Adbench: Anomaly detection benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
A novel graph convolutional feature based convolutional neural network for stock trend prediction
W Chen, M Jiang, WG Zhang, Z Chen - Information Sciences, 2021 - Elsevier
Stock trend prediction is one of the most widely investigated and challenging problems for
investors and researchers. Since the convolutional neural network (CNN) was introduced to …
investors and researchers. Since the convolutional neural network (CNN) was introduced to …
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …
distribution. Existing unsupervised approaches often suffer from high computational cost …
A survey on malware detection with graph representation learning
T Bilot, N El Madhoun, K Al Agha, A Zouaoui - ACM Computing Surveys, 2024 - dl.acm.org
Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …
complexity of malware. Traditional detection methods based on signatures and heuristics …
Adgym: Design choices for deep anomaly detection
Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …
across various fields such as finance, medical services, and cloud computing. However …
Graph neural networks for intrusion detection: A survey
T Bilot, N El Madhoun, K Al Agha, A Zouaoui - IEEE Access, 2023 - ieeexplore.ieee.org
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
Deep learning techniques to detect cybersecurity attacks: a systematic mapping study
D Torre, F Mesadieu, A Chennamaneni - Empirical Software Engineering, 2023 - Springer
Context Recent years have seen a lot of attention into Deep Learning (DL) techniques used
to detect cybersecurity attacks. DL techniques can swiftly analyze massive datasets, and …
to detect cybersecurity attacks. DL techniques can swiftly analyze massive datasets, and …
Botnet detection approach using graph-based machine learning
A Alharbi, K Alsubhi - Ieee Access, 2021 - ieeexplore.ieee.org
Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML)
techniques have been widely used for botnet detection with flow-based features. The prime …
techniques have been widely used for botnet detection with flow-based features. The prime …
Curvature graph neural network
Graph neural networks (GNNs) have achieved great success in many graph-based tasks.
Much work is dedicated to empowering GNNs with adaptive locality ability, which enables …
Much work is dedicated to empowering GNNs with adaptive locality ability, which enables …
[HTML][HTML] GANBOT: A GAN-based framework for social bot detection
Nowadays, a massive number of people are involved in various social media. This fact
enables organizations and institutions to more easily access their audiences across the …
enables organizations and institutions to more easily access their audiences across the …