💻 computer science

On-Chain Anchoring-Based Information Security on Iot for Secure Smart-City Using Pekeccak and Gsbdostlru

This paper proposes a secure Smart City framework that prevents gateway-level log falsification by integrating PEKeccak-based on-chain anchoring with advanced cryptographic and machine learning techniques, achieving a 98.91% integrity verification success rate.

Original authors: Pullaiah Chowdary Vutla, Basker Ganesan, Triveni Yenugu

Published 2026-11-12

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CC BY 4.0

Original authors: Pullaiah Chowdary Vutla, Basker Ganesan, Triveni Yenugu

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the sprawling digital nervous system of a modern smart city, countless small devices work tirelessly to keep traffic flowing, lights bright, and public spaces safe. These Internet of Things sensors collect a constant stream of data, but this vast network faces a persistent vulnerability: the gateways that gather and transmit this information can be tricked. If a bad actor alters the logs at these gateways, the entire city's decision-making process can be compromised, leading to chaos or danger. To solve this, researchers are turning to a combination of two powerful concepts. First, blockchain acts as an unchangeable public ledger, a digital record that, once written, cannot be erased or altered without everyone noticing. Second, advanced cryptography provides the mathematical locks that keep the data itself secret and secure, even against future threats from quantum computers. The challenge has been to make these heavy security tools light enough to run on the small, energy-efficient devices that power our cities, while also ensuring that the system can spot new, unseen attacks before they cause harm.

A team of researchers has proposed a new framework designed to secure this delicate ecosystem by anchoring data directly to the blockchain while simultaneously checking for intruders. Their approach begins at the very source, where sensors collect raw information. Instead of sending this data in plain sight, the system immediately wraps it in a protective layer using a specialized form of encryption that is resistant to future quantum computing attacks. This ensures that even if the data is intercepted, it remains unreadable. The researchers also introduced a method for splitting the secret keys needed to unlock this data. Rather than keeping the key in one place where it could be stolen, the system breaks it into pieces and stores them in different locations. This way, an attacker would need to find all the scattered pieces to unlock the information, a task that is nearly impossible if the pieces are managed correctly.

Once the data is secured and the keys are safely distributed, the system moves to the edge of the network, where local computers process the information. Here, the researchers deployed a sophisticated security guard known as an intrusion detection system. Unlike older systems that only recognize known threats, this new guard uses a learning model trained to spot unusual patterns in network traffic. It was specifically designed to learn from new types of attacks as they appear, rather than just waiting for a list of known bad behaviors. To make this learning process faster and more accurate, the system was fine-tuned using an optimization strategy inspired by the hunting and escaping behaviors of the secretary bird, a large wading bird known for its keen vision and agility. This allowed the system to quickly adapt to new threats without getting stuck in old ways of thinking.

When the data passes the security checks, it is prepared for its final journey to the blockchain. Before being recorded, the system creates a unique digital fingerprint of the data. To make this process fast enough for a busy city, the researchers used a technique that analyzes the order and flow of the data to create a lightweight version of the fingerprint. This fingerprint is then permanently anchored to the blockchain, creating an immutable record that proves the data existed at a specific time and has not been tampered with. The study found that this method successfully verified the integrity of the data in nearly 99 percent of cases, a significant improvement over previous methods. Furthermore, the system detected intrusions with an accuracy of over 99 percent, correctly identifying a wide range of attacks, from simple password guessing to complex distributed denial-of-service attempts.

The results suggest that this layered approach offers a robust defense for the future of smart cities. By combining quantum-resistant encryption, split-key storage, and an adaptive learning system, the researchers created a framework that is both secure and efficient. The system managed to authenticate devices and secure data with a speed that makes it practical for real-world use, with data processing times measured in just a few seconds. The authors note that while their model is designed to handle the complex environment of a smart city, future work will focus on detecting even more coordinated attacks that might target the digital infrastructure simultaneously. For now, this research provides a clear path toward a city where the data driving our daily lives is protected from tampering and hidden from prying eyes.

Technical Summary: On-Chain Anchoring-Based Information Security on IoT for Secure Smart-City Using PEKeccak and GSBDOSTLRU

1. Problem Statement and Research Gap

The paper addresses critical information security challenges within the Internet of Things (IoT) infrastructure of Smart Cities (SC). While IoT devices underpin applications like traffic management and public health, their large-scale deployment introduces vulnerabilities that compromise critical decision-making. The authors identify specific limitations in prevailing works:

Gateway-Level Log Falsification: A primary research gap identified is the lack of solutions addressing the alteration, deletion, or fabrication of sensor logs at the gateway level, which leads to security loss in SC IoT environments.
Quantum Vulnerability: Conventional systems often rely on long-term keys susceptible to decryption by quantum adversaries.
Authentication Deficiencies: Existing SC models frequently lack multi-layer authentication, allowing the injection of fake sensor values.
Intrusion Detection Limitations: Prevailing methods fail to generalize against new threats in dynamic environments, resulting in inaccurate intrusion detection, and existing IoT nodes often struggle with heavy cryptographic learning loads.

2. Proposed Methodology

The authors propose a comprehensive framework integrating Permutation Entropy-Keccak (PEKeccak) for on-chain anchoring and Gated Secretary Bird Dawson Optimization Suish Transfer Learning Recurrent Unit (GSBDOSTLRU) for intrusion detection. The system operates across four layers:

2.1 System Initialization and Signature Creation

The framework initializes sensors ( $K$ ), edge nodes ( $P$ ), gateways ( $D$ ), and the Blockchain ( $M$ ).

PRDSA: A Polynomial Rolling Digital Signature Algorithm (PRDSA) is employed to generate signatures for authentication. Unlike standard DSA, PRDSA utilizes a Polynomial Rolling (PR) function with large prime moduli to create the hash function input ( $\delta$ ), preventing hash hacking.
Signature Storage: The generated signature ( $\mathfrak{A}$ ) is stored on the Blockchain for subsequent authentication.

2.2 Perception Layer (Data Sensing and Security)

FFPQC: Raw data ( $H$ ) is sensed and secured using Feistel Function-based Post-Quantum Cryptography (FFPQC).
Key Generation: To mitigate risks associated with random secret keys, a Feistel Function (FF) is used for the creation of the secret key ( $\epsilon$ ), which is referred to as a "quantum key" in the paper, by varying quantum states through repeated substitution and permutation.
Encryption: The data is encrypted using the generated key and photon polarization factors to prevent eavesdropping.

2.3 Edge Layer (Authentication, IDS, and Storage)

Device Authentication: The edge node authenticates the sensor device using the PRDSA signature stored on the Blockchain.
Intrusion Detection System (IDS):
- Data Pre-processing: Network data is imputed (mean of neighbors), normalized (min-max), and balanced using SMOTE (Synthetic Minority Over-sampling Technique).
- GSBDOSTLRU Model: The core IDS utilizes a Gated Recurrent Unit (GRU) enhanced with:
  - Secretary Bird Optimization (SBO): Tunes hyperparameters using hunting (exploration) and escaping (exploitation) strategies.
  - Dawson Function (DF): Integrated into SBO to avoid local minima and enable quasi-oscillatory behavior.
  - Suish Activation: Replaces standard activations to ensure strong gradient flow.
  - Transfer Learning (TL): Applied to learn new attack patterns and improve generalization.
- Output: The system classifies traffic as normal or attacks (e.g., DDoS, XSS, Ransomware). If an attack is detected, transmission is blocked.
Split-Key Storage:
- STISS: The secret key is split using Shamir's Trigonometric Integral Secret Sharing (STISS). This enhances Shamir's Secret Sharing (SSS) by integrating a Trigonometric Integral ( $\lambda$ ) to integrate the share freshness using pseudo-random transformation masks.
- Off-Chain Storage: Split keys and encrypted data are stored in the InterPlanetary File System (IPFS), generating a Content Identifier (CID).

2.4 Gateway Layer (On-Chain Anchoring)

Edge Authentication: The gateway authenticates the edge node via PRDSA.
PEKeccak Anchoring:
- Permutation Entropy (PE): Used to estimate the internal state of the Keccak hash function, reducing memory requirements.
- Lightweight Masking: A mask ( $Z$ ) is generated based on PE states to transform the input CID.
- Hashing: The Keccak sponge function generates a hash ( $V$ ) which is anchored to the Merkle root of the Blockchain.
- Performance Note: The proposed PEKeccak method achieves an anchoring latency of 1253 ms, which is significantly lower than the 3821 ms reported for the standard Keccak model in the experimental results, confirming the efficiency gains claimed by the authors.

2.5 Blockchain and Application Layer

Blockchain: The gateway is authenticated, and the hash ( $V$ ) is immutably stored in the Blockchain's Merkle root, ensuring tamper-evidence and traceability.
Application Layer: Users request data access. The system verifies the Merkle proof, reconstructs the split keys using STISS to retrieve the secret key, and decrypts the data from IPFS.

3. Key Contributions

The paper claims the following specific contributions:

PEKeccak-based Anchoring: A lightweight on-chain anchoring mechanism designed specifically to prevent gateway-level log falsification, demonstrating reduced latency compared to standard Keccak.
FFPQC and STISS: Implementation of Feistel Function-based Post-Quantum Cryptography for data security and Trigonometric Integral-based split-key storage to enhance confidentiality and quantum robustness.
PRDSA Multi-Layer Authentication: Utilization of the Polynomial Rolling Digital Signature Algorithm for authentication across sensors, edge, gateway, and Blockchain layers.
GSBDOSTLRU Intrusion Detection: A novel classifier combining GRU with Secretary Bird Optimization, Dawson Function, Suish activation, and Transfer Learning to detect intrusions and adapt to new patterns.

4. Results and Performance Evaluation

The proposed framework was evaluated using the NF-TON-IOT-V2 dataset (balanced via SMOTE) and implemented in Python.

On-Chain Anchoring (PEKeccak):
- Integrity Verification Success Rate (IVSR): 98.91% (vs. 96.21% for standard Keccak).
- Collision Resistance Level (CRL): 99.01%.
- Anchoring Latency: 1253 ms (significantly lower than Keccak's 3821 ms and SHA's 9841 ms).
Intrusion Detection (GSBDOSTLRU):
- Accuracy: 99.31% (vs. ~91.05% for conventional GRU/LSTM/RNN).
- Precision: 99.19%.
- Recall: 99.23%.
- F-Measure: 99.15%.
Authentication (PRDSA):
- Signature Creation Time (SCT): 2784 ms (vs. 4928 ms for DSA).
- Signature Verification Time (SVT): 2191 ms (vs. 4178 ms for DSA).
Data Security (FFPQC):
- Security Level: 98.47% with an attack level of only 1.27%.
- Encryption/Decryption Time: 2294 ms / 2110 ms (faster than PQC, ECC, AES, and DES).
Key Storage (STISS):
- Entropy: 251 bits (vs. 214 bits for SSS).
- Quantum Robustness: 98.21%.

5. Significance and Scope

The authors assert that this research establishes a robust information security framework for Smart City IoT environments. The significance lies in:

Addressing the Log Falsification Gap: By anchoring data at the gateway level using PEKeccak, the system specifically targets the vulnerability of gateway log manipulation.
Quantum Resilience: The integration of FFPQC and STISS provides a defense against future quantum adversaries.
Adaptive Security: The inclusion of Transfer Learning in the IDS allows the system to generalize against new, dynamic threats that traditional models miss.
Efficiency: The use of lightweight algorithms (PE for state estimation, PRDSA for signatures) ensures the framework is viable for resource-constrained IoT nodes while maintaining high security standards, as evidenced by the reduced anchoring latency (1253 ms vs. 3821 ms) compared to standard Keccak.

The paper concludes that the proposed framework successfully secures the IoT Smart City environment through a multi-layered approach involving secure initialization, quantum-resistant encryption, adaptive intrusion detection, and immutable on-chain anchoring. Future work is suggested to focus on detecting synchronized attacks on digital infrastructure.