Please solve the following project:

Intrusion Detection Systems $($IDS$)$ with IoT Systems

Project Goals

Data Exploration: Navigate IoT traffic datasets to identify structures, patterns, and potential anomalies.

Project Target: Python on Colab; use any data set suitable from Kaggle

Data Pre$-$processing: Clean and transform raw data, ensuring quality for analysis.

Machine Learning Modeling: Apply algorithms for intrusion classification, anomaly detection, and clustering.

Visualization: Create visualizations for network flows, intrusions, and anomaly patterns.

Interpretation: Develop insights into IDS effectiveness within IoT security.

Datasets

The dataset is divided into sections focusing on specific intrusion scenarios and devices. Based on your assigned section, create an IDS model for intrusion detection and validate its performance.

Group or Individual Work

You may work individually or in groups, with each group analyzing a specific dataset portion.

Required Tasks

Data Loading: Import dataset $($e$.$g$.,$ via pd$.$read$\_$csv$).$ Handle missing values, data types, and duplicates.

EDA: Profile data, explore distributions, and identify data quality issues using line charts, scatter plots, bar plots, and heatmaps.

Data Manipulation: Group and aggregate data to calculate summary statistics.

$5\mathrm{.}1$ Supervised Machine Learning Tasks: Tabular$/$Time$-$Series$/$Image Datasets

In this section, you will explore the potential applications of supervised machine learning, specifically fo$-$ cusing on classification and$/$or regression tasks, using the provided IoT$-$based Intrusion Detection System $($IDS$)$ dataset. You will build, evaluate, and optimize machine learning models to ensure their efficiency and robustness for intrusion detection. Follow these steps to guide your project:

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$1.$ Normalize or scale features as necessary: Ensure the numerical features in the dataset are normalized or scaled $($e$.$g$.,$ Min$-$Max Scaling, Standardization$)$ to improve model performance.

$2.$ Preprocess the data: Encode any categorical variables $($if present$),$ and preprocess the dataset by scaling the data appropriately.

$3.$ Split the dataset: Divide the dataset into training and testing sets, typically using an $80/20$ or $70/30$ split, ensuring a balanced distribution of classes if performing classification.

$4.$ Train multiple models: Train at least three different classification models $($e$.$g$.,$ Decision Tree, Ran$-$ dom Forest, SVM$)$ using Python$$s Scikit$-$learn library. Optionally, you can also experiment with deep learning models using Keras or PyTorch frameworks, particularly if working with time$-$series or image data.

$5.$ Evaluate model performance: Assess the performance of each model on the testing set by calculat$-$ ing relevant metrics such as $($Accuracy$,$ Precision, Recall, F$1$ Score$).$

$6.$ Model selection: Select the best$-$performing model based on the evaluation metrics. Explain why this model performed better compared to others in your specific case.

$7.$ Visualization: Visualize the performance of the selected model using appropriate techniques $($e$.$g$.,$ confusion matrix, ROC curves, precision$-$recall curves$)$ to illustrate its effectiveness in detecting in$-$ trusions.

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$8.$ Hyperparameter tuning: Apply different hyperparameter tuning methods such as Grid Search, Ran$-$ dom Search, or Bayesian Optimization to improve your models$$ performance. Analyze the impact of tuning on your model and compare the results with non$-$tuned versions.

$9.$ Re$-$evaluate tuned models: After hyperparameter tuning, evaluate the performance of each tuned model on the testing set using the same metrics $($accuracy$,$ precision, recall, F$1$ score$)$ to determine if tuning enhanced model performance.

$10.$ Feature engineering: Experiment with creating new features or transforming existing ones to boost the performance of your models. Investigate whether feature engineering leads significant improvements in model accuracy and other metrics.

Documentation & Presentation: Prepare a detailed Jupyter Notebook covering all steps, including key insights and model comparisons. Summarize in a professional presentation highlighting major results and improvements.

Learning Outcomes

Proficiency in Python for cybersecurity tasks, practical experience with IoT datasets, and enhanced critical thinking in cybersecurity and IoT.

Conclusion