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shannastrk's Projects

aes icon aes

Implementation of Rijndael cipher algorithm

aes-illustrated icon aes-illustrated

An implementation of the Advanced Encryption Standard (AES) algorithm meant for study to go along with "A Stick Figure Guide to the Advanced Encryption Standard (AES)" blog post at www.moserware.com

encryption icon encryption

This code is a demonstration of secure communication between IOT devices. This establish a secure connection between your sensor, gateway and the server.

iot-aes-128 icon iot-aes-128

Pure AES-128 implementation for IoT Module (Tested in Particle Photon)

iot-cyber-security-with-machine-learning icon iot-cyber-security-with-machine-learning

IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.

iot-cyber-security-with-machine-learning-research-project icon iot-cyber-security-with-machine-learning-research-project

IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.

otp-jwt icon otp-jwt

One time password (email, SMS) authentication support for HTTP APIs.

pds icon pds

Machine Learning for Engineers

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