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Ravi Prakash Pandey's Projects

democode icon democode

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

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

threadlimitor icon threadlimitor

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

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