Bot-IoT Traffic Detection in Python Projects

Bot-IoT Traffic Detection in Python Projects
Abstract
The Internet of Things (IoT) has connected billions of devices worldwide, enabling smart applications across industries. However, this connectivity has also introduced severe vulnerabilities, making IoT networks prime targets for cyberattacks such as botnets. The Bot-IoT dataset, developed to simulate malicious IoT traffic, is widely used for training and testing security solutions. This project, Bot-IoT Traffic Detection in Python, focuses on detecting malicious IoT traffic using machine learning and deep learning algorithms. The system preprocesses the dataset, extracts key features, and trains classifiers such as Random Forest, SVM, Logistic Regression, and Deep Neural Networks to distinguish between benign and malicious traffic. Implemented in Python using Scikit-learn, TensorFlow/Keras, Pandas, and NumPy, the project provides a scalable and automated detection framework that can be deployed in real-time IoT environments to enhance cybersecurity.

Existing System
Traditional network intrusion detection systems (NIDS) primarily rely on signature-based techniques or conventional machine learning models. While these methods are effective for detecting known threats, they often fail to identify zero-day attacks or evolving IoT-specific threats. Moreover, existing systems struggle with the high volume, velocity, and variety of IoT network traffic, leading to scalability challenges and high false-positive rates. Some earlier ML-based approaches using small datasets lack the robustness and accuracy needed for modern large-scale IoT networks.

Proposed System

The proposed system introduces a Python-based intelligent intrusion detection framework trained on the Bot-IoT dataset to classify network traffic as benign or malicious. Data preprocessing techniques such as feature scaling, normalization, and dimensionality reduction (PCA) are applied before training. Multiple machine learning models are tested, and a deep learning approach (DNN or LSTM) is implemented for improved accuracy and detection of temporal attack patterns. The system evaluates performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Compared to existing systems, this approach is more robust, scalable, and capable of handling real-time IoT traffic, thereby improving detection rates and reducing false alarms.

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