Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present a multi-environment human activity recognition system based on observing the changes in the CSI values of the exchanged wireless packets carried by OFDM subcarriers. In essence, we introduce a five-stage CSI-based human activity recognition approach. First, the acquired CSI values associated with each recorded activity instance are processed to remove the existing noise from the recorded data. A novel segmentation algorithm is then presented to identify and extract the portion of the signal that contains the activity. Next, the extracted activity segment is processed using the procedure proposed in the first stage. After that, the relevant features are extracted, and the important features are selected. Finally, the selected features are used to train a support vector machine (SVM) classifier to identify the different performed activities. To validate the performance of the proposed approach, we collected data in two different environments. In each of the environments, several activities were performed by multiple subjects. The performed experiments showed that our proposed approach achieved an average activity recognition accuracy of 91.27%.

This study proposes a passive human identification system based on wireless signals. The proposed system comprises three phases; preprocessing and standardization of recorded channel state information (CSI) signals and the extraction of relevant data using principal component analysis (PCA), transformation of the signals into feature vectors, and finally construction of classification models. To evaluate the proposed approach, we collected data from ten subjects in an indoor environment while performing a set of activities. The proposed approach was tested using three different activities: walking, sitting/standing, and picking up a pen, achieving subject identification accuracy of 97.3%, 99.75%, and 65.5%, respectively. The results suggest that activity-based identification systems can serve as an effective alternative to traditional methods such as passwords and smart cards.

A fast-flux network (FFN) keeps itself accessible by frequently altering its IP addresses, usually avoiding standard detection techniques. In April 2025, six countries released a joint advisory describing this method as a national security threat and calling on DNS providers to improve their detection analytics. Most existing datasets used for these analytics are outdated. This data descriptor presents a new fast-flux dataset compiled from confirmed fast-flux domains and two months of repeated DNS lookups. We used VirusTotal to find additional domains associated with each resolved address. The dataset contains 67,606 fast-flux domains and 23,924 legitimate domains taken from the top Alexa sites. For each domain and IP address, we record the geolocation, autonomous system number (ASN), and country. The dataset also includes a feature file consisting of 91,530 records and 20 features. It also includes the threat model, three analysis tasks, and evaluation results from various studies. A feature-definition forest trained with this protocol achieves 96.0% classification accuracy and serves as the reference baseline. We include the original JSON responses so users can examine or alter the feature definitions.