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 126–150 of 800 papers

TitleStatusHype
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
Detecting message modification attacks on the CAN bus with Temporal Convolutional NetworksCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
A Renewal Model of IntrusionCode0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Data Distribution ValuationCode0
Behavioural Reports of Multi-Stage MalwareCode0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
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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