Machine learning for anomaly detection in iot networks: Malware analysis on the iot-23 data set

NA Stoian - 2020 - essay.utwente.nl
The Internet of Things is one of the newer developments in the domain of the Internet. It is
defined as a network of connected devices and sensors, both physical and digital, that …

A review and analysis of the bot-iot dataset

JM Peterson, JL Leevy… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Machine learning is rapidly changing the cybersecu-rity landscape. The use of predictive
models to detect malicious activity and identify inscrutable attack patterns is providing levels …

Artificial intelligence and machine learning for ensuring security in smart cities

S Ahmed, MF Hossain, MS Kaiser, MBT Noor… - Data-Driven Mining …, 2021 - Springer
The smart city emerged as a model with the rapid growth of robust information and
communication technology and the development of ubiquitous sensing technology. A smart …

Malicious mining code detection based on ensemble learning in cloud computing environment

S Li, Y Li, W Han, X Du, M Guizani, Z Tian - Simulation Modelling Practice …, 2021 - Elsevier
Hackers increasingly tend to abuse and nefariously use cloud services by injecting
malicious mining code. This malicious code can be spread through infrastructures in the …

Internet of Things botnet detection approaches: Analysis and recommendations for future research

M Wazzan, D Algazzawi, O Bamasaq, A Albeshri… - Applied Sciences, 2021 - mdpi.com
Internet of Things (IoT) is promising technology that brings tremendous benefits if used
optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the …

Containerized cloud-based honeypot deception for tracking attackers

VSD Priya, SS Chakkaravarthy - Scientific Reports, 2023 - nature.com
Discovering malicious packets amid a cloud of normal activity, whether you use an IDS or
gather and analyze machine and device log files on company infrastructure, may be …

XGB-RF: A hybrid machine learning approach for IoT intrusion detection

JA Faysal, ST Mostafa, JS Tamanna, KM Mumenin… - Telecom, 2022 - mdpi.com
In the past few years, Internet of Things (IoT) devices have evolved faster and the use of
these devices is exceedingly increasing to make our daily activities easier than ever …

Secure and efficient data storage and sharing scheme for blockchain‐based mobile‐edge computing

L Zhang, M Peng, W Wang, Z Jin, Y Su… - Transactions on …, 2021 - Wiley Online Library
With the rapid development of Internet of Things (IoT) technology, IoT devices have been
widely used to collect physiological health data and provide diversified services to the …

P2IDF: A privacy-preserving based intrusion detection framework for software defined Internet of Things-fog (SDIoT-Fog)

P Kumar, R Tripathi, G P. Gupta - Adjunct Proceedings of the 2021 …, 2021 - dl.acm.org
The Software Defined Internet of Things-Fog (SDIoT-Fog) has provided a new connectivity
paradigm for effective service provisioning. SDIoT-Fog uses network resource virtualization …

An investigation and comparison of machine learning approaches for intrusion detection in IoMT network

A Binbusayyis, H Alaskar, T Vaiyapuri… - The Journal of …, 2022 - Springer
Abstract Internet of Medical Things (IoMT) is network of interconnected medical devices
(smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and …