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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 800 papers

TitleStatusHype
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
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G—0
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—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
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques—0
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
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
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
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities—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