Introduction
The financial sector faces increasing threats from sophisticated cyberattacks targeting data integrity, transaction records, and algorithmic systems. These attacks, often orchestrated by organized criminal groups like the Carbonak syndicate, exploit vulnerabilities in traditional centralized platforms that suffer from single points of failure, inconsistent data management, and prolonged settlement times—sometimes taking up to three days post-trade. Conventional stock exchange infrastructures rely on multiple intermediaries, leading to inefficiencies, limited transparency, and elevated operational risks.
To overcome these limitations, blockchain (BC) technology has emerged as a transformative solution. By leveraging decentralized consensus mechanisms, blockchain enables tamper-proof transaction ledgers, enhances auditability, and streamlines financial settlements. In particular, private Ethereum consortium blockchains (PEC-BC) offer a balanced approach—combining the security and immutability of blockchain with the controlled access and regulatory compliance required by financial institutions.
This article presents an advanced financial security system built on a smart contract-based PEC-BC framework, enhanced with a hybrid optimization strategy integrating the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA) and Adaptive Deep Temporal Context Networks (ADTCN). The system aims to improve transaction throughput, reduce latency, optimize leader block selection, and strengthen smart contract security through intelligent parameter tuning and deep learning-driven anomaly detection.
👉 Discover how next-gen blockchain systems are redefining financial security and efficiency.
Existing Works in Blockchain-Based Financial Security
Key Research Contributions
Recent advancements have laid the foundation for blockchain adoption in finance:
- Abdallah et al. (2020) proposed a decentralized stock market architecture using a consortium blockchain, demonstrating sufficient throughput for real-world trading environments like the Singapore Exchange.
- Tsoulias et al. (2020) introduced a graph-based blockchain model using Neo4j, enhancing data visualization and attack prevention through structural analysis of transaction relationships.
- Wang et al. (2022) integrated edge computing with blockchain for supply chain financing, improving data privacy and fraud monitoring while facing computational intensity challenges.
- Amponsah et al. (2022) applied blockchain to secure National Health Insurance Scheme (NHIS) transactions, reducing fraud but highlighting data management weaknesses.
- Li et al. (2023) developed Fabric-SCF, a blockchain-based secure storage system with fine-grained access control, achieving high throughput in simulated financial scenarios.
Integration of Deep Reinforcement Learning
Liu et al. (2021) introduced a deep reinforcement learning (DRL) model using the Proximal Policy Optimization (PPO) algorithm to optimize Bitcoin trading strategies. Their work demonstrates that AI-driven models can enhance decision-making speed and resource efficiency in dynamic blockchain environments—insights directly applicable to optimizing smart contract execution paths and reducing transaction costs in financial systems.
Research Gaps and Challenges
Despite progress, existing blockchain-based financial models face critical limitations:
| System Type | Key Benefits | Major Limitations |
|---|---|---|
| Consortium Networks | Reduced inefficiency, lower complexity | Long training times, weak transparency |
| Edge Computing | Real-time fraud detection | High computational load |
| Smart Contracts | Improved throughput | High implementation cost |
| Peer-to-Peer Models | High integrity, immutability | Poor privacy handling |
Common issues include poor scalability under peak loads, high consensus latency, vulnerability to spoofing, and insufficient adaptability in high-frequency trading environments. These gaps necessitate a new approach combining optimized consensus mechanisms with intelligent anomaly detection.
Blockchain-Based Financial Security: Core Concepts
Why Financial Security Needs Blockchain
Blockchain addresses core financial security challenges by:
- Eliminating single points of failure
- Ensuring data immutability and traceability
- Reducing reliance on intermediaries
- Enabling near real-time settlements
- Enhancing auditability and transparency
Ethereum-based smart contracts automate agreement enforcement without third-party oversight, significantly reducing fraud risk and operational delays.
Understanding Blockchain Architecture
A blockchain is a distributed ledger where transactions are grouped into blocks, cryptographically linked via hash pointers. Each node maintains a copy of the ledger, ensuring consensus through validation protocols. Ethereum extends this model with smart contracts—self-executing code that triggers actions when predefined conditions are met.
Types of Blockchains
- Permissionless (Public) BC: Open to all participants; high decentralization but slower consensus (e.g., Bitcoin).
- Permissioned (Private/Consortium) BC: Access restricted to authorized entities; faster transactions and better compliance (e.g., Hyperledger, PEC-BC).
Consortium blockchains strike an optimal balance for financial institutions requiring both security and regulatory control.
Private Ethereum Consortium Blockchain (PEC-BC) for Financial Security
Architecture and Advantages
PEC-BC operates within a closed network of pre-approved nodes—typically banks, brokers, or clearinghouses. This structure enables:
- Faster consensus due to fewer validators
- Enhanced privacy through restricted data access
- Higher transaction throughput
- Reduced risk of malicious attacks
Each member institution accesses the network via secure gateways, with permissions managed centrally by the consortium. This hybrid model supports scalable, compliant, and secure financial operations.
Addressing Network Centralization Risks
While consortium models improve efficiency, they risk centralization. To mitigate this:
- Consensus algorithms must be resilient to collusion
- Leader node selection should be dynamic and unpredictable
- Network monitoring tools must detect anomalies in real time
👉 See how hybrid blockchain models are solving scalability without sacrificing security.
Smart Contract Logic in Financial Systems
Smart contracts on Ethereum are written in Solidity and executed by miners across the network. In a PEC-BC environment:
- Contracts govern trade execution, settlement, and compliance checks
- Execution outcomes are validated by all participating nodes
- State changes are recorded immutably on the ledger
These contracts can be restricted to subset members for sensitive operations, preserving confidentiality while maintaining audit trails.
Proposed Financial Security Framework
The proposed system integrates three core components:
- Data Collection & Storage: Customer financial data is secured on-chain using PEC-BC.
- Leader Block Selection: Optimized via DB-BOA to minimize computation time, communication cost, and memory usage.
- Smart Contract Execution: Powered by ADTCN with hyperparameters tuned by DB-BOA for maximum accuracy and minimal false positives.
This architecture ensures robust performance even under high transaction volumes.
Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA)
Algorithm Design
DB-BOA fuses two metaheuristic approaches:
- Dynamic Butterfly Optimization Algorithm (DBOA): Excels in global exploration
- Billiards Optimization Algorithm (BOA): Strong in local exploitation
The hybrid model dynamically switches between them based on fitness ratios:
$$ \text{If } rand < \left( \frac{\text{bestfit}}{\text{worstfit}} \right), \text{ use DBOA; else use BOA} $$
This prevents premature convergence and improves solution quality.
Leader Block Selection Mechanism
Leader blocks are chosen based on reputation scores and optimized using DB-BOA to minimize:
$$ \text{Obf1} = \mathop{\arg \min}\limits_{\{Lb^{bc}\}} [CT + CC + MS] $$
Where:
- $CT$: Computation Time
- $CC$: Communication Cost
- $MS$: Memory Size
By optimizing these parameters, the system achieves faster consensus and higher throughput.
Adaptive Deep Temporal Context Networks (ADTCN) for Security Enhancement
How ADTCN Improves Smart Contract Security
Traditional vulnerability detection methods struggle with complex patterns and lack labeled data. ADTCN overcomes these issues by:
- Analyzing temporal sequences in transaction data
- Detecting anomalies using multi-scale attention mechanisms
- Adapting to evolving threat patterns
The model comprises:
- Multi-modal Joint Embedding (MJE): Processes heterogeneous financial data
- Temporal Context Learning (TCL): Captures long-term dependencies
- Multiple Time-scale Temporal Attention (MTTA): Identifies short-term fluctuations
Objective Function for Model Optimization
$$ \text{Obf2} = \mathop{\arg \max}\limits_{\{Hn^D, Ep^D, Se^D\}} \left[ (Acc + Pre + NPV + MCC) + \frac{1}{FPR} \right] $$
Where:
- $Hn^D$: Hidden neuron count
- $Ep^D$: Epoch count
- $Se^D$: Steps per epoch
- $Acc$: Accuracy
- $Pre$: Precision
- $NPV$: Negative Predictive Value
- $MCC$: Matthews Correlation Coefficient
- $FPR$: False Positive Rate
DB-BOA tunes these hyperparameters to maximize detection performance.
Results and Discussion
Performance Metrics Overview
The system was tested against benchmarks including MBO, WSA, DBOA, BOA, LGP, and GP using MATLAB 2020a.
| Metric | DB-BOA-ADTCN | Best Competitor Gain |
|---|---|---|
| Accuracy | 96% | +6.7% over EfficientNet |
| Throughput | 385 tx/sec | +85% over BOA-ADTCN |
| Block Propagation Delay | Reduced by 68% | vs LGP (α=0.1) |
| Computation Time | 13.003 sec | 29% faster than BOA-ADTCN |
Key Findings
- ROC Analysis: DB-BOA-ADTCN outperformed DTCN by 29% at 0.2 FPR.
- Scalability: Maintained low latency (<50ms) with up to 350 validators.
- Robustness: Sustained >94% accuracy under noise coefficients up to 25.
- Attack Detection: Achieved 98.3% accuracy in attack scenarios.
These results confirm superior performance in high-frequency trading environments.
Frequently Asked Questions (FAQ)
Q: What makes PEC-BC more suitable for finance than public blockchains?
A: PEC-BC offers faster transaction speeds, better regulatory compliance, and controlled access—critical for financial institutions needing privacy and auditability.
Q: How does DB-BOA improve upon traditional optimization algorithms?
A: By combining global exploration (DBOA) with local exploitation (BOA), DB-BOA avoids local optima and converges faster to high-quality solutions.
Q: Can ADTCN detect zero-day smart contract vulnerabilities?
A: Yes—its temporal context learning enables detection of novel attack patterns even with limited labeled data.
Q: Is this system compatible with existing financial infrastructure?
A: Yes—it's designed as a modular layer that integrates with current clearing and settlement systems via APIs.
Q: How does the model handle sudden spikes in transaction volume?
A: Through adaptive resource allocation and dynamic leader selection, maintaining sub-second latency during peak loads.
Conclusion
This study presents a novel financial security framework combining private Ethereum consortium blockchain, DB-BOA-based leader selection, and ADTCN-powered smart contract validation. The hybrid optimization strategy significantly improves transaction speed, reduces latency, and enhances security compared to existing models.
Experimental results demonstrate:
- 96% accuracy in fraud detection
- 85% higher throughput than baseline models
- Sub-second finality under heavy load
The system provides a scalable, secure foundation for next-generation financial platforms—from stock exchanges to cross-border payments.
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