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A hybrid encryption system combining DNA-based shift protection and AES to secure data in edge–cloud environments. It transforms user data into DNA sequences and amino acids before encryption, adding an extra biological-inspired layer of security. Designed for high confidentiality, integrity, and performance in distributed computing systems.

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🔐 Hybrid Encryption using DNA-Based Shift Protection and AES for Edge-Cloud System Security

This repository implements HEADS (Hybrid Encryption with AES and DNA-based Shift Protection) — a next-generation encryption algorithm designed to enhance data security across edge–cloud systems. By combining the biological principles of DNA encoding with the Advanced Encryption Standard (AES), the model provides multi-layered data protection, mitigating modern cyber threats in distributed computing environments.


🧠 Overview

As modern infrastructures like smart cities, IoT networks, and connected homes rely on edge–cloud communication, data traverses multiple nodes — increasing exposure to attacks. The HEADS algorithm introduces an additional layer of protection by incorporating DNA-based data transformation and shift-protected amino acid encoding before applying AES encryption.

This ensures:

  • Stronger resistance to cryptanalysis and replay attacks
  • Minimal increase in computation time
  • High confidentiality and integrity in distributed systems

🧩 Core Concepts

Concept Description
DNA-based Encoding Converts binary data into DNA bases (A, C, G, T) and then into amino acid codons.
Shift Protection Applies a user-defined or system-generated shift key (Ksp) to rearrange amino acid sequences, introducing data randomness.
AES Encryption Performs symmetric encryption on the transformed DNA-based data for strong confidentiality.
Hybrid Key Generation Uses password-based DNA transformation combined with dynamic key shifting for session-level key renewal.

⚙️ Algorithm Workflow

🧬 Encryption Process

  1. Convert plaintext to ASCII → binary
  2. Map binary to DNA bases (A=00, C=01, G=10, T=11)
  3. Group DNA bases into codons and translate to amino acids
  4. Apply shift protection using Ksp
  5. Encrypt amino acid sequence using AES
  6. Transmit securely between edge and cloud nodes

🔓 Decryption Process

  1. Decrypt AES ciphertext using password-based key
  2. Remove shift protection using Ksp
  3. Convert amino acids → DNA codons → binary → ASCII
  4. Reconstruct original plaintext

🧪 Example

Input:

Plain text = "Hello World"

After Encryption:

Cipher Text = v3bUVeJRyJbifPdlW7j2jCs2itrxChEmUjH6J4bx2132OwZy7JHxRaGgStibaV2

After Decryption:

Plain text = "Hello World"

📈 Performance Analysis

Metric AES Blowfish HEADS
Throughput (Kb/sec) 158 200 164
Encryption Time Moderate Fastest Moderate
Security Strength High Medium Very High
Resistance to Replay/Cryptanalysis Limited Limited Strong

The HEADS algorithm achieves superior security with only minimal performance trade-offs, making it suitable for edge-cloud systems, where latency and resource constraints are critical.


🧰 Technologies Used

  • Java 8 – Core language for algorithm implementation
  • Apache Commons Math – Statistical computation and analysis
  • CloudSim Plus 6.1.1 – Cloud and Edge simulation environment
  • Amazon Top Cell Phones Dataset (Kaggle) – Dataset 1
  • Vehicle Number Plate Dataset (Kaggle) – Dataset 2

🧱 System Architecture

+-------------+         +-------------+         +-------------+
|   End User  | <---->  |  Edge Node  | <---->  |  Cloud Server |
| (Encrypts)  |         | (Processes) |         | (Stores Data) |
+-------------+         +-------------+         +-------------+

Data Flow:
Plaintext → DNA Encoding → Shift Protection → AES Encryption → Cloud Storage

🛡️ Security Highlights

Attack Type HEADS Defense Mechanism
Man-in-the-Middle Dual-layer encryption prevents useful data interception.
Dictionary Attack DNA and shift-based encoding obscure password patterns.
Replay Attack Session-specific AES keys invalidate reused packets.
Cryptanalysis DNA randomization and codon shifting make pattern analysis infeasible.

🧩 Key Generation Process

User Password → ASCII → Binary → DNA Base Conversion
↓
Random Shift Key (Ksp) applied
↓
Resulting DNA key used for AES encryption

Each encryption session generates a unique key, ensuring forward secrecy and minimizing reuse vulnerabilities.


🚀 Simulation Environment

Parameter Value
Simulator CloudSim Plus 6.1.1
Nodes 5 Edge Nodes, 2 Cloud Data Centers
Bandwidth 500 Mbps
Storage 10 GB (Cloud), 3 GB (Edge)
Processor Intel i5-8250U
OS Windows 10
RAM 8 GB

📊 Results

  • The HEADS algorithm outperforms AES-only encryption in terms of security resilience while maintaining similar performance.
  • Demonstrates resistance to replay and dictionary attacks.
  • Proves feasibility of DNA-inspired cryptography for real-world edge-cloud systems.

🧾 Authors

Chandan Cherukuri, Sambit Kumar Mishra, Pavuluri Venkata Dheeraj, Deepak Puthal

  • Department of Computer Science and Engineering, SRM University AP, India
  • Department of EECS, Khalifa University, Abu Dhabi, UAE 📧 {chandan.cherukuri, dheeraj.krishnamohan}@srmap.edu.in, deepak.puthal@ku.ac.ae

🧠 Future Enhancements

  • Implement mutual authentication between edge and cloud nodes
  • Expand to multi-user asymmetric cryptography
  • Integrate machine learning for adaptive trust and anomaly detection
  • Support for IoT-scale deployments

🧾 License

This project is released under the MIT License. You are free to use, modify, and distribute it for academic and research purposes with proper citation.


📚 Reference

IEEE Xplore: Sambit Kumar Mishra, Chandan Cherukuri, Pavuluri Venkata Dheeraj, Deepak Puthal (2023). “A Hybrid Encryption Approach using DNA-Based Shift Protected Algorithm and AES for Edge-Cloud System Security,” Proceedings of the 21st OITS International Conference on Information Technology (OCIT 2023).


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A hybrid encryption system combining DNA-based shift protection and AES to secure data in edge–cloud environments. It transforms user data into DNA sequences and amino acids before encryption, adding an extra biological-inspired layer of security. Designed for high confidentiality, integrity, and performance in distributed computing systems.

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