We conduct a longitudinal study of the National Vulnerability Database (NVD), focusing on the mappings between vulnerabilities (CVEs) and weaknesses (CWEs).Surprisingly, the study reveals that a significant portion of CVEs, fluctuating between 15% and 30% over the years, lack proper CWE mapping, and that almost 40% of the updates are non-informative.We introduce a methodology, based on knowledge graphs, for automating root cause weakness mapping for CVEs and for fixing existing inaccurate mappings.We showcase promising preliminary results toward this end.

Security assessment relies on public information about products, vulnerabilities, and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this article, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CVE, CWE, and CPE). We apply the threat knowledge graph to predict associations between threat databases, specifically between products, vulnerabilities, and weaknesses. We evaluate the prediction performance both in closed world with associations from the knowledge graph and in open world with associations revealed afterward. Using rank-based metrics (i.e., Mean Rank, Mean Reciprocal Rank, and Hits@N scores), we demonstrate the ability of the threat knowledge graph to uncover many associations that are currently unknown but will be revealed in the future, which remains useful over different time periods. We propose approaches to optimize the knowledge graph and show that they indeed help in further uncovering associations. We have made the artifacts of our work publicly available.

The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typically arising from the transmission of large-scale models. Existing adaptive quantization methods, designed to mitigate these overheads, operate under the impractical assumption of uniform device participation. Additionally, these methods are limited in their adaptability due to the necessity of manual quantization level selection and often overlook biases inherent in local devices' data, thereby affecting the robustness of the global model. In response, this paper introduces AQUILA (adaptivequantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL. AQUILA integrates a sophisticated device selection method that prioritizes the quality and usefulness of device updates. Utilizing the exact global model stored by devices enables a more precise device selection criterion, reduces model deviation, and limits the need for hyperparameter adjustments. Furthermore, AQUILA presents an innovative quantization criterion, optimized to improve communication efficiency while assuring model convergence. Our experiments demonstrate that AQUILA significantly decreases communication costs compared to existing methods, while maintaining comparable model performance across diverse non-homogeneous FL settings, such as Non-IID data and heterogeneous model architectures.

The learning approaches of designing a controller to guide the collective behavior of swarm robots have gained significant attention in recent years. However, the scalability of swarm robots and their inherent stochasticity complicate the control problem due to increasing complexity, unpredictability, and non-linearity. Despite considerable progress made in swarm robotics, addressing these challenges remains a significant issue. In this work, we model the stochastic dynamics of a swarm robot system and then propose a novel control framework based on a mean-field control (MFC) embedding multi-agent reinforcement learning (MARL) approach named MF-MARL to deal with these challenges. While MARL is able to deal with stochasticity statistically, we integrate MFC, allowing MF-MARL to cope with large-scale robots. Moreover, we apply statistical moments of robots' state and control action to discretize continuous input and enable MF-MARL to be applied in continuous scenarios. To demonstrate the effectiveness of MF-MARL, we evaluate the performance of the robots on a specific swarm simulation platform. The experimental results show that our algorithm outperforms the traditional algorithms both in navigation and manipulation tasks. Finally, we demonstrate the adaptability of the proposed algorithm through the component failure test.