Digitally-enabled technologies are increasingly cyber-physical systems (CPS). They are networked in nature and made up of geographically dispersed components that manage and control data received from humans, equipment, and the environment. Researchers evaluating such technologies are thus challenged to include CPS subsystems and dynamics that might not be obvious components of a product system. Although analysts might assume CPS have negligible or purely beneficial impact on environmental outcomes, such assumptions require justification. As the physical environmental impacts of digital processes (e.g., cryptocurrency mining) gain attention, the need for explicit attention to CPS in environmental assessment becomes more salient. This review investigates how the peer-reviewed environmental assessment literature treats environmental implications of CPS, with a focus on journal articles published in English between 2010-2020. We identify nine CPS subsystems and dynamics addressed in this literature: energy system, digital equipment, non-digital equipment, automation & management, network infrastructure, direct costs, social & health effects, feedbacks, and cybersecurity. Based on these categories, we develop a “cyber-consciousness score” reflecting the extent to which the 115 studies that met our evaluation criteria address CPS, then summarize analytical methods and modeling techniques drawn from reviewed literature to facilitate routine inclusion of CPS in environmental assessment. We find that, given challenges in establishing system boundaries, limited standardization of how to evaluate CPS dynamics, and failure to recognize the role of CPS in a product system under evaluation, the extant environmental assessment literature in peer-reviewed journals largely ignores CPS subsystems and dynamics when evaluating digital or digitally-enabled technologies.

In several networks, intrusion detection plays an important role in assuring cyber security. Numerous studies deal with various cyber attacks in the data via modeling several supervised techniques; however, they have not considered the database size at the time of the optimization. As the size of data increases exponentially, it is vital to cluster the database ahead of detecting the intruder presence in the system. To overcome these confronts, and therefore this paper developed Enhanced Gravitational Search Algorithm -Adaptive Particle Swarm Optimization Algorithm (EGSA-APSO) optimization technique. With the optimization algorithm, the database is clustered into various groups by the developed Intrusion Detection System (IDS) as well as it detects the intrusion presence in the clusters with the employ of the Hyperbolic Secant-based Decision Tree (HSDT) classifier. Subsequently, to the Deep Neural Network (DNN), the compacted data is subjected and train with the optimization method to identify the intrusion detection in the whole database. The experimentation of the developed optimization technique is performed by exploiting several measures such as True Positive Rate (TPR), accuracy, and True Negative Rate (TNR), the outcomes exhibit a superior performance over the conventional models.