abs/2404.01109v1
• Date: Mon, 1 Apr 2024 13:33:40 GMT
• Title: An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks
• Authors: Abdullah H Alqahtani,
• Abstract summary: Advanced Persistent Threats (APTs) are a type of attack that implement a wide range of strategies to evade detection. Machine Learning (ML) techniques in Intrusion Detection Systems (IDSs) is widely used to detect such attacks but has a challenge when the data distribution changes. An incremental hybrid adaptive Network Intrusion Detection System (NIDS) is proposed to tackle the issue of concept drift in SDN.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network attacks have became increasingly more sophisticated and stealthy due to the advances in technologies and the growing sophistication of attackers. Advanced Persistent Threats (APTs) are a type of attack that implement a wide range of strategies to evade detection and be under the defence radar. Software Defined Network (SDN) is a network paradigm that implements dynamic configuration by separating the control plane from the network plane. This approach improves security aspects by facilitating the employment of network intrusion detection systems. Implementing Machine Learning (ML) techniques in Intrusion Detection Systems (IDSs) is widely used to detect such attacks but has a challenge when the data distribution changes. Concept drift is a term that describes the change in the relationship between the input data and the target value (label or class). The model is expected to degrade as certain forms of change occur. In this paper, the primary form of change will be in user behaviour (particularly changes in attacker behaviour). It is essential for a model to adapt itself to deviations in data distribution. SDN can help in monitoring changes in data distribution. This paper discusses changes in stealth attacker behaviour. The work described here investigates various concept drift detection algorithms. An incremental hybrid adaptive Network Intrusion Detection System (NIDS) is proposed to tackle the issue of concept drift in SDN. It can detect known and unknown attacks. The model is evaluated over different datasets showing promising results.

Related papers

• CND-IDS: Continual Novelty Detection for Intrusion Detection Systems [7.196884299359838]
  Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged as a promising solution for IDS, offering highly accurate intrusion detection. We propose CND-IDS, a continual novelty detection IDS framework which consists of (i) a learning-based feature extractor that continuously updates new feature representations of the system data, and (ii) a novelty detector that identifies new cyber attacks by leveraging principal component analysis (PCA) reconstruction.
  arXiv  Detail & Related papers  (2025-02-19T20:47:22Z)
• Unseen Attack Detection in Software-Defined Networking Using a BERT-Based Large Language Model [5.062869359266078]
  We introduce a novel approach that leverages Natural Language Processing (NLP) and the pre trained BERT base model to enhance attack detection in Software defined networking (SDN) Our approach transforms network flow data into a format interpretable by language models, allowing BERT to capture intricate patterns and relationships within network traffic. Our approach is specifically designed to detect previously unseen attacks, offering a solution for identifying threats that the model was not explicitly trained on.
  arXiv  Detail & Related papers  (2024-12-09T06:27:20Z)
• SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type Classification [0.0]
  Intrusion detection systems (IDSs) are far from being able to quickly and efficiently identify complex and varied network attacks. The SCGNet is a novel deep learning architecture that we propose in this study. It exhibits promising results on the NSL-KDD dataset in both task, network attack detection, and attack type classification with 99.76% and 98.92% accuracy, respectively.
  arXiv  Detail & Related papers  (2024-10-29T09:09:08Z)
• Dynamics-aware Adversarial Attack of Adaptive Neural Networks [75.50214601278455]
  We investigate the dynamics-aware adversarial attack problem of adaptive neural networks. We propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. Our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods.
  arXiv  Detail & Related papers  (2022-10-15T01:32:08Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Early Detection of Network Attacks Using Deep Learning [0.0]
  A network intrusion detection system (IDS) is a tool used for identifying unauthorized and malicious behavior by observing the network traffic. We propose an end-to-end early intrusion detection system to prevent network attacks before they could cause any more damage to the system under attack.
  arXiv  Detail & Related papers  (2022-01-27T16:35:37Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack [46.79557381882643]
  We present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. TANTRA achieves an average success rate of 99.99% in network intrusion detection system evasion.
  arXiv  Detail & Related papers  (2021-03-10T19:03:38Z)
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network [62.77129284830945]
  We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
  arXiv  Detail & Related papers  (2021-01-28T16:18:19Z)
• Cassandra: Detecting Trojaned Networks from Adversarial Perturbations [92.43879594465422]
  In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors into the models. We propose a method to verify if a pre-trained model is Trojaned or benign. Our method captures fingerprints of neural networks in the form of adversarial perturbations learned from the network gradients.
  arXiv  Detail & Related papers  (2020-07-28T19:00:40Z)
• Machine Learning based Anomaly Detection for 5G Networks [0.0]
  This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic.
  arXiv  Detail & Related papers  (2020-03-07T00:17:08Z)

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.