DShield Traffic Analysis Using ELK: A Simple Guide to Effective Threat Hunting

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In the world of cybersecurity, identifying suspicious or malicious activity from network traffic is crucial. Using a powerful open-source toolset such as ELK (Elasticsearch, Logstash, and Kibana), cybersecurity professionals can analyze vast amounts of data from different sources. However, the overwhelming volume of traffic can make it difficult to pinpoint potential threats. In this article, we explore how DShield traffic analysis can be effectively carried out using Kibana’s ELK stack, focusing on various logs, filters, and query techniques to help hone in on suspicious activity.

Summary:

The article explains the process of analyzing DShield traffic using ELK, with a focus on how to identify interesting activity within large datasets. The author uses logs from the cowrie honeypot, webhoneypot, and firewall as key data sources. The first step in the analysis involves filtering through firewall data to identify potential reconnaissance activity based on IP and port patterns. A particular source IP (193.68.89.10) is tracked over time, where it is found to have consistent scanning activity. Web traffic analysis reveals several GET and HEAD requests, but no login attempts, while TTL analysis hints at the possibility of different routing paths. Additionally, SIEM alerts provide a broader understanding of the threat intelligence surrounding the source. The process is presented as simple and efficient, demonstrating how ELK can be leveraged to enhance cybersecurity efforts through retrospective analysis.

What Undercode Says:

DShield traffic analysis using the ELK stack offers cybersecurity professionals a structured approach to dive deep into network traffic, identify suspicious behavior, and gain actionable insights. By utilizing tools like Kibana, one can make sense of huge datasets collected over time from various honeypots, firewalls, and network sensors. This approach isn’t just about having the right tools; it’s about understanding the nuances of data that could reveal potential threats.

Key Steps in Analysis:

Setting Time Range: By limiting the time range, security analysts can focus on a manageable subset of traffic, avoiding the overwhelming volume of data that can often cloud critical insights. Setting the time frame for analysis—such as one week—helps narrow down potential points of interest.
Filtering Suspicious Activity: The method of identifying source ports and IP addresses tied to potential reconnaissance activities is crucial. In this case, the focus on static values such as IPID 54321 offers a promising start. It’s important to note that irregularities like unusually high counts or static values can often indicate scanning or other malicious activities.
Cross-Sensor Analysis: Leveraging multiple sensor data sources is another effective technique for gaining more granular insights. By using a query to cross-reference three different sensors—picollector, collector, and vps-711a413c—the user is able to track the same IP across different systems, ensuring that no data point is missed.
Observing Trends Over Time: The analysis also shows how traffic from the source IP 193.68.89.10 varied over time. Notably, there was a sharp spike in web activity on February 15th. Such spikes in activity, particularly when tied to scanning or reconnaissance, often point to deeper issues, such as automated bots or attack preparation.
Deep Dive into Web Traffic: The focus on the webhoneypot logs uncovers interesting details, such as multiple GET and HEAD requests but no login attempts. While this may suggest a reconnaissance activity, it’s also critical to understand why there are no login attempts—indicating the possibility that the attacker was merely mapping the network or system.
TTL Analysis: One particularly insightful part of the analysis is the Time-to-Live (TTL) analysis. It reveals that the same source IP has traffic that takes different routes or potentially uses proxies. This difference in TTL suggests an attacker is trying to hide their footprint, making it more difficult to track the origin of the malicious activity.
Threat Intelligence and SIEM Alerts: The inclusion of SIEM alerts in the analysis provides a real-time view of the threat intelligence landscape surrounding the source IP. SIEM alerts, combined with network traffic analysis, offer a comprehensive view of the potential danger posed by the IP in question.

By combining these multiple steps, it’s clear that ELK-based analysis offers a powerful tool for cybersecurity experts to perform detailed retrospective analysis. The ability to query, visualize, and correlate traffic data from multiple sensors enhances the effectiveness of threat hunting. Using such an approach, professionals can uncover hidden threats, providing a vital layer of security.

Fact Checker Results:

Tools and Techniques: The article relies on proven open-source tools (Kibana, Elasticsearch, Logstash) for traffic analysis, ensuring that the methodology is robust and accessible for cybersecurity practitioners.
Data Sources: The DShield traffic, as well as logs from honeypots and firewalls, are well-established sources of information for traffic analysis. Cross-referencing these logs is a standard practice in security research.
Analysis Validity: The use of common filtering techniques, such as IP and port scans, is in line with current threat-hunting methodologies, making the analysis credible. However, external tools like Zeek and packet capture could provide more in-depth analysis beyond the scope of this article.