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Found 10 papers, 1 papers with code
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis
However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection.
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods
In this context, we proposed a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the classifier model.
Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data
In this research, we trained and evaluated our proposed LSTM-AE model on reflection-based DDoS attacks (DNS, LDAP, and SNMP).
Generative Adversarial Networks for Malware Detection: a Survey
This paper surveys the current research and literature for the use of Generative Adversarial Networks in the malware problem space.
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset
In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification.
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes
Also, selection of right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks, especially when it come to identifying the subcategories of attacks.
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being.
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset
Then, we apply recursive feature elimination(RFE) as a wrapper feature selection method to further eliminate redundant features recursively on the reduced feature subsets.
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on.
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers
Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset.
