Feature analysis for machine learning-based IoT intrusion detection
Internet of Things (IoT) networks have become an increasingly attractive target of
cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to …
cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to …
[HTML][HTML] Feature extraction for machine learning-based intrusion detection in IoT networks
A large number of network security breaches in IoT networks have demonstrated the
unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network …
unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network …
Intrusion detection system for large-scale IoT NetFlow networks using machine learning with modified Arithmetic Optimization Algorithm
With the rapid expansion of Internet of Things (IoT) networks, the need for robust security
measures to detect and report potential threats is becoming more urgent. In this paper, we …
measures to detect and report potential threats is becoming more urgent. In this paper, we …
[HTML][HTML] Attack-Aware IoT network traffic routing leveraging ensemble learning
Q Abu Al-Haija, A Al-Badawi - Sensors, 2021 - mdpi.com
Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against
various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs …
various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs …
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 …
A lightweight supervised intrusion detection mechanism for IoT networks
S Roy, J Li, BJ Choi, Y Bai - Future Generation Computer Systems, 2022 - Elsevier
Abstract As the Internet of Things (IoT) is becoming increasingly popular, we have
experienced more security breaches that are associated with the connection of vulnerable …
experienced more security breaches that are associated with the connection of vulnerable …
DEMISe: Interpretable deep extraction and mutual information selection techniques for IoT intrusion detection
LR Parker, PD Yoo, TA Asyhari, L Chermak… - Proceedings of the 14th …, 2019 - dl.acm.org
Recent studies have proposed that traditional security technology--involving pattern-
matching algorithms that check predefined pattern sets of intrusion signatures--should be …
matching algorithms that check predefined pattern sets of intrusion signatures--should be …
Deep learning in IoT intrusion detection
Abstract The Internet of Things (IoT) is the new paradigm of our times, where smart devices
and sensors from across the globe are interconnected in a global grid, and distributed …
and sensors from across the globe are interconnected in a global grid, and distributed …
Feco: Boosting intrusion detection capability in iot networks via contrastive learning
Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range
of applications. However, a lack of sufficient security features in IoT devices renders IoT …
of applications. However, a lack of sufficient security features in IoT devices renders IoT …
[HTML][HTML] A deep learning methodology for predicting cybersecurity attacks on the internet of things
OA Alkhudaydi, M Krichen, AD Alghamdi - Information, 2023 - mdpi.com
With the increasing severity and frequency of cyberattacks, the rapid expansion of smart
objects intensifies cybersecurity threats. The vast communication traffic data between …
objects intensifies cybersecurity threats. The vast communication traffic data between …