Network Intrusion Detection

46 papers with code • 5 benchmarks • 12 datasets

Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.

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Use these libraries to find Network Intrusion Detection models and implementations
2 papers
257

Latest papers with no code

EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning

no code yet • 24 Mar 2024

As the number of IoT devices increases, security concerns become more prominent.

Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks

no code yet • 18 Mar 2024

Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS).

A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats

no code yet • 17 Mar 2024

Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm.

An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection

no code yet • 25 Feb 2024

As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes.

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

no code yet • 22 Jan 2024

Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities.

Real-time Network Intrusion Detection via Decision Transformers

no code yet • 12 Dec 2023

Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.

RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture

no code yet • 27 Nov 2023

Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability.

SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification

no code yet • 19 Nov 2023

Numerous studies have proved their effective strength in detecting Control Area Network (CAN) attacks.

Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection

no code yet • 30 Oct 2023

Finally, we show LLM generated decision tree explanations correlate highly with human ratings of readability, quality, and use of background knowledge while simultaneously providing better understanding of decision boundaries.

netFound: Foundation Model for Network Security

no code yet • 25 Oct 2023

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.