SIABench 🚀
Welcome to the SIABench repository! This benchmark is designed for Security Incident Analysis tasks, encompassing various cybersecurity investigation domains such as Memory Forensics, Malware Analysis, Network Forensics, and more.
Overview
SIABench consists of two main components:
- Part I: SIA Tasks - Simulates the in-depth investigation process of SIA tasks with 25 unique security scenarios containing 229 investigative questions in total
- Part II: Alert Triage - Includes true and false positives for evaluating classification tasks with 135 alert scenarios and 135 questions in total (for the time being, only the alerts generated from CIC-IDS2017 network traffic are included in this repo).
We evaluate different LLMs with our dataset using the [🤖 SIABench Agent].
📊 Performance Evaluation on SIA Dataset
Summary Results
| Model | Fully Solved Scenarios (FS) | Overall Solving Percentage (%) |
|---|---|---|
| Claude-4.5-Sonnet | 8/25 | 81.7% |
| GPT-5 | 5/25 | 80.7% |
| Claude-3.5-Sonnet | 4/25 | 71.03% |
| GPT-4.0 | 3/25 | 57.93% |
| DeepSeek-Reasoner | 1/25 | 49.33% |
| Gemini1.5-pro | 1/25 | 41.61% |
| OpenAI-o3-mini | 1/25 | 41.1% |
| GPT-4.0mini | 1/25 | 32.53% |
| Llama3.1-405B | 0/25 | 23.65% |
| Llama3.1-70B | 0/25 | 16.53% |
| Llama3.1-8B | 0/25 | 7.03% |
Detailed Results by Category
📊 Click to view the detailed breakdown by category and difficulty
| Model | NF-E (3S) | NF-M (4S) | NF-H (1S) | MF-E (4S) | MF-M (2S) | MF-H (1S) | MA-E (2S) | MA-M (3S) | MA-H (1S) | MS-E (3S) | MS-M (1S) | MS-H | 🌟Overall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Llama3.1-8B | 0/3 (8.33%) | 0/4 (0%) | 0/1 (0%) | 0/4 (2.27%) | 0/2 (0%) | 0/1 (0%) | 0/2 (25%) | 0/3 (0%) | 0/1 (0%) | 0/3 (27.5%) | 0/1 (9.09%) | — | 0/25 (7.03%) |
| Llama3.1-70B | 0/3 (17.5%) | 0/4 (15.08%) | 0/1 (0%) | 0/4 (14.09%) | 0/2 (5%) | 0/1 (0%) | 0/2 (26.67%) | 0/3 (6.67%) | 0/1 (8.33%) | 0/3 (47.78%) | 0/1 (9.09%) | — | 0/25 (16.53%) |
| Llama3.1-405B | 0/3 (31.79%) | 0/4 (19.07%) | 0/1 (10%) | 0/4 (24.09%) | 0/2 (5%) | 0/1 (0%) | 0/2 (43.33%) | 0/3 (10.26%) | 0/1 (16.67%) | 0/3 (53.33%) | 0/1 (9.09%) | — | 0/25 (23.65%) |
| GPT-4.0 | 0/3 (64.05%) | 0/4 (43.17%) | 0/1 (40%) | 1/4 (61.47%) | 0/2 (60%) | 0/1 (33.33%) | 1/2 (80%) | 0/3 (58.97%) | 0/1 (16.67%) | 1/3 (84.72%) | 0/1 (36.36%) | — | 3/25 (57.93%) |
| GPT-4.0mini | 0/3 (29.29%) | 0/4 (26.49%) | 0/1 (20%) | 0/4 (33.14%) | 0/2 (18.33%) | 0/1 (0%) | 0/2 (73.33%) | 0/3 (18.46%) | 0/1 (16.67%) | 1/3 (58.33%) | 0/1 (36.36%) | — | 1/25 (32.53%) |
| Gemini1.5-pro | 0/3 (62.98%) | 0/4 (31.79%) | 0/1 (20%) | 0/4 (33.35%) | 0/2 (31.67%) | 0/1 (6.67%) | 0/2 (73.33%) | 0/3 (25.13%) | 0/1 (16.67%) | 1/3 (72.22%) | 0/1 (45.45%) | — | 1/25 (41.61%) |
| DeepSeek-Reasoner | 0/3 (70.12%) | 0/4 (36.29%) | 0/1 (30%) | 0/4 (50.88%) | 0/2 (38.33%) | 0/1 (26.67%) | 1/2 (70%) | 0/3 (48.72%) | 0/1 (33.33%) | 0/3 (61.67%) | 0/1 (36.36%) | — | 1/25 (49.33%) |
| OpenAI-o3-mini | 0/3 (72.14%) | 0/4 (41.91%) | 0/1 (40%) | 0/4 (31.24%) | 0/2 (15%) | 0/1 (13.33%) | 0/2 (53.33%) | 0/3 (25.13%) | 0/1 (25%) | 1/3 (63.89%) | 0/1 (36.36%) | — | 1/25 (41.1%) |
| Claude-4.5-Sonnet | 0/3 (87.74%) | 1/4 (86.12%) | 1/1 (100%) | 2/4 (86.74%) | 1/2 (85%) | 0/1 (33.33%) | 0/2 (81.67%) | 1/3 (80.51%) | 0/1 (41.67%) | 2/3 (94.44%) | 0/1 (54.55%) | — | 8/25 (81.7%) |
| GPT-5 | 1/3 (92.5%) | 0/4 (84.92%) | 0/1 (90%) | 2/4 (86.04%) | 0/2 (81.67%) | 0/1 (26.67%) | 1/2 (91.67%) | 0/3 (76.41%) | 0/1 (41.67%) | 1/3 (86.11%) | 0/1 (63.64%) | — | 5/25 (80.7%) |
Legend
- FS = Fully Solved Scenario
- PS = Partially Solved Scenarios (Average Percentage)
- S = Scenarios, Q = Questions
Category Abbreviations:
- MF = Memory Forensics
- MA = Malware Analysis
- NF = Network Forensics
- MS = Miscellaneous
Difficulty Levels:
- E = Easy
- M = Medium
- H = Hard
🔬 Optimized Performance with Model Combinations
While individual models show varying performance across different scenarios, our analysis reveals that strategic model combination can significantly improve overall performance. Different models excel at different types of security investigation tasks, and leveraging their complementary strengths leads to better results.
🎯 Ensemble Analysis: DeepSeek-Reasoner & o3-mini
We conducted a comprehensive ensemble analysis combining DeepSeek-Reasoner and o3-mini to explore different optimization strategies:
Individual Baseline Performance
- DeepSeek-Reasoner: 48.03% (110/229 questions)
- o3-mini: 40.61% (93/229 questions)
Oracle Performance (Theoretical Upper Bound)
When at least one model answers correctly: 58.52% (134/229 questions)
This represents a +10.48% improvement over the best individual model, demonstrating significant complementary capabilities:
- Both models correct: 69 questions (30.13%)
- Only o3-mini correct: 24 questions (10.48%)
- Only DeepSeek correct: 41 questions (17.90%)
- Both models incorrect: 95 questions (41.48%)
Practical Optimization Strategies
1. Scenario-Based Routing (Best: 51.97%)
Select the optimal model for each security scenario type:
- o3-mini excels at: PsExec (71.43%), HawkEye (66.67%), Network Forensics scenarios
- DeepSeek-Reasoner excels at: Malware Analysis (Ramnit 50%, XLM_Macro 46.15%), Obfuscated code (80%), Port scanning (80%), Tomcat exploitation (87.5%)
2. Task Category-Based Routing (49.78%)
Route questions based on investigation domain:
- Network Forensics → o3-mini (54.02% vs 49.43%)
- Malware Analysis → DeepSeek-Reasoner (50.00% vs 30.43%)
- Memory Forensics → DeepSeek-Reasoner (41.67% vs 25.00%)
- Miscellaneous → DeepSeek-Reasoner (52.78% vs 47.22%)
3. Difficulty-Based Routing (48.03%)
All difficulty levels favor DeepSeek-Reasoner:
- Easy questions: 61.11% vs 52.22%
- Medium questions: 43.14% vs 36.27%
- Hard questions: 29.73% vs 24.32%
📊 Performance Comparison Summary
| Strategy | Performance | Improvement |
|---|---|---|
| Oracle (Either correct) | 58.52% | +10.48% |
| Scenario-Based Ensemble | 51.97% | +3.93% |
| Task Category Ensemble | 49.78% | +1.75% |
| Difficulty-Based Ensemble | 48.03% | +0.00% |
| DeepSeek-Reasoner (single) | 48.03% | baseline |
| o3-mini (single) | 40.61% | -7.42% |
💡 Key Insights
- Complementary Strengths: The 10.48% oracle improvement shows significant non-overlapping capabilities between models
- Scenario-Specific Excellence: Different models excel at different attack types and investigation scenarios
- Practical Gains: Scenario-based routing achieves 75% of the theoretical oracle improvement with practical implementation
- Domain Specialization: o3-mini shows strength in network analysis, while DeepSeek-Reasoner excels at malware and memory forensics
📊 Performance Evaluation on Alert Triaging Dataset
📢 Note: The TII-SRC-23 dataset will be uploaded soon!
TII-SRC-23 Dataset (50 TP and 50 FP)
| Model | True Positives (TP) | False Positives (FP) | Accuracy |
|---|---|---|---|
| GPT-5 | 48/50 (96.00%) | 50/50 (100.00%) | 98.00% |
| Claude-4.5-Sonnet | 50/50 (100.00%) | 46/50 (92.00%) | 96.00% |
| DeepSeek-Reasoner | 47/50 (94.00%) | 42/50 (84.00%) | 89.00% |
| GPT-4.0mini | 43/50 (86.00%) | 32/50 (64.00%) | 75.00% |
CIC-IDS2017 Dataset (5 TP and 30 FP)
| Model | True Positives (TP) | False Positives (FP) | Accuracy |
|---|---|---|---|
| GPT-5 | 4/5 (80.00%) | 30/30 (100.00%) | 97.14% |
| Claude-4.5-Sonnet | 4/5 (80.00%) | 27/30 (90.00%) | 88.57% |
| DeepSeek-Reasoner | 4/5 (80.00%) | 26/30 (86.67%) | 85.71% |
| GPT-4.0mini | 4/5 (80.00%) | 14/30 (46.67%) | 51.43% |
🏆 Leaderboard
📊 View Interactive Leaderboard
Explore detailed performance metrics and comparisons across all models on our interactive leaderboard! 🚀
🧠 Model Integrations
✅ Evaluated Models
SIA Agent has been tested and integrated with 11 popular LLMs:
| Model | Provider | API Documentation |
|---|---|---|
| Claude-4.5-Sonnet | Anthropic | Claude API |
| GPT-5 | OpenAI | OpenAI API |
| Claude-3.5-Sonnet | Anthropic | Claude API |
| GPT-4.0 | OpenAI | OpenAI API |
| DeepSeek-Reasoner | DeepSeek | DeepSeek API |
| Gemini1.5-pro | Google Gemini API | |
| OpenAI-o3-mini | OpenAI | OpenAI API |
| GPT-4.0mini | OpenAI | OpenAI API |
| Llama3.1-8B | Meta | Fireworks API |
| Llama3.1-70B | Meta | Fireworks API |
| Llama3.1-405B | Meta | Fireworks API |
📂 Repository Structure
SIABench/
├── SIA_Dataset/ # Contains JSON files with SIA scenarios
│ ├── SIA_Dataset.md # Dataset structure and format documentation
│ └── [scenario files...] # JSON files with security scenarios
├── Alert_Triaging_Dataset/ # Contains JSON files with Alert triaging scenarios
│ ├── Alert_Triaging_Dataset.md # Dataset structure and format documentation
│ └── [scenario files...] # JSON files with security scenarios
├── ETHICS.md # Ethics statement and responsible use guidelines
└── README.md # This file
📋 Dataset Documentation
For detailed information about the SIA and Alert Triaging dataset structure, format, and examples, please refer to:
- SIA_Dataset.md - Complete documentation of the SIA dataset structure, question types, and data format
- Alert_Triaging_Dataset.md - Complete documentation of the Alert Triaging dataset structure, question types, and data format
🛡️ Ethics and Responsible Use
Please review our Ethics Statement which covers:
- Data compliance and curation practices
- Risk assessment and mitigation strategies
- Responsible use guidelines for researchers and practitioners
🌟 Acknowledgments
- The LangChain community
- Security researchers who continue to push boundaries in automated incident analysis