Modelling of hybrid deep learning framework with recursive feature elimination for distributed denial of service attack detection systems
Revolutionizing DDoS Attack Detection with Responsible AI
This study introduces a responsible AI-based hybridisation framework (RAIHFAD-RFE) for DDoS attack detection. Leveraging Z-score standardisation for data pre-processing, Recursive Feature Elimination (RFE) for feature selection, and a hybrid LSTM-BiGRU model for classification, the system aims for high accuracy and efficiency. An improved Orca Predation Algorithm (IOPA) optimizes hyperparameters. Experimental results on CIC-IDS-2017 and Edge-IIoT datasets demonstrate superior accuracy (99.35% and 99.39% respectively) compared to existing models, highlighting its potential for robust cybersecurity.
Key Enterprise Impact Metrics
RAIHFAD-RFE delivers significant improvements in cybersecurity, offering unparalleled accuracy and efficiency in detecting complex DDoS threats.
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Deep Analysis & Enterprise Applications
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AI in Cybersecurity: An Overview
Artificial Intelligence is transforming cybersecurity by enabling more proactive and sophisticated threat detection. In the context of Distributed Denial of Service (DDoS) attacks, AI-driven solutions offer significant advantages over traditional methods, particularly in identifying complex, evolving attack patterns and managing large volumes of network traffic. AI can analyze vast datasets to pinpoint anomalies, predict future threats, and automate response mechanisms, thereby strengthening an enterprise's digital defenses.
The continuous learning capabilities of AI systems allow them to adapt to new attack vectors, making them indispensable in the ever-escalating cyber warfare landscape. For enterprises, this means reduced vulnerability, faster incident response times, and ultimately, greater operational resilience against disruptive cyberattacks.
Current Challenges in DDoS Detection
Detecting Distributed Denial of Service (DDoS) attacks presents several challenges for enterprises. The sheer volume and variety of attack methods make it difficult for traditional rule-based systems to keep up. Attackers constantly evolve their techniques, often mimicking legitimate traffic to evade detection. Furthermore, the need for real-time analysis, coupled with the computational complexity of deep learning models, poses a significant hurdle.
Existing solutions often struggle with high false positive rates, which can disrupt legitimate services, or high false negative rates, leading to successful attacks. Balancing detection accuracy with computational efficiency and scalability is crucial for effective DDoS defense, especially in large-scale enterprise and IoT environments. The proposed RAIHFAD-RFE framework aims to address these limitations.
The RAIHFAD-RFE Approach
The Responsible AI-based Hybridisation Framework for Attack Detection with Recursive Feature Elimination (RAIHFAD-RFE) is a multi-stage approach designed for enhanced DDoS detection. It begins with Z-score standardization to preprocess raw network data, ensuring consistency and reducing bias. This is followed by Recursive Feature Elimination (RFE), an iterative feature selection technique that identifies and retains only the most crucial features, significantly improving model performance and reducing complexity.
For classification, RAIHFAD-RFE employs a hybrid Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) model. This powerful combination allows for the effective capture of temporal patterns and contextual dependencies in sequential network traffic data. Finally, the Improved Orca Predation Algorithm (IOPA) is used for hyperparameter tuning, optimizing the model's parameters for peak accuracy and robust performance across diverse scenarios.
Enterprise Process Flow: RAIHFAD-RFE Workflow
Raw Data Input
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Z-Score Standardization
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Recursive Feature Elimination (RFE)
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Feature Selection
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LSTM-BiGRU Classification
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IOPA Hyperparameter Tuning
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DDoS Attack Detection
99.39%
Accuracy on Edge-IIoT Dataset
| Method | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) | Inference Latency (ms) |
|---|---|---|---|---|---|
| Shallow ANN | 93.36 | 93.73 | 87.11 | 96.16 | 17.96 |
| Isolated LSTM | 98.27 | 93.72 | 88.93 | 89.31 | 19.53 |
| CNN Classifier | 96.90 | 93.15 | 79.47 | 95.61 | 10.68 |
| RF Method | 82.51 | 90.31 | 88.04 | 92.22 | 22.91 |
| SVM Model | 79.23 | 88.07 | 85.79 | 96.24 | 22.52 |
| DNN Algorithm | 96.38 | 91.85 | 79.60 | 93.40 | 10.21 |
| Inception Time | 96.60 | 80.81 | 89.26 | 94.69 | 22.08 |
| RAIHFAD-RFE (Proposed) | 99.39 | 96.37 | 96.37 | 96.37 | 7.63 |
Enhanced DDoS Attack Detection in IIoT
The RAIHFAD-RFE model significantly boosts detection rates in industrial IoT environments, crucial for protecting critical infrastructure. Its ability to process complex temporal data and adapt to evolving threats makes it highly effective against advanced DDoS attacks, ensuring operational continuity and data integrity. The system's high accuracy (99.39% on Edge-IIoT) directly translates to reduced downtime and financial losses for enterprises.
Key ROI: Reduced downtime, improved threat response, enhanced operational security.
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Annual Savings
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Enterprise AI Implementation Timeline
A structured approach to integrating RAIHFAD-RFE into your enterprise cybersecurity strategy for optimal results.
Discovery & Data Assessment
Initial consultation to understand current cybersecurity infrastructure, data sources (network traffic logs, system events), and specific DDoS attack patterns. Assess data quality and identify key features for model training.
RAIHFAD-RFE Model Adaptation
Tailor the Z-score standardization and RFE algorithms to the enterprise's unique network data. Fine-tune the LSTM-BiGRU architecture and IOPA parameters based on initial data analysis to optimize for specific traffic characteristics and attack vectors.
Integration & Training
Integrate the RAIHFAD-RFE framework into existing security information and event management (SIEM) systems or network monitoring tools. Train the model using historical and real-time enterprise network traffic data, leveraging powerful GPU infrastructure for efficient processing.
Validation & Deployment
Rigorous validation of the model's accuracy, precision, recall, and F1-score using unseen data and simulated attack scenarios. Deploy the validated model in a controlled environment for real-time DDoS detection, with continuous monitoring and feedback loops.
Continuous Optimization & Scalability
Establish a continuous learning pipeline for the model to adapt to new and evolving DDoS attack types. Implement scalability solutions to handle increasing network traffic volumes and integrate with enterprise-wide security orchestration for automated response.
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