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 251–275 of 800 papers

TitleStatusHype
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
Deep Reinforcement Learning for Cyber Security—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
A short review on Applications of Deep learning for Cyber security—0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms—0
ADASYN-Random Forest Based Intrusion Detection Model—0
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System—0
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware—0
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection—0
DualNet: Locate Then Detect Effective Payload with Deep Attention 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