Enterprise AI Analysis
Network attack knowledge inference with graph convolutional networks and convolutional 2D KG embeddings
This paper presents KGConvE, a novel method for network attack knowledge inference. It combines graph convolutional neural networks (GCN) for attack classification and convolutional 2D knowledge graph embeddings (ConvE) for reasoning about attack relationships. The method constructs a cybersecurity knowledge graph from CVE, CWE, CAPEC, and APT data, then classifies attacks using GCN, and infers implicit relationships within attack categories using ConvE. Experimental results show significant performance improvements (MRR of 0.68, Hits@10 of 0.58) over baselines in tasks like CVE-CVE, CVE-CWE, and CVE-CAPEC relationship inference, validating its effectiveness in proactive defense.
Executive Impact & Key Metrics
KGConvE delivers tangible advancements in cybersecurity intelligence, providing critical insights for proactive defense strategies and enhanced threat analysis.
MRR (Mean Reciprocal Rank)
Signifies the average of reciprocal ranks of correctly predicted attack relationships, outperforming baseline methods and demonstrating high reasoning accuracy.
Hits@10
Indicates the proportion of correct predictions found within the top 10 results, showcasing the model's superior performance in identifying relevant attack knowledge.
Attack Classification Accuracy (GCN)
Represents the model's high accuracy in classifying CVEs into APT organizations, crucial for targeted threat analysis.
Inferred Relationships
Number of new, implicit CVE-CAPEC relationships accurately inferred for CVE-2017-0144, demonstrating the model's ability to uncover hidden attack patterns.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Flowchart
Key Findings (MRR, Hits@10)
GCN Classification Performance
ConvE Inference Results (CVE-CAPEC)
The KGConvE model integrates GCN for attack classification and ConvE for knowledge graph completion to infer complex attack relationships.
Enterprise Process Flow
Data Collection & Organization
→
Entity & Relationship Extraction
→
Cybersecurity Knowledge Graph Construction
→
GCN Attack Knowledge Classification
→
ConvE Attack Knowledge Inference
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Proactive Defense & Situational Awareness
KGConvE significantly outperforms baseline methods in network attack knowledge inference, achieving a MRR of 0.68 and Hits@10 of 0.58, demonstrating its superior ability to predict implicit relationships between CVEs, CWEs, and CAPECs.
MRR (Mean Reciprocal Rank) for KGConvE
KGConvE significantly outperforms baseline methods in network attack knowledge inference, achieving a MRR of 0.68 and Hits@10 of 0.58, demonstrating its superior ability to predict implicit relationships between CVEs, CWEs, and CAPECs.
The GCN model achieves high accuracy (0.995), precision (0.999), recall (0.993), and F1 score (0.996) in classifying CVEs into APT organizations, especially on larger datasets.
| Model | Accuracy (800 samples) | Precision (800 samples) | Recall (800 samples) | F1 Score (800 samples) |
|---|---|---|---|---|
| KGConvE (GCN component) | 0.90 | 0.80 | 0.85 | 0.88 |
| BGCRKAN | 0.82 | 0.70 | 0.72 | 0.78 |
| GKAN | 0.80 | 0.65 | 0.68 | 0.73 |
| CNN-LSTM | 0.78 | 0.60 | 0.65 | 0.68 |
| EMKNN | 0.75 | 0.55 | 0.60 | 0.63 |
| KGCN | 0.70 | 0.58 | 0.50 | 0.53 |
| MLP | 0.67 | 0.45 | 0.42 | 0.39 |
KGConvE's GCN component consistently demonstrates superior performance across all metrics, validating its effectiveness in capturing APT attack features and associations for classification.
The ConvE model successfully infers crucial implicit relationships, such as those between CVE-2017-0144 and specific CAPECs (e.g., CAPEC-586, CAPEC-147, CAPEC-492, CAPEC-227) with high probability (>0.8), validating its ability to uncover hidden attack patterns.
CVE-2017-0144 Attack Pattern Inference
Context: A specific threat intelligence case involving CVE-2017-0144 was used to validate the inference results. The APT-C-06 organization exploited this vulnerability and employed various attack patterns.
Inferred Relationships:
- CVE-2017-0144 related to CAPEC-586 (Exploiting Software Vulnerabilities)
- CVE-2017-0144 related to CAPEC-147 (Bypass Security Measures)
- CVE-2017-0144 related to CAPEC-492 (Escalate Privileges)
- CVE-2017-0144 related to CAPEC-227 (Persistence Attacks)
Validation: The inferred relationships align perfectly with the actual attack methods detailed in the threat intelligence report, confirming KGConvE's accuracy and practical utility in real-world scenarios.
Calculate Your Potential ROI with KGConvE
Understand the direct financial and operational benefits of implementing advanced AI-driven threat intelligence.
Your Path to Advanced Cybersecurity Intelligence
A structured approach to integrating KGConvE into your existing security operations.
Phase 1: Knowledge Graph Integration (1-2 Weeks)
Integrate existing cybersecurity data sources (CVE, CWE, CAPEC, APT reports) into a unified knowledge graph. Focus on entity and relationship extraction for a foundational understanding of threats.
Phase 2: GCN Model Deployment for Classification (2-4 Weeks)
Deploy and train the GCN component for classifying attack knowledge (e.g., mapping CVEs to APT organizations). Tune parameters for optimal accuracy and generalization on your specific threat landscape.
Phase 3: ConvE Model Deployment for Inference (3-5 Weeks)
Implement and train the ConvE component to infer implicit relationships between cybersecurity entities. Validate inference results against known attack scenarios to refine predictive capabilities.
Phase 4: Real-time Threat Intelligence Integration (Ongoing)
Establish continuous data feeds for real-time threat intelligence updates. Integrate KGConvE's outputs into existing security information and event management (SIEM) systems for proactive defense and situational awareness.
Proactive Cybersecurity Defense and Situational Awareness
KGConvE provides enterprises with an intelligent framework for anticipating and preventing advanced persistent threats (APTs). By accurately classifying attacks and inferring complex relationships between vulnerabilities (CVE), weaknesses (CWE), and attack patterns (CAPEC), organizations can achieve superior threat intelligence and develop more effective defense strategies.
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Early Detection of APTs
Identify potential attack paths and exploitation methods before they materialize.
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Enhanced Threat Intelligence
Gain deeper insights into attacker behavior and complex attack chains.
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Optimized Resource Allocation
Focus defensive efforts on the most probable and impactful threats.
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Improved Incident Response
Accelerate analysis and response to sophisticated cyberattacks.
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Comprehensive Risk Assessment
Understand hidden vulnerabilities and their implications across the network.
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