On the public citation graph

Iotpredictor: A Security Framework for Predicting Iot Device Behaviours and Detecting Malicious Devices Against Cyber Attacks

https://doi.org/10.2139/ssrn.4751263
0 signed · 29 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/0471250953.bia03as18openalex-extracted
An Introduction to Hidden Markov Models
inferredcites →https://doi.org/10.1007/s10776-020-00483-7openalex-extracted
IoT Ecosystem: A Survey on Devices, Gateways, Operating Systems, Middleware and Communication
inferredcites →https://doi.org/10.1007/s11227-021-03824-2openalex-extracted
Resource discovery in the Internet of Things integrated with fog computing using Markov learning model
inferredcites →https://doi.org/10.1016/j.asoc.2018.05.049openalex-extracted
Semi-supervised learning based distributed attack detection framework for IoT
inferredcites →https://doi.org/10.1016/j.cose.2017.08.016openalex-extracted
A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments
inferredcites →https://doi.org/10.1016/j.cosrev.2023.100549openalex-extracted
Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges
inferredcites →https://doi.org/10.1016/j.future.2020.07.020openalex-extracted
Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment
inferredcites →https://doi.org/10.1016/j.iot.2018.09.005openalex-extracted
The Internet of Things, Fog and Cloud continuum: Integration and challenges
inferredcites →https://doi.org/10.1016/j.iot.2019.100059openalex-extracted
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
inferredcites →https://doi.org/10.1016/j.iot.2023.100780openalex-extracted
Internet of Things (IoT) security dataset evolution: Challenges and future directions
inferredcites →https://doi.org/10.1038/s41586-020-2649-2openalex-extracted
Array programming with NumPy
inferredcites →https://doi.org/10.1108/09685221011079199openalex-extracted
A survey of intrusion detection and prevention systems
inferredcites →https://doi.org/10.1109/access.2019.2962829openalex-extracted
Intrusion Detection Protocols in Wireless Sensor Networks Integrated to Internet of Things Deployment: Survey and Future Challenges
inferredcites →https://doi.org/10.1109/access.2022.3144425openalex-extracted
An In-Depth Study and Improvement of Isolation Forest
inferredcites →https://doi.org/10.1109/ccnc.2019.8651782openalex-extracted
A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks
inferredcites →https://doi.org/10.1109/ccwc.2019.8666450openalex-extracted
AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning
inferredcites →https://doi.org/10.1109/icaccs.2017.8014590openalex-extracted
Support vector machine based intrusion detection system with reduced input features for advanced metering infrastructure of smart grid
inferredcites →https://doi.org/10.1109/icdm.2008.17openalex-extracted
Isolation Forest
inferredcites →https://doi.org/10.1109/jiot.2019.2958185openalex-extracted
Anomaly Detection for IoT Time-Series Data: A Survey
inferredcites →https://doi.org/10.1109/jiot.2022.3211346openalex-extracted
An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/lsp.2002.806705openalex-extracted
An efficient forward-backward algorithm for an explicit-duration hidden Markov model
inferredcites →https://doi.org/10.1109/proc.1973.9030openalex-extracted
The viterbi algorithm
inferredcites →https://doi.org/10.1109/tii.2023.3272696openalex-extracted
Cloud-Fog Automation: Vision, Enabling Technologies, and Future Research Directions
inferredcites →https://doi.org/10.1109/tnsm.2019.2933358openalex-extracted
A Survey on Big Data for Network Traffic Monitoring and Analysis
inferredcites →https://doi.org/10.1109/tnsm.2020.2966951openalex-extracted
IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge
inferredcites →https://doi.org/10.1155/2021/6699313openalex-extracted
Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression
inferredcites →https://doi.org/10.1201/9780203748039openalex-extracted
Markov Models and Optimization
inferredcites →https://doi.org/10.3390/su14148707openalex-extracted
XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems
inferredcites →https://doi.org/10.4172/jcsb.1000182openalex-extracted
Scapy A Python Tool For Security Testing
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.