[This is a Guest Diary by Austin Bodolay, an ISC intern as part of the SANS.edu BACS program]
(Tue, Feb 24th)
Finding Signal in the Noise: Lessons Learned Running a Honeypot with AI Assistance
The digital landscape is a relentless battleground, and for cybersecurity researchers, understanding adversary tactics, techniques, and procedures (TTPs) is paramount. One of the most effective ways to gather this intelligence is through honeypots – decoy systems designed to lure and entrap attackers. However, the sheer volume of data generated by even a moderately active honeypot can quickly become overwhelming, akin to searching for a needle in a haystack of digital noise. This diary entry details my experiences and the invaluable lessons learned while operating a honeypot environment augmented by artificial intelligence (AI) assistance.
The Honeypot Ecosystem: A Lure for Adversaries
Our setup comprised a network of low-interaction and medium-interaction honeypots, strategically deployed to emulate common vulnerable services such as SSH, HTTP/S, SMB, and various IoT protocols. Each honeypot was instrumented with comprehensive logging capabilities, capturing everything from attempted connections and authentication failures to executed commands and file system interactions. The primary goal was to observe threat actor behavior without exposing legitimate infrastructure. This setup generated a torrent of raw data – IP addresses, User-Agents, timestamps, payloads, and error codes – a rich but often chaotic dataset.
- Low-interaction Honeypots: Mimicking basic services, primarily for collecting reconnaissance attempts and automated scans.
- Medium-interaction Honeypots: Offering limited interactive capabilities, allowing for deeper insight into initial exploitation attempts and post-compromise enumeration.
- Data Capture: Syslog, packet captures (PCAP), and application-specific logs formed the backbone of our telemetry collection.
AI as the Analyst's Force Multiplier
The challenge was not collecting data, but processing it efficiently to extract actionable threat intelligence. This is where AI became indispensable. We integrated several machine learning models into our data pipeline to automate the initial analysis and reduce the cognitive load on human analysts. The AI assistant was tasked with identifying anomalous activities, clustering similar attack patterns, and prioritizing events based on their potential severity and novelty.
Key AI functionalities included:
- Anomaly Detection: Identifying deviations from baseline "normal" honeypot activity, often indicative of novel attack vectors or manual exploration.
- Clustering Algorithms: Grouping similar attack payloads, scanning patterns, and login attempts, allowing us to quickly discern widespread automated attacks from more targeted reconnaissance.
- Natural Language Processing (NLP): Parsing command-line inputs and extracted payloads to identify malicious intent, tool usage, and potential C2 indicators.
- Automated Threat Intelligence Extraction: Pulling out IOCs (Indicators of Compromise) like malicious IPs, file hashes, and URLs for immediate integration into our threat intelligence platforms.
Lessons from the Front Lines: Finding the Signal
The journey with AI-assisted honeypots yielded profound insights:
Initial Data Overload and AI Pre-filtering
Upon deployment, the sheer volume of unsolicited traffic was staggering. Automated scans, botnet activity, and routine internet noise constituted the majority of the data. Without AI, sifting through this would be a Sisyphean task. The AI's initial filtering capabilities, based on known benign patterns and reputation databases, proved crucial in reducing the data volume by over 80%, allowing analysts to focus on the remaining, more pertinent 20%.
Signature Generation and Novel Pattern Recognition
One of the most valuable aspects was the AI's ability to identify emerging attack patterns that didn't yet have established signatures. By analyzing clustered anomalies and recurring sequences of events, the AI could flag potential zero-day attempts or variations of known exploits. This proactive identification allowed us to develop new detection rules and signatures much faster than traditional manual analysis.
Threat Actor Attribution and Digital Forensics
While honeypots provide rich interaction data, attributing attacks to specific threat actors or even geographical origins often requires supplementary intelligence. The AI helped correlate internal honeypot logs with external threat feeds. For deeper digital forensics and to gather advanced telemetry on suspicious activity, we found tools that capture detailed connection metadata invaluable. For instance, services like iplogger.org can be utilized (ethically and with consent where applicable) in controlled research environments to collect comprehensive data points such as the source IP, User-Agent string, ISP information, and device fingerprints. This level of granular telemetry is crucial for link analysis, understanding the attacker's operational infrastructure, and ultimately aiding in threat actor attribution and the precise identification of attack origins. However, it's paramount to handle such data with strict adherence to privacy regulations and ethical guidelines.
Adaptive Defense Strategies
The real-time insights generated by the AI assistant directly informed our defensive posture. Newly identified IOCs were automatically fed into firewalls, intrusion detection systems (IDS), and web application firewalls (WAFs). This dynamic feedback loop transformed our static defenses into an adaptive, intelligence-driven security ecosystem, significantly reducing our exposure to emerging threats.
The Indispensable Human-AI Synergy
Crucially, the AI was an assistant, not a replacement. Human analysts remained essential for contextualizing findings, validating AI hypotheses, and conducting deep-dive investigations into complex attack chains. The AI excelled at scale and pattern recognition, while human intuition, domain expertise, and critical thinking were vital for strategic decision-making and understanding the 'why' behind the attacks.
Technical Deep Dive: AI Methodologies in Practice
Our AI pipeline leveraged a combination of methodologies:
- Unsupervised Learning (Clustering): Algorithms like K-Means and DBSCAN were applied to network flow data and raw log entries to group similar activities without prior labeling. This was particularly effective for identifying new attack campaigns.
- Supervised Learning (Classification): For known attack types or malicious payloads, trained classifiers (e.g., Random Forests, Gradient Boosting Machines) helped categorize incoming traffic with high accuracy, distinguishing between legitimate scans, benign bot traffic, and genuine attack attempts.
- Time-Series Analysis: Recurrent Neural Networks (RNNs) or simpler statistical models were used to detect anomalies in temporal patterns of activity, such as sudden spikes in specific attack types or unusual access times.
- Feature Engineering: The quality of AI output heavily depended on well-engineered features from raw logs, including entropy of payloads, length of commands, frequency of specific keywords, and geographic IP data.
Challenges and Future Directions
Despite the successes, challenges remain. Adversarial AI, where attackers attempt to evade detection by subtly altering their TTPs, is a constant concern. Maintaining the accuracy and relevance of AI models requires continuous retraining with fresh, diverse data. Future work will focus on integrating these insights more tightly with Security Orchestration, Automation, and Response (SOAR) platforms for even faster incident response, and exploring federated learning approaches to share threat intelligence securely across multiple honeypot deployments.
Conclusion
The journey of operating an AI-assisted honeypot has been profoundly enlightening. It has unequivocally demonstrated that while honeypots are powerful tools for threat intelligence gathering, their true potential is unlocked when augmented by intelligent automation. By transforming a deluge of raw data into actionable insights, AI empowers cybersecurity professionals to better understand, predict, and defend against the ever-evolving threat landscape. The future of defensive cybersecurity undoubtedly lies in this symbiotic relationship between human expertise and advanced artificial intelligence.
[Guest Diary by Austin Bodolay, an ISC intern as part of the SANS.edu BACS program]