Maidhili Mohan

https://doi.org/10.64823/ijter.2608006

Industrial control systems (ICS) are fundamental to critical infrastructures such as electrical grids, water processing plants, and manufacturing plant, where Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems regulate physical processes that cannot stop operating at ease. These formerly isolated systems acquire a greater variety of challenges as they become interconnected with IT networks, including command-injection, replay, and false-data injection attacks that may actually result in serious physical harm in addition to some data loss. Two defense methods that have been evolved are discussed in this review. By integrating a private random signal on the control command, dynamic watermarking (DW) takes on an active, physics-based approach resulting in any anomaly in the sensor-actuator feedback loop becoming statistically visible. In contrast, machine learning (ML) approaches try to learn what malicious behavior appears from data. We keep track of the innovations in both directions to distinguish their supplementing powers and blind spots, as well as look at the simple but growing set of work that attempts to bring them together. Also we employed the case studies from PLC-controlled water-tank testbeds, networked control systems, and power-system automatic generation control. The paper ends by discussing the barriers that prevent these research findings from being put into practice, including the ability to scale, adversarial robustness, and real-time deployment on legacy PLC hardware.

The seventeen core goals of the UN's 2030 Agenda for Sustainable Development demand the need for collective action along social, economic, and environmental fronts. Artificial intelligence (AI), the Internet of Things (IoT), blockchain, along with cloud/edge computing are notable examples of intelligent technologies and also regarded as facilitators of this agenda. However, the literature that discusses these technologies tends to interpret technology and policy goals in isolation, thus paying inconsistent attention across the seventeen goals and hardly establishing its claims on the realities of any one nation. This paper aims to bridge that gap. It presents a taxonomy of intelligent technologies, aligns them onto a particular segment of the Sustainable Development Goals (SDGs) involving infrastructure, human well-being, education, energy and climate, disparities, and sustainable cities, and foundations that connecting on case studies from smart city initiatives and digital public infrastructure projects in India. With the goal to lead future technology-for-development research, the research study builds on this depiction and recommends a four-layer conceptual framework: Infrastructure, Intelligence, Trust, and Impact. It draws its conclusion by inspecting the obstacles that challenge this optimism, such as the digital gap, data privacy, a bias in the algorithm and environmental impact of computation itself, before emphasizing federated and privacy-preserving techniques as a feasible direction for further research.