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Found 13 papers, 1 papers with code
Measuring Technological Convergence in Encryption Technologies with Proximity Indices: A Text Mining and Bibliometric Analysis using OpenAlex
Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation.
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.
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.
A Game-Theoretic Approach for AI-based Botnet Attack Defence
The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection.
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection
In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection.
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold
To address this, we propose a new novel feature extraction method based on covariance matrices that extract spatial-temporal characteristics of network traffic data for detecting malicious network traffic behavior.
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware
Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the training dataset, resulting in high false-positive rates.
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.
