# Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems
###### Abstract  
Malware triage is essential for the security of cyber-physical systems, particularly against Advanced Persistent Threats (APTs). Proper data for this task, however, are hard to come by, as organizations are often reluctant to share their network data due to security concerns. To tackle this issue, this paper presents a secure and distributed framework for the collaborative training of a global model for APT triage without compromising privacy. Using this framework, organizations can share knowledge of APTs without disclosing private data. Moreover, the proposed design employs robust aggregation protocols to safeguard the global model against potential adversaries. The proposed framework is evaluated using real-world data with 15 different APT mechanisms. To make the simulations more challenging, we assume that edge nodes have partial knowledge of APTs. The obtained results demonstrate that participants in the proposed framework can privately share their knowledge, resulting in a robust global model that accurately detects APTs with significant improvement across different model architectures. Under optimal conditions, the designed framework detects almost all APT scenarios with an accuracy of over 90 percent. federated learning; advanced persistent threats; robust aggregation; cyber security; malware triage +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-000  
detected malware should be analyzed based on past events and the dynamics of the network so that the potential impact of the attack on the system can be estimated. This enables security teams to manage their resources and eliminate the threat more effectively. By doing so, organizations can identify and respond to APTs promptly, which, in turn, reduces the risk of long-term infiltration and damage [13]. In other words, utilizing this approach facilitates streamlining the response process, allowing security teams to focus on the most critical threats first, such as APTs, while efficiently handling less severe malware incidents. Existing APT detection models can be categorized into anomaly detection and signature-based detection models [7]. Signature-based models usually employ supervised learning to distinguish between benign and malicious traffic [14; 15]. Nonetheless, given the small proportion of APT samples in network traffic, signature-based models may fail during operation despite the high accuracy they exhibit during training (i.e., the accuracy paradox) [16]. Moreover, signature-based methods may struggle to detect unknown APT samples, as their supervised models usually recognize data distributions similar to those of their training data [17]. Conversely, anomaly detection methods are designed to detect abnormal patterns that may indicate an adversary in the network [10; 14]. However, this approach usually creates an excessive workload for the security team when the false alarm rate increases. As a solution, rule mining can be used to rank the detected anomalies and assist in triaging them [10]. The similarity between the signature-based approach and the anomaly detection approach stems from their need for large amounts of training data [7; 14]. Since these models depend on centralized data offerings, their scope of knowledge is quite limited, and hence the effectiveness of detection is adversely affected, especially against new APTs. Similar to other data-driven methods, building an automated system for the detection and triage of APTs requires ample high-quality data. Nevertheless, such data are hard to come by for several reasons. Firstly, due to the complexity and long-term nature of APTs, simulating these attacks in a lab setting is very challenging and time-consuming. Secondly, real-world data associated with real-world cases of APTs are very rare, as organizations are reluctant to share their network data publicly due to security concerns.