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 276–300 of 800 papers

TitleStatusHype
A concise method for feature selection via normalized frequencies—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
An AutoML-based approach for Network Intrusion Detection—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Attribute Learning for Network Intrusion Detection—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
A Transfer Learning Approach for Network Intrusion Detection—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
A SVM and K-means clustering based fast and efficient intrusion detection system—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