RuleGenie: SIEM Detection Rule Set Optimization (2505.06701v1)

Abstract: SIEM systems serve as a critical hub, employing rule-based logic to detect and respond to threats. Redundant or overlapping rules in SIEM systems lead to excessive false alerts, degrading analyst performance due to alert fatigue, and increase computational overhead and response latency for actual threats. As a result, optimizing SIEM rule sets is essential for efficient operations. Despite the importance of such optimization, research in this area is limited, with current practices relying on manual optimization methods that are both time-consuming and error-prone due to the scale and complexity of enterprise-level rule sets. To address this gap, we present RuleGenie, a novel LLM aided recommender system designed to optimize SIEM rule sets. Our approach leverages transformer models' multi-head attention capabilities to generate SIEM rule embeddings, which are then analyzed using a similarity matching algorithm to identify the top-k most similar rules. The LLM then processes the rules identified, utilizing its information extraction, language understanding, and reasoning capabilities to analyze rule similarity, evaluate threat coverage and performance metrics, and deliver optimized recommendations for refining the rule set. By automating the rule optimization process, RuleGenie allows security teams to focus on more strategic tasks while enhancing the efficiency of SIEM systems and strengthening organizations' security posture. We evaluated RuleGenie on a comprehensive set of real-world SIEM rule formats, including Splunk, Sigma, and AQL (Ariel query language), demonstrating its platform-agnostic capabilities and adaptability across diverse security infrastructures. Our experimental results show that RuleGenie can effectively identify redundant rules, which in turn decreases false positive rates and enhances overall rule efficiency.

Analysis of RuleGenie: SIEM Detection Rule Set Optimization

The paper "RuleGenie: SIEM Detection Rule Set Optimization" presents an innovative approach to optimizing Security Information and Event Management (SIEM) systems using LLMs. In the context of increasing cyber threats and expanding IT infrastructures, the optimization of SIEM systems, which aggregate security events from diverse sources and employ rule-based logic for threat detection, is critical. The presence of redundant or overlapping rules within SIEM systems leads to excessive false alerts, exacerbating alert fatigue among security analysts and hindering effective threat response. Existing optimization methods are predominantly manual, highlighting the necessity for more sophisticated, automated solutions.

Methodology and Implementation

RuleGenie introduces a systematic, LLM-aided methodology for SIEM rule set optimization. Leveraging transformer models, RuleGenie effectively generates embeddings for SIEM rules, subsequently analyzed using a similarity matching algorithm to pinpoint the top-k most similar rules. Employing LLMs, RuleGenie undertakes a comprehensive analysis, employing information extraction, language understanding, and reasoning capabilities. This facilitates the evaluation of rule similarity, threat coverage, and performance metrics, culminating in automated rule optimization recommendations.

The RuleGenie pipeline is organized into three distinct phases: rule embedding generation, similarity detection, and LLM-driven analysis. The first phase utilizes a transformer-based model, with CodeT5 emerging as the optimal choice due to its ability to capture complex rule syntax through comprehensive embeddings. The second phase employs cosine similarity for syntactic comparison, implementing a top-k retrieval mechanism to identify potentially redundant rules. Finally, the LLM aids in redundancy analysis, performing semantic evaluation and offering targeted recommendations for rule optimization.

Results and Evaluation

The authors evaluate RuleGenie against real-world SIEM rule formats, namely Splunk, Sigma, and AQL, evidencing the system's platform-agnostic adaptability across different security infrastructures. Empirical results illustrate RuleGenie's capability to identify redundant rules effectively, evidenced by a significant reduction in false positive alerts and enhancement of rule efficiency. Notably, the use of Qwen-2.5-14B-Instruct LLM, evaluated against GPT-4o and Llama, achieved superior performance due to its precision and contextual understanding capabilities, while also maintaining data privacy and cost-effectiveness.

The evaluation metrics focused on precision and recall, quantifying RuleGenie's performance in detecting redundancies and generating correct recommendations. The analysis confirmed the efficacy of embedding-based preprocessing and chain-of-thought reasoning in enhancing the system's analysis quality and scalability, thus providing a robust framework for large-scale rule optimization.

Implications and Future Work

The integration of LLMs for SIEM rule set optimization, as exemplified by RuleGenie, holds significant implications for modern cybersecurity operations. By automating the detection of redundant rules and optimizing rule sets, security teams can focus on strategic tasks, potentially transforming the operational landscape of SOCs.

Future research aims to build upon RuleGenie's foundation by developing automated solutions for implementing LLM-generated recommendations and optimizing the execution of rule sequences. This direction not only promises enhanced accuracy and computational efficiency within SIEM systems but also aims to refine resource allocation, fostering a more responsive security framework adaptable to evolving cyber threats.

Overall, the introduction of RuleGenie marks a significant advancement in the domain of cybersecurity, offering a practical and innovative approach to managing the complexity and scale inherent in modern SIEM systems. Through continued research and development, the framework proposed in this paper could become a pivotal tool in safeguarding organizational infrastructure against emerging threats.