Pinned Repositories
tamil-songs-corpus
There are near to 3500 Tamil songs, from 1004 movies. tamil_songs_corpus.csv consists of an row of movie JSON objects. Also Scraping script Notebook was attached.
Dinogy
Moway
Virtual self driving car using reinforcement learning
Nano-Processor-Design
This 4-bit Nano processor, which can execute 4 instructions. It can add and subtract integers. It includes 4 bit add/subtract unit, 3-bit adder, 3-bit program counter, register bank, program ROM multiplexers, and an instruction decoder. The output can be visualized in a 7-segment.
DDoS-testing-server
APIs are exposed to the public or internal network interfaces, thus they are vulnerable to various security threats. Hackers can attack such APIs to steal sensitive data or to disrupt the services provided by APIs to the intended users. Therefore, API-based attack detection is important to identify and prevent fraudulent access to APIs. Since Machine learning (ML) and Artificial Intelligence (AI) have shown great potential in detecting abnormal patterns, AI is a useful tool in detecting attacks to the APIs. However, using AI/ML requires accurate data to learn the fraudulence patterns and to validate the developed solutions, which is a major challenge faced by data scientists and researchers. To address this challenge, we proposed an approach that learns to detect attacks using the generated data by attacking the APIs. Therefore, the solution will consist of two models for 1) attack detection, 2) attack generation. Assume if we want to detect DDOS attacks, the attack simulation model will try to simulate the DDOS attack without being detected by the attack detection model. If the attack is undetected and leads to the unavailability of the API, we can assign a penalty to attack detection model, and reward to the attacking model. We can allow both models to compete with each other similar to adversarial learning to achieve highly accurate attack detection models. This blogs [1] explains how adversarial learning is used to prevent attacks to the image recognition models. The goal of this project is to deliver an attack simulation and detection tool by improving adversarial learning approaches to simulate and detect API-based attacks.
ddos-master
remocolab
remocolab is a Python module to allow remote access to Google Colaboratory using SSH or TurboVNC.
chatapp_flask_socketIO
This is a sample Python chat app for test the Flask and Socket IO
Distributed-Content-Sharing-Application
Distributed content sharing application Distributed Systems (CS4262) - Group Project Goal Develop a simple overlay-based solution that allows a set of nodes to share contents (e.g., music files) among each other. Consider a set of nodes connected via some overlay topology. Each of the nodes has a set of files that it is willing to share with other nodes. Suppose aliveNode x is interested in a file f. x issues a search query to the overlay to locate a at least one aliveNode y containing that particular file. Once the aliveNode is identified, the file f can be exchanged between X and y.
FallingApples
sajeevan16's Repositories
sajeevan16/DDoS-testing-server
APIs are exposed to the public or internal network interfaces, thus they are vulnerable to various security threats. Hackers can attack such APIs to steal sensitive data or to disrupt the services provided by APIs to the intended users. Therefore, API-based attack detection is important to identify and prevent fraudulent access to APIs. Since Machine learning (ML) and Artificial Intelligence (AI) have shown great potential in detecting abnormal patterns, AI is a useful tool in detecting attacks to the APIs. However, using AI/ML requires accurate data to learn the fraudulence patterns and to validate the developed solutions, which is a major challenge faced by data scientists and researchers. To address this challenge, we proposed an approach that learns to detect attacks using the generated data by attacking the APIs. Therefore, the solution will consist of two models for 1) attack detection, 2) attack generation. Assume if we want to detect DDOS attacks, the attack simulation model will try to simulate the DDOS attack without being detected by the attack detection model. If the attack is undetected and leads to the unavailability of the API, we can assign a penalty to attack detection model, and reward to the attacking model. We can allow both models to compete with each other similar to adversarial learning to achieve highly accurate attack detection models. This blogs [1] explains how adversarial learning is used to prevent attacks to the image recognition models. The goal of this project is to deliver an attack simulation and detection tool by improving adversarial learning approaches to simulate and detect API-based attacks.
sajeevan16/tamil-songs-corpus
There are near to 3500 Tamil songs, from 1004 movies. tamil_songs_corpus.csv consists of an row of movie JSON objects. Also Scraping script Notebook was attached.
sajeevan16/ddos-master
sajeevan16/remocolab
remocolab is a Python module to allow remote access to Google Colaboratory using SSH or TurboVNC.
sajeevan16/chatapp_flask_socketIO
This is a sample Python chat app for test the Flask and Socket IO
sajeevan16/Dinogy
sajeevan16/Distributed-Content-Sharing-Application
Distributed content sharing application Distributed Systems (CS4262) - Group Project Goal Develop a simple overlay-based solution that allows a set of nodes to share contents (e.g., music files) among each other. Consider a set of nodes connected via some overlay topology. Each of the nodes has a set of files that it is willing to share with other nodes. Suppose aliveNode x is interested in a file f. x issues a search query to the overlay to locate a at least one aliveNode y containing that particular file. Once the aliveNode is identified, the file f can be exchanged between X and y.
sajeevan16/FallingApples
sajeevan16/grommet
a react-based framework that provides accessibility, modularity, responsiveness, and theming in a tidy package
sajeevan16/h2database
H2 is an embeddable RDBMS written in Java.
sajeevan16/Moway
Virtual self driving car using reinforcement learning
sajeevan16/Nano-Processor-Design
This 4-bit Nano processor, which can execute 4 instructions. It can add and subtract integers. It includes 4 bit add/subtract unit, 3-bit adder, 3-bit program counter, register bank, program ROM multiplexers, and an instruction decoder. The output can be visualized in a 7-segment.
sajeevan16/openmrs-contrib-android-client
Android client for OpenMRS
sajeevan16/openmrs-core
OpenMRS API and web application code
sajeevan16/playList
Project of Video PlayList
sajeevan16/Python-Thunder
A curated list of Python applications
sajeevan16/Random-Team-generator
sajeevan16/Ride-Fare-Classification
sajeevan16/sajeevan16
sajeevan16/sajeevan16.github.io
sajeevan16/simple-chess
A chess game made with TypeScript
sajeevan16/Time-Series-and-Stochastic-Processes
The stochastic process is a model for the analysis of time series.
sajeevan16/time-to-leave
Log work hours and get notified when it's time to leave the office and start to live.
sajeevan16/TrafficLight-Detector
Simple traffic light detector by opencv python
sajeevan16/TreasureLoot
This was a multi-thread game to enhance the learning of object-oriented design and programming concepts.
sajeevan16/worldify
WORLDIFY - Get spotify geographically based content