Semi-supervised anomaly detection algorithms: A comparative summary and future research directions
While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …
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 …
A survey on machine learning methods for churn prediction
L Geiler, S Affeldt, M Nadif - International Journal of Data Science and …, 2022 - Springer
The diversity and specificities of today's businesses have leveraged a wide range of
prediction techniques. In particular, churn prediction is a major economic concern for many …
prediction techniques. In particular, churn prediction is a major economic concern for many …
Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing
Our world is moving fast towards the era of the Internet of Things (IoT), which connects all
kinds of devices to digital services and brings significant convenience to our lives. With the …
kinds of devices to digital services and brings significant convenience to our lives. With the …
Autonomous fault diagnosis and root cause analysis for the processing system using one-class SVM and NN permutation algorithm
In this era of Industry 4.0, there are continuing efforts to develop fault detection and
diagnosis methods that are fully autonomous; these methods are self-learning, with little or …
diagnosis methods that are fully autonomous; these methods are self-learning, with little or …
A review of research works on supervised learning algorithms for SCADA intrusion detection and classification
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in
providing remote access, monitoring and control of critical infrastructures (CIs) which …
providing remote access, monitoring and control of critical infrastructures (CIs) which …
A machine learning-based recommender system for improving students learning experiences
Outcome-based education (OBE) is a well-proven teaching strategy based upon a
predefined set of expected outcomes. The components of OBE are Program Educational …
predefined set of expected outcomes. The components of OBE are Program Educational …
A network-based positive and unlabeled learning approach for fake news detection
Fake news can rapidly spread through internet users and can deceive a large audience.
Due to those characteristics, they can have a direct impact on political and economic events …
Due to those characteristics, they can have a direct impact on political and economic events …
Target specific mining of COVID-19 scholarly articles using one-class approach
The novel coronavirus disease 2019 (COVID-19) began as an outbreak from epicentre
Wuhan, People's Republic of China in late December 2019, and till June 27, 2020 it caused …
Wuhan, People's Republic of China in late December 2019, and till June 27, 2020 it caused …
A novel OC-SVM based ensemble learning framework for attack detection in AGC loop of power systems
This paper presents a Semi-supervised Learning approach for anomaly detection in the
Automatic Generation Control loop of the power systems. The proposed technique is an …
Automatic Generation Control loop of the power systems. The proposed technique is an …