Train Without Label: A Self-supervised One-Class Classification Approach for IoT Anomaly Detection
The intrusion detection techniques remain essential for network security, especially for the
Internet of Things (IoT) environment, where there are crucial network systems and …
Internet of Things (IoT) environment, where there are crucial network systems and …
A Semi-supervised Deep Auto-encoder Based Intrusion Detection for IoT.
The main problem facing the Internet of Things (IoT) today is the identification of attacks due
to the constrained nature of IoT devices. To address this problem, we present a lightweight …
to the constrained nature of IoT devices. To address this problem, we present a lightweight …
Deep Learning Based Binary and Multi-class Classification Comparison for Anomaly Detection
Security remains one of the biggest challenges in the IoT field. This is why several machine
learning and deep learning techniques are used to set up models capable of monitoring …
learning and deep learning techniques are used to set up models capable of monitoring …
An ensemble of activation functions in autoencoder applied to IoT anomaly detection
We propose an ensemble of activation functions for learning the latent representation of
AutoEncoder (AE) to improve the accuracy of Internet of Things (IoT) botnet (anomaly) …
AutoEncoder (AE) to improve the accuracy of Internet of Things (IoT) botnet (anomaly) …
Network intrusion detection for IoT security based on learning techniques
Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn
cyberattack exposed the critical fault-lines among smart networks. Security of IoT has …
cyberattack exposed the critical fault-lines among smart networks. Security of IoT has …
Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks
The intrusion detection system (IDS) is a promising technology for ensuring security against
cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and …
cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and …
Unsupervised machine learning for network-centric anomaly detection in IoT
R Bhatia, S Benno, J Esteban, TV Lakshman… - Proceedings of the 3rd …, 2019 - dl.acm.org
Industry 4.0 holds the promise of greater automation and productivity but also introduces
new security risks to critical industrial control systems from unsecured devices and …
new security risks to critical industrial control systems from unsecured devices and …
IoT botnet anomaly detection using unsupervised deep learning
The Internet of Things has become a cutting-edge technology that is continuously evolving
in size, connectivity, and applicability. This ecosystem makes its presence felt in every …
in size, connectivity, and applicability. This ecosystem makes its presence felt in every …
An intrusion detection system for the internet of things based on the ensemble of unsupervised techniques
Y Wang, G Sun, X Cao, J Yang - … Communications and Mobile …, 2022 - Wiley Online Library
Recently, machine learning techniques, especially supervised learning techniques, have
been adopted in the Intrusion Detection System (IDS). Due to the limit of supervised …
been adopted in the Intrusion Detection System (IDS). Due to the limit of supervised …
FS3: Few-Shot and Self-Supervised Framework for Efficient Intrusion Detection in Internet of Things Networks
D Ayesha S, S AB - Proceedings of the 39th Annual Computer Security …, 2023 - dl.acm.org
Securing the Internet of Things is critical for its successful deployment in various industries.
While Machine Learning techniques have shown promise for intrusion detection in the …
While Machine Learning techniques have shown promise for intrusion detection in the …