In this paper, we propose a novel digital over-the-air computation (AirComp) framework employing space–time line codes (STLC) to achieve optimal spatial diversity gain with only local channel state information (CSI) at transmitters in uplink scenarios. The proposed technique alleviates the overhead of conventional AirComp, where the access point (AP) must acquire global CSI from all sensors, and is tailored to compute target functions under peak power constraints. Each sensor uniformly quantizes its measurement, applies bit slicing with low-order modulation, and transmits STLC-encoded symbols over two time slots. The AP subsequently performs linear combining of the received signals to detect the superimposed symbols and reconstruct the target sum. Simulation results demonstrate that the proposed technique achieves lower normalized mean-squared error than conventional analog and digital AirComp approaches.

Twitter is a popular public source for threat hunting. Many security vendors and security professionals use Twitter in practice for collecting Indicators of Compromise (IOCs). However, little is known about IOCs on Twitter. Their important characteristics such as earliness, uniqueness, and accuracy have never been investigated. Moreover, how to extract IOCs from Twitter with high accuracy is not obvious. In this paper, we present Twiti, a system that automatically extracts various forms of malware IOCs from Twitter. Based on the collected IOCs, we conduct the first empirical assessment and thorough analysis of malware IOCs on Twitter. Twiti extracts IOCs from tweets identified as having malware IOC information by leveraging natural language processing and machine learning techniques. With extensive evaluation, we demonstrate that not only can Twiti extract malware IOCs accurately, but also the extracted IOCs are unique and early. By analyzing IOCs in Twiti from various aspects, we find that Twitter captures ongoing malware threats such as Emotet variants and malware distribution sites better than other public threat intelligence (TI) feeds. We also find that only a tiny fraction of IOCs on Twitter come from commercial vendor accounts and individual Twitter users are the main contributors of the early detected or exclusive IOCs, which indicates that Twitter can provide many valuable IOCs uncovered in commercial domain