Autoencoder & GRU Attack Classifier
Budget: ₹600 – ₹1,500 INR
I'm seeking the expertise of a data scientist or machine learning engineer to develop a model for classifying various types of attacks on a KDD dataset. The ideal candidate will have extensive knowledge in the use of autoencoders for feature extraction and GRU models.
Key project elements include:
* Professionals will need to implement an autoencoder for feature extraction with the core goal of reducing data dimensionality. Prior experience in working with high-dimensional data and in deploying autoencoders is necessary.
* Subsequently, the system built should be capable of classifying Denial of Service (DoS), User to Root (U2R), and Probe attacks within the KDD dataset. Good working knowledge of GRU models and the mentioned attacks is required.
* The project includes steps to ensure the accuracy and reliability of both the autoencoder and GRU model. This includes:
1. Hyperparameter Tuning: Applicant should be dexterous in practicing various optimization techniques to increase the performance of our model
2. Data Preprocessing: Applicants should have skills in effective data preprocessing methods to arguable heighten the model's reliability.
This requires broad knowledge of data science, machine learning models, particularly autoencoders and GRU models. Experience in cybersecurity or network systems would be beneficial.
Key project elements include:
* Professionals will need to implement an autoencoder for feature extraction with the core goal of reducing data dimensionality. Prior experience in working with high-dimensional data and in deploying autoencoders is necessary.
* Subsequently, the system built should be capable of classifying Denial of Service (DoS), User to Root (U2R), and Probe attacks within the KDD dataset. Good working knowledge of GRU models and the mentioned attacks is required.
* The project includes steps to ensure the accuracy and reliability of both the autoencoder and GRU model. This includes:
1. Hyperparameter Tuning: Applicant should be dexterous in practicing various optimization techniques to increase the performance of our model
2. Data Preprocessing: Applicants should have skills in effective data preprocessing methods to arguable heighten the model's reliability.
This requires broad knowledge of data science, machine learning models, particularly autoencoders and GRU models. Experience in cybersecurity or network systems would be beneficial.