Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.

Globally, the number of falls among the elderly is rising, particularly among those 60 and older. An important contributing element to these falls is the fact that elderly people who live alone are not regularly supervised. A significant number of claims are filed for injuries caused by falls in the elderly, and at times these falls result in fatalities. Therefore, well-founded, and practical e-health technologies are critical for elder care, particularly for individuals who live alone. One of the emerging and rapid-growing technologies like Artificial Intelligence would be an excellent companion for them to continuously monitor their health condition and prevent falls. This review paper compares various research, surveys, studies, and experiments conducted on elderly fall prevention utilizing Artificial Intelligence and other technologies such as Internet of Things (IoT), Sensor, Radio Detection and Ranging (RADAR), Infrared Radiation (IR) and Hardware technologies. It has been identified that in real time and long-term monitoring without human intervention, AI-IoT technology will be the best solution for fall prevention in older adults.