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 101–125 of 800 papers

TitleStatusHype
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Intrusion Detection Using Mouse DynamicsCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection SystemsCode0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Detecting message modification attacks on the CAN bus with Temporal Convolutional NetworksCode0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Data Distribution ValuationCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networksCode0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
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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