abstract review

Enhanced Botnet Detection in IoT Networks Using a Hybrid Deep Learning Model

Page 1: Title & Authors

  • Title: Enhanced Botnet Detection in IoT Networks Using a Hybrid Deep Learning Model

  • Authors: 22D21A0549, 22D21A0548, 22D21A0504

Page 2: Table of Contents

  • Abstract

  • Introduction

    • 1.1 Purpose

    • 1.2 Scope

  • System Analysis

    • 2.1 Existing System & Disadvantages

    • 2.2 Problem Statement

    • 2.3 Proposed System & Advantages

  • System Requirements Specification

    • 3.1 Software Requirements

    • 3.2 Hardware Requirements

  • Conclusion

Page 3: Introduction

Purpose

  • Develop a deep learning model for detecting botnet attacks in IoT networks.

  • Traditional security methods are inadequate due to increasing cyber threats.

  • Aim to leverage hybrid deep learning models to improve detection accuracy and response time.

Scope

  • Focus on detecting botnets in IoT networks.

  • Utilizes a combination of deep learning techniques to enhance classification accuracy.

  • Uses the UNSW-NB15 dataset for training and evaluation.

  • Aims to contribute to the fields of cybersecurity and IoT security research.

Page 4: System Analysis

Existing System & Disadvantages

  • Current techniques rely on traditional rule-based IDS, which face:

    • High false positive rates.

    • Limited adaptability to new botnet attack patterns.

    • Ineffective in managing large-scale IoT traffic data.

Problem Statement

  • Botnet attacks are becoming more complex, rendering traditional IDS ineffective.

  • A new adaptive, scalable, and efficient detection approach is essential to enhance IoT security.

Page 5: Proposed System & Advantages

  • Integrates multiple deep learning models (ANN, CNN, LSTM, RNN) for analyzing network traffic.

  • Advantages include:

    • Higher accuracy in detecting advanced botnet attacks.

    • Reduction in false positives and negatives.

    • Scalability to accommodate large datasets.

Page 6: System Requirements Specification

Software Requirements

  • Python 3.x: Main programming language for deep learning implementation.

  • TensorFlow / PyTorch: Frameworks for model training and evaluation.

  • Scikit-learn: Data preprocessing and performance evaluation library.

  • Keras: High-level API for building deep learning models.

  • NumPy: Library for numerical computations and multidimensional arrays management.

  • Pandas: Tool for data manipulation and analysis.

  • Matplotlib: Visualization library for plotting results.

  • Jupyter Notebook: Environment for writing and testing code interactively.

Page 7: Hardware Requirements

  • RAM: Minimum of 8GB, 16GB recommended for optimal performance.

  • GPU: NVIDIA RTX 3060 or higher for accelerating deep learning calculations.

  • Storage: Minimum of 500GB required for datasets, models, and logs storage.

  • Internet: High-speed connection for downloading datasets and dependencies.

Page 8: Conclusion

  • The proposed hybrid deep learning model greatly enhances botnet detection in IoT networks.

  • Integration of ANN, CNN, LSTM, and RNN increases accuracy and efficiency compared to traditional methods.

  • Research highlights the potential of AI-driven security solutions in mitigating cyber threats, encouraging advancements in automated cybersecurity defenses.