In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets’ requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems.

Horizontal displays are emerging as a standard platform for engaging participants in collaborative tasks. Little is known about how groups of people view visualizations in these collaborative settings. Several techniques have been proposed to assist, such as duplicating or reorienting the visual displays. However, when visualizations compete for pixels on the display, prior solutions do not work effectively. We first ran an experiment to identify whether orientation on horizontal displays impacts the legibility of simple visualizations such as charts. The results reveal that users are best at reading a chart when it is the right side up, taking them 20% less time to read than when it is upside down. This insight led us to develop the Orientation Agnostic Graph (OA-Graph), making use of a radial layout designed to be legible regardless of orientation. In a second experiment we found that users can read OA-Graphs better than when the graphs are upside down but less well than traditional graphs in the right side up. The design of our novel visualization, informed by radial visualization methods will assist designers in developing charts that are not easily affected by user orientation, an issue that is prevalent in collaborative table-top systems. Certain tasks such as observing relative differences can benefit from OA-Graphs.

Visual video analytics research, stemming from data captured by surveillance cameras, have mainly focused on traditional computing paradigms, despite emerging platforms including mobile devices. We investigate the potential for situated video analytics, which involves the inspection of video data in the actual environment where the video was captured [14]. Our ultimate goal is to explore the means to visually explore video data effectively, in situated contexts. We first investigate the performance of visual analytic tasks in situated vs. non-situated settings. We find that participants largely benefit from environmental cues for many analytic tasks. We then pose the question of how best to represent situated video data. To answer this, in a design session we explore end-users’ views on how to capture such data. Through the process of sketching, participants leveraged being situated, and explored how being in-situ influenced the participants’ integration of their designs. Based on these two elements, our paper proposes the need to develop novel spatial analytic user interfaces to support situated video analysis.