Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
[HTML][HTML] Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects
The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …
(ML) have affected several research fields, leading to improvements that could not have …
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
An effective intrusion detection approach using SVM with naïve Bayes feature embedding
J Gu, S Lu - Computers & Security, 2021 - Elsevier
Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …
detection system plays a critical role in protecting it. Various machine learning techniques …
[HTML][HTML] From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry …
Abstract The Internet of Things (IoT), Big Data and Machine Learning (ML) may represent
the foundations for implementing the concept of intelligent production, smart products …
the foundations for implementing the concept of intelligent production, smart products …
[HTML][HTML] An explainable deep learning-enabled intrusion detection framework in IoT networks
M Keshk, N Koroniotis, N Pham, N Moustafa… - Information …, 2023 - Elsevier
Although the field of eXplainable Artificial Intelligence (XAI) has a significant interest these
days, its implementation within cyber security applications still needs further investigation to …
days, its implementation within cyber security applications still needs further investigation to …
Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems
Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are
vulnerable to security and safety issues that threaten the lives of the people. Unlike manual …
vulnerable to security and safety issues that threaten the lives of the people. Unlike manual …
A novel intrusion detection method based on lightweight neural network for internet of things
The purpose of a network intrusion detection (NID) is to detect intrusions in the network,
which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently …
which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently …
Supervised feature selection techniques in network intrusion detection: A critical review
Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …