Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

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Product Code: Matlab - Web Application
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Abstract:

           A typical Internet of Things (IoT) deployment includes a wide pervasive network of (smart) Internet-connected devices, Internet-connected vehicles, embedded systems, sensors and other devices/systems that autonomously sense, store, transfer and process collected data. Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes. These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. IoT (Internet of Things) and IoBT (Internet of Battlefield Things) malware detection approach based on class-wise selection of OpCodes sequence as a feature for classification task.

          A graph of selected features was created for each sample and a deep Eigen space learning approach was used for malware classification. In this proposed system to evaluate our approach against larger and broader datasets and implementing a prototype of the proposed approach in an IoT and IoBT system for evaluation and refinement. If the transmitter sends any information to the receiver in between if any attacks like man in the middle attack occurs. Then the admin will monitor the actions performed by the server. These actions will get updated to the admin whenever any attacks occur.


Proposed System:

          In this proposed system to evaluate our approach against larger and broader datasets and implementing a prototype of the proposed approach in an IoT and IoBT system for evaluation and refinement. If the transmitter sends any information to the receiver in between if any attacks like man in the middle attack occurs. Then the admin will monitor the actions performed by the server. These actions will get updated to the admin whenever any attacks occur.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.