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 776–800 of 800 papers

TitleStatusHype
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
A survey on deep packet inspection for intrusion detection systems—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
A SVM and K-means clustering based fast and efficient intrusion detection system—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
A Transfer Learning Approach for Network Intrusion Detection—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
Attribute Learning for Network Intrusion Detection—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
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
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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