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 176–200 of 800 papers

TitleStatusHype
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Blockchain Large Language Models—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
A survey on deep packet inspection for intrusion detection systems—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—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