Enterprise AI Analysis
A Lightweight Sequential AI Framework for Real Time Intrusion Detection in Dynamic Vehicular Networks
This paper proposes a novel Sequential AI-Powered Lightweight Intrusion Detection System (Seq-AIIDS) for real-time intrusion detection in dynamic vehicular networks (VANETs). The system enhances accuracy with minimal time consumption by using data acquisition, scrubbing, feature selection via Rosenthal correlation, and a liquid neural network with multipoint spiral search optimization for classification. Experimental results demonstrate superior performance in accuracy (98%), precision (96.57%), recall (98%), F1-score (97%), specificity (98.4%), and lower computational latency (29ms) compared to existing deep learning models.
Quantifiable Impact
Key performance indicators from the research highlight the significant advantages for enterprise integration.
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Deep Analysis & Enterprise Applications
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Data Scrubbing Efficiency
The Seq-AIIDS efficiently cleans datasets by handling missing values with multivariate linear regression and removing outliers using the deviate statistical test. This significantly reduces data size and improves model performance.
41KB
Data Reduction (from 1137KB to 1096KB)
Intrusion Detection Flow
The system follows a sequential process from data acquisition to optimized classification.
Enterprise Process Flow
Data Acquisition
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Data Scrubbing
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Feature Selection (Rosenthal Correlation)
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Liquid Neural Network Classification
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Multipoint Spiral Search Optimization
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Real-time Intrusion Detection
Performance Benchmarking
Seq-AIIDS consistently outperforms traditional deep learning models across key metrics.
| Metric | Seq-AIIDS | LSTM [1] | CNN [2] | GNN [3] | CNN-GRU [11] |
|---|---|---|---|---|---|
| Accuracy (%) | 98.13 | 91.33 | 92.74 | 94.7 | 96.98 |
| Precision (%) | 96.57 | 85.47 | 87.57 | 90.66 | 95.33 |
| Recall (%) | 98.07 | 89.37 | 89.37 | 92.47 | 94.73 |
| F1 score (%) | 97.31 | 86.35 | 88.46 | 91.55 | 94.99 |
| Specificity (%) | 98.41 | 91.91 | 93.63 | 95.25 | 97.31 |
| Computational Latency (ms) | 29.06 | 37.98 | 34.55 | 32.03 | 30.12 |
VANET Security Enhancement
The core application of Seq-AIIDS is to fortify Vehicular Ad-Hoc Networks (VANETs) against real-time intrusions, crucial for Intelligent Transportation Systems (ITS).
The Challenge
VANETs face significant security threats due to their dynamic and open nature, leading to issues like blackhole, Sybil, and DoS attacks. Traditional IDS struggle with real-time detection, high node mobility, and dynamic topology changes.
The Solution
Seq-AIIDS leverages a sequential AI-driven framework with a Liquid Neural Network, optimized by a multipoint spiral search algorithm. It employs Rosenthal correlation for efficient feature selection and robust data scrubbing, ensuring accurate and low-latency detection of malicious activities.
The Outcome
Improved real-time intrusion detection accuracy by up to 13% over previous models, with a 24% reduction in computational latency. This leads to enhanced road safety, traffic efficiency, and overall reliability of VANET communication, providing a more secure foundation for ITS.
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Estimated Annual Savings
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Your AI Implementation Roadmap
A typical phased approach to integrate advanced AI solutions into your enterprise operations.
Phase 1: Discovery & Strategy (2-4 Weeks)
In-depth analysis of current systems, identifying key opportunities for AI integration, and developing a tailored strategy with clear objectives and success metrics.
Phase 2: Pilot & Proof-of-Concept (4-8 Weeks)
Developing and testing a small-scale AI pilot to validate the proposed solution, gather initial performance data, and refine the approach based on real-world feedback.
Phase 3: Development & Integration (8-16 Weeks)
Full-scale development of the AI solution, seamless integration with existing enterprise infrastructure, and rigorous testing to ensure robustness and scalability.
Phase 4: Deployment & Optimization (Ongoing)
Go-live of the AI system, continuous monitoring of performance, iterative optimization based on operational data, and ongoing support to ensure sustained value and evolution.
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