This study presents a comprehensive analysis of information diffusion in social networks with time delay mechanisms. We first analyze real Reddit thread data, identifying limitations in the sample size. To overcome this, we develop synthetic network models with varied structural properties. Our approach tests three delay types (constant, uniform, exponential) across different network structures, using machine learning models to identify key factors influencing information coverage. The results show that spread probability consistently impacts diffusion across all datasets. Gradient Boosting models achieve R² = 0.847 on synthetic data. Random networks with a constant delay mechanism and high spread probability (0.4) maximize coverage. When verified against test data, peak speed time emerges as the strongest predictor (r = 0.995, p < 0.001). Our findings provide practical recommendations for optimizing information spread in social networks and demonstrate the value of integrating real and synthetic data in diffusion modeling.

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.

Wireless sensor networks are often used to monitor mobile devices in a specific sensor field from unauthorized intrusion. The research examines intrusions into wireless sensor networks based on the creation of streams of false events. According to the simulation results, the property of the dependence of the wireless sensor network life cycle on the type of error event flow is revealed. Research on the impact of erroneous events on the sensor field has shown that ensuring the mobility of sensor nodes can increase the life cycle of the network. A model of a wireless sensor network is proposed, which is a Poisson sensor field and a flow of events in the form of erroneous streams crossing a given plane. According to the research results, the density distribution from the faulty object to the first sensor, the required sensor node density, and the required sensor node detection radius at a given probability of detecting a faulty object, the number of messages transmitted by the object through the sensor field in the network.

The problems and solutions in the logistics of warehouse operations of the transport company are highlighted. The software for the implementation of the simulation model is defined. Simulation modeling of the functioning of the transport company's composition was carried out. The main stages in the implementation of the simulation model are considered. Created a context diagram for the simulation model and refined it using context diagram decomposition. The stages of creating a simulation model in FlexSim are considered. Database created and connected to FlexSim software. The objects for the implementation of the simulation model were considered, all objects were given initial parameters. Run and get basic statistics during the simulation. After the simulation simulation analysis, optimization was carried out.