Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
Intrusion detection systems: A state-of-the-art taxonomy and survey
M Alkasassbeh, S Al-Haj Baddar - Arabian Journal for Science and …, 2023 - Springer
Abstract Intrusion Detection Systems (IDSs) have become essential to the sound operations
of networks. These systems have the potential to identify and report deviations from normal …
of networks. These systems have the potential to identify and report deviations from normal …
Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches
A Mughaid, S AlZu'bi, A Alnajjar, E AbuElsoud… - Multimedia Tools and …, 2023 - Springer
Abstract Non Orthogonal Multiple Access (NOMA) successfully drew attention to the
deployment of 5th Generation (5G) wireless communication systems, and it is now …
deployment of 5th Generation (5G) wireless communication systems, and it is now …
Polymorphic Adversarial DDoS attack on IDS using GAN
R Chauhan, SS Heydari - 2020 International Symposium on …, 2020 - ieeexplore.ieee.org
Intrusion Detection systems are important tools in preventing malicious traffic from
penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly …
penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly …
Machine learning based anomaly detection for 5g networks
J Lam, R Abbas - arXiv preprint arXiv:2003.03474, 2020 - arxiv.org
Protecting the networks of tomorrow is set to be a challenging domain due to increasing
cyber security threats and widening attack surfaces created by the Internet of Things (IoT) …
cyber security threats and widening attack surfaces created by the Internet of Things (IoT) …
Machine-learning techniques for detecting attacks in SDN
MS Elsayed, NA Le-Khac, S Dev… - 2019 IEEE 7th …, 2019 - ieeexplore.ieee.org
With the advent of Software Defined Networks (SDNs), there has been a rapid advancement
in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However …
in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However …
Detecting abnormal traffic in large-scale networks
MS Elsayed, NA Le-Khac, S Dev… - 2020 International …, 2020 - ieeexplore.ieee.org
With the rapid technological advancements, organizations need to rapidly scale up their
information technology (IT) infrastructure viz. hardware, software, and services, at a low cost …
information technology (IT) infrastructure viz. hardware, software, and services, at a low cost …
Investigation of dual-flow deep learning models LSTM-FCN and GRU-FCN efficiency against single-flow CNN models for the host-based intrusion and malware …
D Čeponis, N Goranin - Applied Sciences, 2020 - mdpi.com
Intrusion and malware detection tasks on a host level are a critical part of the overall
information security infrastructure of a modern enterprise. While classical host-based …
information security infrastructure of a modern enterprise. While classical host-based …
Reconstruction probability-based anomaly detection using variational auto-encoders
T Iqbal, S Qureshi - International Journal of Computers and …, 2023 - Taylor & Francis
Anomaly detection is a method of categorizing unexpected data points or events in a
dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a …
dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a …
Exploration of Machine Learning Algorithms for Development of Intelligent Intrusion Detection Systems
For anomaly-based intrusion detection systems, software implementation of several string-
matching algorithms using deep/machine learning approaches are frequently available. The …
matching algorithms using deep/machine learning approaches are frequently available. The …