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Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

Dimitrios Papamartzivanos, Felix Gomez Marmol, Georgios Kambourakis • IEEE Access (2019)

The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2019.2893871

Can machine learning be secure?

Marco Barreno, Blaine A. Nelson, Russell Sears, Anthony D. Joseph et al. • ACM Asia Conference on Computer and Communications Security (ASIA CCS) (2006)

Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a targe...

OPENALEX DOI: https://doi.org/10.1145/1128817.1128824

Machine learning and its applications: A review

Sheena Angra, Sachin Ahuja • International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017)

Nowadays, large amount of data is available everywhere. Therefore, it is very important to analyze this data in order to extract some useful information and to develop an algorithm based on this analysis. This can be achieved through data m...

OPENALEX DOI: https://doi.org/10.1109/icbdaci.2017.8070809

Exploring Traffic Patterns Through Network Programmability: Introducing SDNFLow, a Comprehensive OpenFlow-Based Statistics Dataset for Attack Detection

Jorge Buzzio-Garcia, Jaime Vergara, Santiago Rios-Guiral, Christian Garzon et al. • IEEE Access (2024)

In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3378271

Machine Learning

Yagang Zhang • InTech eBooks (2010)

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large...

OPENALEX Open Access DOI: https://doi.org/10.5772/217

Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks

Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee • Discover Internet of Things (2025)

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits pra...

DOAJ Open Access (DOAJ) DOI: 10.1007/s43926-025-00100-0

Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches

T. Anitha Kumari, Sanket Mishra • Egyptian Informatics Journal (2024)

The integration of sensors in the monitoring of essential bodily measurements, air quality, and energy consumption in buildings demonstrates the importance of the Internet of Things (IoT) in everyday life. These security breaches are caused...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.eij.2024.100520

A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems

Tala Talaei Khoei, Naima Kaabouch • Information (2023)

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and ...

DOAJ Open Access (DOAJ) DOI: 10.3390/info14020103