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 701–725 of 800 papers

TitleStatusHype
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns—0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments—0
Intrusion Detection Using Mouse DynamicsCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
Flow-based Network Traffic Generation using Generative Adversarial Networks—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
Sequence Covering for Efficient Host-Based Intrusion DetectionCode0
Enhanced network anomaly detection based on deep neural networks—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Active Learning for Wireless IoT Intrusion Detection—0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
V-CNN: When Convolutional Neural Network encounters Data Visualization—0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks—0
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—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