Pinned Repositories
active_task2
Hetrogenious IoT connectivity through LoRa gateway nad NB-IoT BS
ARIMA
Estimating Bitcoin price based on the bitcoin price history
cellular-traffic-analysis
Analysis of real cellular traffic captured on a device
DoF-Simulator
Simulator of a K-user interference channel aided by instantaneous relays
FraudDetection
FraudDetection
Grant-free-IoT
Simulator for Grant-free IoT
Machine-Learning-for-IoT
The use of on-device machine learning for IoT in 5G
ML4RRM
Simulator for investigating application of machine learning in serving urgent URLLC
NB-IoT
A simulator for representing behavior of NB-IoT in providing wide area connectivity. For the first time, this simulator shows the adverse impact of extreme coverage on nearby devices.
Rel-GFA
Reliability analysis of large-scale GF IoT Networks
AminAzari's Repositories
AminAzari/cellular-traffic-analysis
Analysis of real cellular traffic captured on a device
AminAzari/Machine-Learning-for-IoT
The use of on-device machine learning for IoT in 5G
AminAzari/ML4RRM
Simulator for investigating application of machine learning in serving urgent URLLC
AminAzari/DoF-Simulator
Simulator of a K-user interference channel aided by instantaneous relays
AminAzari/Grant-free-IoT
Simulator for Grant-free IoT
AminAzari/active_task2
Hetrogenious IoT connectivity through LoRa gateway nad NB-IoT BS
AminAzari/ARIMA
Estimating Bitcoin price based on the bitcoin price history
AminAzari/FraudDetection
FraudDetection
AminAzari/NB-IoT
A simulator for representing behavior of NB-IoT in providing wide area connectivity. For the first time, this simulator shows the adverse impact of extreme coverage on nearby devices.
AminAzari/Rel-GFA
Reliability analysis of large-scale GF IoT Networks
AminAzari/aminazari.github.io
AminAzari/Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
AminAzari/dash30
AminAzari/ELIoT
Emulated IoT platform for testing purposes.
AminAzari/example-analysis
Illustrative analysis examples
AminAzari/Examples
Classification Examples
AminAzari/FakeAI
Use AI with care!
AminAzari/FindNumPaths
AminAzari/FreeCoAP
An implementation of a CoAP client, a CoAP server and a HTTP/CoAP proxy with full DTLS support.
AminAzari/prime_number
AminAzari/quicperf
A QUIC protocol traffic generator based on quic-go
AminAzari/UM1
AminAzari/web-traffic-generator
A quick and dirty HTTP/S "organic" traffic generator.