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.

Libraries

Use these libraries to find Network Intrusion Detection models and implementations
2 papers
255

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network

renj-xu/negsc 3 Mar 2024

To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.

5
03 Mar 2024

On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection

marcocantone/lycos-unicas-ids2018 15 Feb 2024

The results show nearly perfect classification performance when the models are trained and tested on the same dataset.

1
15 Feb 2024

Deep Learning Applications for Intrusion Detection in Network Traffic

fisher85/ml-cybersecurity Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS) 2024

The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection.

43
13 Jan 2024

Improving Transferability of Network Intrusion Detection in a Federated Learning Setup

ghosh64/transferability 7 Jan 2024

Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device.

2
07 Jan 2024

A Study on Transferability of Deep Learning Models for Network Intrusion Detection

ghosh64/transferability 17 Dec 2023

In this paper, we explore transferability in learning between different attack classes in a network intrusion detection setup.

2
17 Dec 2023

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

donghao51/nng-mix 20 Nov 2023

While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.

3
20 Nov 2023

LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

wangkai-tech23/LiPar 14 Nov 2023

Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.

2
14 Nov 2023

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

kahramankostas/IoTGeM 17 Oct 2023

In this paper we present an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance.

3
17 Oct 2023

PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference

martaandronic/polylut 5 Sep 2023

We show that by using polynomial building blocks, we can achieve the same accuracy using considerably fewer layers of soft logic than by using linear functions, leading to significant latency and area improvements.

29
05 Sep 2023

Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

andreacorsini1/cyberood 28 Aug 2023

Our findings suggest that existing detectors can identify a consistent portion of new malicious traffic, and that improved embedding spaces enhance detection.

4
28 Aug 2023