By Oluwapelumi BankoleResearcher, Information Systems & Cybersecurity, University of Nevada, Las Vegas Every morning, millions of Americans wake up in homes full of connected devices. The thermostat knows when you leave. The doorbell camera watches your street. The hospital down the road runs infusion pumps, patient monitors, and HVAC systems that communicate over the same category of network as your smart refrigerator. And almost none of these devices are adequately protected. We have built an extraordinary infrastructure of connected machines, and we are defending it with tools designed for a different era. This is not a problem of awareness. Cybersecurity is a top federal priority. The Cybersecurity and Infrastructure Security Agency (CISA) publishes advisories weekly. Billions of dollars flow into enterprise firewalls, endpoint protection, and security operations centers. And yet, the attack surface keeps growing. As of 2024, the U.S. power grid alone hosts over 2.3 million connected IoT devices, many running outdated firmware with no patching schedule and no monitoring in place. The gap is not between what we know and what we fear. The gap is between the security systems we have built and the environments those systems actually need to operate in. The Lab Looks Nothing Like the Real World Intrusion detection systems, the software designed to flag malicious activity on a network, have improved dramatically over the past decade. Machine learning and deep learning models can now identify attack patterns with remarkable accuracy in research settings. Transformer architectures borrowed from natural language processing, long short-term memory networks trained on sequential traffic data, ensemble models combining multiple classifiers: the academic literature is full of systems achieving 98 or 99 percent accuracy. Those numbers are often misleading. The accuracy figure typically comes from a laboratory dataset, collected in controlled conditions, with relatively clean traffic distributions, and tested on