Network Intrusion Detection
47 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.
Benchmarks
These leaderboards are used to track progress in Network Intrusion Detection
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Libraries
Use these libraries to find Network Intrusion Detection models and implementationsDatasets
UNSW-NB15
CICIDS2017
SIDD-Image
CIC IoT Dataset 2022
IoT Network Intrusion Dataset
IoT Benign and Attack Traces
IoT ENVIRONMENT DATASET
Latest papers with no code
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
As a defence method, Adversarial Training is used to increase the robustness of the NIDS model.
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction
However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4.
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier
Performance of XGBoost, which represents conventional ML, is evaluated.
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning
Machine Learning (ML) has become ubiquitous, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes of data.
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation
The knowledge gained from our study on the adversary's ability to make specific evasive perturbations to different types of malicious packets can help defenders enhance the robustness of their NIDS against evolving adversarial attacks.
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour
It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection
Cybersecurity has emerged as a critical challenge for the industry.
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection
To address the above issue, this paper proposes a graph neural network algorithm based on behavior similarity (BS-GAT) using graph attention network.
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems
However, many studies in the field of computer vision have shown that ML can be fooled by intentionally crafted instances, called adversarial examples.
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems
As a result, the purpose of this study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems (NIDS), demonstrating that it is entirely possible to fool these ML-based IDSs using our proposed adversarial algorithm while assuming as many constraints as possible in a black-box setting.
