Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Projects in Detection and Identification of Attacks in the Internet of Things using Deep Learning

projects-in-detection-and-identification-of-attacks-in-the-internet-of-things-using-deep-learning.jpg

Python Projects in Detection and Identification of Attacks in the Internet of Things using Deep Learning for Masters and PhD

    Project background:
    The Detection and Identification of Attacks in the Internet of Things (IoT) using Deep Learning addresses the escalating concerns surrounding the security of IoT ecosystems. With the proliferation of interconnected devices in various domains, from smart homes to industrial settings, the vulnerability to cyber-attacks has become a critical challenge. Traditional security measures often fail to identify and mitigate sophisticated attacks on IoT networks. This work aims to leverage the capabilities of deep learning to enhance the detection and identification of potential threats and attacks within IoT environments. By analyzing patterns and anomalies in data generated by IoT devices, deep learning algorithms can adapt and learn to recognize unusual behavior indicative of malicious activities. Through integrating deep learning techniques, the project aspires to contribute to developing more robust and adaptive security mechanisms for the evolving landscape of the IoT.

    Problem Statement

  • The Detection and Identification of Attacks in the IoT addresses the critical problem of ensuring the security and integrity of IoT ecosystems.
  • As interconnected devices grow, the vulnerability to cyber-attacks within IoT networks becomes increasingly pronounced.
  • Traditional security mechanisms often struggle to keep pace with the evolving tactics of malicious actors, leaving IoT systems susceptible to various attacks such as malware, unauthorized access, and data breaches.
  • The challenge lies in developing a robust and adaptive defense mechanism capable of effectively detecting and identifying these diverse and dynamic threats.
  • Deep Learning provides a promising avenue for addressing this problem, as its ability to analyze intricate patterns and anomalies in large datasets aligns well with IoT device complex and diverse data.

    • Aim and Objectives

  • Enhance the security of IoT ecosystems by implementing deep learning techniques for detecting and identifying cyber-attacks.
  • Develop deep learning models capable of analyzing patterns and anomalies in IoT data to identify potential security threats.
  • Implement adaptive algorithms to recognize evolving attack vectors and tactics within IoT networks.
  • Enhance the accuracy and efficiency of attack detection, minimizing false positives and negatives.
  • Provide real-time monitoring and response mechanisms to mitigate identified security threats swiftly.

    • Contributions to Detection and Identification of Attacks in the Internet of Things Using Deep Learning

    1. Contributes to the field by employing deep learning techniques to achieve advanced and adaptive detection of diverse cyber-attacks within IoT environments.
    2. Enhances the ability to recognize anomalies and irregular patterns in the vast and complex datasets generated by IoT devices, enabling the identification of potential security threats.
    3. Implementing adaptive algorithms, the project addresses the challenge of evolving attack vectors within IoT networks, providing a proactive defense against emerging cyber threats.
    4. Aim to significantly improve the accuracy of attack detection significantly, minimizing false positives and negatives, thereby enhancing the reliability of the security system.
    5. Contributes to real-time monitoring capabilities, allowing for swift identification and response to security threats as they emerge, reducing the impact of potential attacks.
    6. Contributing to the development of more robust and adaptive security measures tailored for the unique challenges presented by the dynamic and interconnected nature of IoT ecosystems.

    Deep Learning Algorithms for Detection and Identification of Attacks in the Internet of Things

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Long Short-Term Memory (LSTM) Networks
  • Generative Adversarial Networks (GAN)
  • Restricted Boltzmann Machines (RBM)
  • Deep Belief Networks (DBN)
  • Stacked Autoencoders
  • Capsule Networks
  • Variational Autoencoders (VAE)
  • Radial Basis Function Networks (RBFN)
  • Deep Residual Networks (ResNets)
  • Densely Connected Networks (DenseNets)

    • Datasets for Detection and Identification of Attacks in the Internet of Things Using Deep Learning

  • CIC-IDS-2017 Dataset
  • AWID (Aegean Wi-Fi Intrusion Dataset)
  • Stratosphere IPS Dataset
  • USTC-TK2016 IoT Botnet Traffic Dataset
  • DARPA IoT Dataset
  • UNSW-NB15 (Network-Based 15) Dataset
  • ISCX-Bot-2014 Dataset
  • WuHan University IoT Malware Dataset
  • Friday IoT Botnet Traffic Dataset
  • Nikhef Dataset
  • CSE-CIC-IDS2018 Dataset

    • Performance Metrics

  • Accuracy
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
  • Area Under the Precision-Recall Curve (AUC-PR)
  • Receiver Operating Characteristic (ROC) Curve
  • Precision-Recall (PR) Curve
  • Kappa Statistic
  • False Positive Rate (FPR)
  • False Negative Rate (FNR)

    • Software Tools and Technologies

    Operating System:  Ubuntu 18.04 LTS 64bit / Windows 10
    Development Tools:   Anaconda3, Spyder 5.0, Jupyter Notebook
    Language Version: Python 3.9
    Python Libraries:
    1.Python ML Libraries:

  • Scikit-Learn
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
  • Docker
  • MLflow
    • 2.Deep Learning Frameworks:
  • Keras
  • TensorFlow
  • PyTorch

Related Papers