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 331–340 of 800 papers

TitleStatusHype
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—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