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 376–400 of 800 papers

TitleStatusHype
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of VehiclesCode2
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
Digital Twin-based Intrusion Detection for Industrial Control SystemsCode1
IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge—0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities—0
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
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
CoAP-DoS: An IoT Network Intrusion Dataset—0
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
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
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—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