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calculator_ios_ui_practice

democode

The demo source code for our Swift, iOS, and macOS tutorials

detecting-ddos-in-software-defined-network-using-machine-learning

Software-Defined Networking has been proven advantageous in terms of performance, manageability, and scaling for data centers. Still, when it comes to security, there is a loophole in the design of this new paradigm, which is its centralized structure. SDNcontroller is vulnerable to mainly Distributed Denial of Service (DDoS) attacks. If the controller becomes the victim of DDoS attack, it can become a severe problem to the whole network as it keeps entertaining the false requests coming from the attacker. Asa result, the controller becomes overloaded and will not be able to respond to genuine requests. Also, it will produce so many false flows that will be filled in the SDN switches, and as a result, the switches will be overloaded. Thus detection of DDoS attack becomes important for the security of Software Defined Networking. In this project, DistributedDenial of Service (DDoS) attacks have been detected using machine learning models. The datasets have been created using the sFlow-rt tool both for the normal traffic and the attack traffic, and then using these datasets, various machine learning models have been trained. The datasets were trained and tested with Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) classifiers. From the results, we can conclude that machine learning methods can show better results in the detection of DDoS attack.

easydinnerclone

mixer-starter

paytm-customuisdk-react-native

Paytm-customuisdk integration with react native

ravi

requestlimitor

scikit-learn

scikit-learn: machine learning in Python

threadlimitor

This is demonstration of semaphore in swift/ios, where we can limit the number of request for an instance

wavelet-based-modified-fuzzy-local-information-c-means-with-minimized-energy-function

This project presents a modification to a fuzzy based segmentation algorithm proposed by Krindis and Chatzis known as fuzzy local information c-means (FLICM) which has a variation of fuzzy c-means (FCM) algorithm. In project work, we have proposed a modification to energy function of FLICM.