Botnets have evolved to become one of the most serious threats to the Internet and there is substantial research on both botnets and botnet detection techniques. This survey reviewed the history of botnets and botnet detection techniques. The survey showed traditional botnet detection techniques rely on passive techniques, primarily honeypots, and that honeypots are not effective at detecting peer-to-peer and other decentralized botnets. Furthermore, the detection techniques aimed at decentralized and peer-to-peer botnets focus on detecting communications between the infected bots. Recent research has shown hierarchical clustering of flow data and machine learning are effective techniques for detecting botnet peer-to-peer traffic.

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The world has realized traditional cybersecurity models are flawed because users and systems behind the perimeter are implicitly trusted. The response has been to treat access requests and behaviors post-access as untrusted. Thus, the aim of such zero trust architecture is to establish a borderless access-control framework. Accordingly, existing research is centered around network perimeters and communications layers. That is, data access channels or endpoints and not data itself. Consequently, we conducted a systematic review of relevant literature and developed a model illustrating a potential application of zero trust tenets and principles to data objects instead of data access pathways based on the findings. Concurrently, given the rising popularity of employing artificial intelligence to zero trust frameworks, our zero trust data concept targets artificial intelligence training and real-world evaluation data segments.

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Lack of gender diversity is a recognized issue within STEM. As a result, much effort has been invested in alternative means of recruitment and education. One example are cybersecurity competitions serving as popular training mechanisms. Yet, earlier research found that competitors perceive these competitions to be designed in a non-gender inclusive manner. The prior work did not consider potential biases in competitors however. Motivationally, future competition design may be misguided if competitors' perceptions were skewed due to exposure and social desirability. This led to the general question of whether grade level is related to identification of such competitions as nongender inclusive in design. Accordingly, this study measured if U.S. collegiate upperclassmen and underclassmen participating in competitions are equally likely to identify cybersecurity competitions as non-gender inclusive. To that end, a chi-square statistic was used to compare the rates of identification of competition design gender inclusiveness amongst 104 cyber competitors. The results of the analysis indicated that upperclassmen are no more likely than underclassmen to identify a cybersecurity competition as non-gender inclusive. Thus, gender non-inclusiveness of competitions may be inherent in the design of such as opposed to a subjective experience of competitors.