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 351–375 of 800 papers

TitleStatusHype
Joint Semantic Transfer Network for IoT Intrusion Detection—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
DI-NIDS: Domain Invariant Network Intrusion Detection System—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Anomaly Detection via Federated Learning—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Network Intrusion Detection System in a Light Bulb—0
LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique—0
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data—0
Zero-day DDoS Attack Detection—0
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networksCode0
ECU Identification using Neural Network Classification and Hyperparameter Tuning—0
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset—0
Designing an Artificial Immune System inspired Intrusion Detection System—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