- We delve into how machine learning performs dynamic malware detection in a scenario where only a single malware sample is available.The different threat scenarios that can happen to a smart home illustrate that compromised IoT devices can affect not just users' comfort and convenience but also their safety.For the advantages of 5G to be unlocked, ample preparation and planning are necessary. Looking at the changes 5G networks might bring about on an enterprise and its security is a good place to start.As manufacturing companies continue to adopt Industry 4.0, many environments could still be falling short on security with outdated systems, unpatched vulnerabilities, and unsecure files that leave them vulnerable to attacks.As the field of telecommunication continues to evolve, so should its security. Understanding its current threat landscape can help reduce the impact of crimes like telecom fraud and prepare us for future threats in the age of the IoT.We examine the nature of internet routing and see how it may be in conflict with border-specific regulations.The evolution of smart homes and smart buildings into complex IoT environments reflects the continuing developments in home and industrial automation. Security should not be left behind as increased complexity also means new threats and risks.Catching malware outbreaks early keeps users, communities, enterprises, and governments safe. But if malware samples are scarce, can machine learning help analyze, detect, and end an outbreak?Radio frequency (RF) technology is being used in operations to control various industrial machines. However, the lack of implemented security in RF communication protocols could lead to production sabotage, system control, and unauthorized access.