Internet of Things connects the physical and cybernetic world. As such, security issues of IoT devices are especially damaging and need to be addressed. In this treatise, we overview current security issues of IoT with the perspective of future threats. We identify three main trends that need to be specifically addressed: security issues of the integration of IoT with cloud and blockchains, the rapid changes in cryptography due to quantum computing, and finally the rise of artificial intelligence and evolution methods in the scope of security of IoT. We give an overview of the identified threats and propose solutions for securing the IoT in the future.

One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) source code of potentially malicious applications. The analyst must study these data, evaluate them, and properly identify and classify suspicious activities and applications. Fast advances in the area of Artificial Intelligence have produced large language models that can perform a variety of tasks, including generating text summaries and reports. In this article, we study the potential black-box use of LLM chatbots as a support tool for security analysts. We provide two case studies: the first is concerned with the identification of vulnerabilities in Android applications, and the second one is concerned with the analysis of security logs. We show how LLM chatbots can help security analysts in their work, but point out specific limitations and security concerns related to this approach.