Match Word with Deed: Maintaining Consistency for IoT Systems with Behavior Models

The proposed study is devoted to the development of an algorithm for detecting network attacks. The considered approach to detection of attacks is based on monitoring and the description of states of the behavior of data packets in networks of the Internet of things. The developed network attack detection subsystem demonstrates high efficiency in detecting unknown deviations in the process of data packet transmission. The proposed attack detection technology can be used to protect data packets in public IoT networks, where there is a high probability of new cyberattacks. The greatest efficiency of the developed algorithm is achieved in IoT systems, where the use of classes of certain network attacks is limited to online services and does not change significantly over time. This algorithm allows the use of programmed models of the normal behavior of network packets to detect possible cyberattacks.

Optimizing code allocation for hybrid on-chip memory in IoT systems

Mission impact assessment (MIA) research has been explored to evaluate the performance and effectiveness of a mission system, such as enterprise networks with organizational missions and military or tactical mission teams with assigned missions. The key components in such mission systems, including assets, services, tasks, vulnerability, attacks, and defenses, are interdependent, and their impacts are interwoven. However, the current state-of-theart MIA approaches have less studied such interdependencies. In addition, they have not modeled strategic attack-defense interactions under partial observability. In this work, we propose a novel MIA framework that assesses measures of performance (MoP) or measures of effectiveness (MoE) based on the service requirements (e.g., correctness or timeliness) of a given mission system based on full and comprehensive modeling and simulation of the key system components and their interdependencies. Particularly, we model intelligent attackdefense strategy selections based on hypergame theory, which allows considering uncertainty in estimating each player's hypergame expected utility (HEU) for its best strategy selection. As the case study, we consider an Internet-of-Things (IoT)-based mission system aiming to accurately and timely detect an object, given stringent accuracy and time constraints for successful mission completion. Via extensive simulation experiments, we validate the quality of the proposed MIA tool in its inference accuracy of the mission performance under a wide range of different environmental settings hindering the mission performance assessment and attack-defense interactions. Our results prove that the developed MIA framework shows a sufficiently high inference accuracy (e.g., 80%) even with a small portion of the training dataset (e.g., 20-50%). We also found the MIA can better assess the system's mission performance when attackers exhibit clearer patterns to take more strategic actions using hypergame theory.