SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 301–350 of 800 papers

TitleStatusHype
Real-time Network Intrusion Detection via Decision Transformers—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
Intrusion Detection System with Machine Learning and Multiple Datasets—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Open Set Dandelion Network for IoT Intrusion Detection—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection—0
Efficient Network Representation for GNN-based Intrusion Detection—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Multidomain transformer-based deep learning for early detection of network intrusion—0
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Real-time Regular Expression Matching—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System—0
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—0
Man-in-the-Middle Intrusion Detection Based on CNN-LSTM Model—0
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation—0
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction—0
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection—0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier—0
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments—0
Intrusion Detection: A Deep Learning Approach—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified