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 426–450 of 800 papers

TitleStatusHype
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Using EBGAN for Anomaly Intrusion Detection—0
Hands-on Wireless Sensing with Wi-Fi: A Tutorial—0
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic ClassificationCode0
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System—0
Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus—0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing—0
A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion DetectionCode0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars—0
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Anomaly Detection in Intra-Vehicle Networks—0
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System—0
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach—0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection—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