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 651–700 of 800 papers

TitleStatusHype
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
Detecting malicious logins as graph anomalies—0
Walling up Backdoors in Intrusion Detection SystemsCode0
Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks—0
A Transfer Learning Approach for Network Intrusion Detection—0
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—0
Sparse Bayesian approach for metric learning in latent spaceCode0
Omni SCADA Intrusion Detection Using Deep Learning Algorithms—0
Road Context-aware Intrusion Detection System for Autonomous Cars—0
Learning Neural Representations for Network Anomaly DetectionCode0
Data Analysis of Wireless Networks Using Classification Techniques—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle—0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
Deep Reinforcement Learning for Cyber Security—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data—0
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
Modern Problems Require Modern Solutions: Hybrid Concepts for Industrial Intrusion Detection—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection -- An Analysis on CIC-AWS-2018 dataset—0
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
A Compendium on Network and Host based Intrusion Detection Systems—0
Efficient GAN-based method for cyber-intrusion detection—0
Active Learning for Network Intrusion Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning—0
Rallying Adversarial Techniques against Deep Learning for Network Security—0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
A comparative evaluation of novelty detection algorithms for discrete sequences—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
A short review on Applications of Deep learning for Cyber security—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—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