A major driver of declining biodiversity is landcover change leading to loss of habitat. Many studies have estimated large-scale declines in biodiversity, but loss of biodiversity at a local scale due to the immediate effects of development have been poorly studied. California, in particular, is a biodiversity hotspot and has rapidly developed; thus, it is important to understand the effects of development on wildlife. Here, we conducted reconnaissance surveys -- a type of survey often used by consulting biologists in support of environmental review of proposed projects -- to measure changes in relative abundance and richness of vertebrate species in response to urban development. We completed 2 reconnaissance surveys at each of 52 control sites that remained undeveloped at the times of both surveys, and at each of 26 impact sites that had been developed by the time of the second survey. We completed the surveys as part of a before-after, control-impact (BACI) experimental design. Our main interest was on the interaction effect between the before-after phases and control-impact treatment levels, or on the impact of development. We also tested for effects of survey duration, years intervening the surveys in the before and after phases, project area size, latitude, degree of connectedness to adjacent open space, and whether the site was a redevelopment site, infill or not infill. After development, the average number of vertebrate wildlife species we detected declined 48% within the project area, and 66% within the bounds of the project sites. Further, the average number of vertebrate animals we counted declined 90% within the project area, and 89% within the bounds of the project sites. Development impacts measured by the mean number of species detected per survey were greatest for amphibians (-100%), followed by mammals (-86%), grassland birds (-75%), raptors (-53%), special-status species (-49%), all birds as a group (-48%), non-native birds (-44%), and synanthropic birds (-28%). Our results indicated that urban development substantially reduced vertebrate species richness and numerical abundance, even after richness and abundance had likely already been depleted by the cumulative effects of loss, fragmentation, and degradation of habitat in the urbanizing environment. Cumulative effects monitoring is needed, and so are conservation measures to mitigate the effects of urbanization.

There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA0043) of the MITRE ATT&CK framework. Using the newly created dataset, UWF-Zeekdata22, based on the MITRE ATT&CK framework, patterns involving network connectivity, connection duration, and data volume were found and loaded into a graph environment. Patterns were also found in the graphed data that match the Reconnaissance as well as other tactics captured by UWF-Zeekdata22. The Star motif was particularly useful in mapping the Reconnaissance tactic. The results of this paper show that graph databases/graph engines can be essential tools for understanding network traffic and trying to detect network intrusions before they happen. Finally, an analysis of the run-time performance of the reduced dataset used to create the graph databases showed that the reduced datasets performed better than the full dataset.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, despite these benefits, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, standards, and server technologies). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices.

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.