Formalization of the feature space for detection of attacks on wireless sensor networks

The article describes the formalization of the feature space in order to detect abnormal behaviour of nodes in wireless sensor network using statistical methods. The main methods of destructive impact on the infrastructure of wireless sensor networks based on ZigBee Protocol stack are considered. Special attention is paid to attacks on integrity and availability, which theoretically can be detected using the methods of machine learning and mathematical statistics. On the basis of standards and specifications, as well as considered attacks, the space of more than 50 features is developed. Using the methods of Shannon, Kullback and accumulated frequencies, informative value of formalized signs was evaluated. Conclusions about the existing dependencies between the information content of features, the statistics collection period and sample size used to calculate the information content are drawn. Received the results can be used as a basis for further evaluation of the most suitable characteristics for the classification of attacks depending on the network characteristics. In the future the main aim of the study is to build an intrusion detection system that uses statistics of the interactions for a certain period of time as a source of information about the system

Planning of Autonomous Multi-agent Intersection

In this paper, we propose a traffic management system with agents acting on behalf autonomous vehicle at the crossroads. Alternatively to existing solutions based on usage of semiautonomous control systems with the control unit, proposed in this paper algorithm apply the principles of decentralized multi-agent control. Agents during their collaboration generate intersection plan and determinate the optimal order of road intersection for a given criterion based on the exchange of information about them and their environment. The paper contains optimization criteria for possible routes selection and experiments that perform in order to estimate the proposed model. Experiment results show that this model can significantly reduce traffic density compared to the traditional traffic management systems. Moreover, the proposed algorithm efficiency increases with road traffic density. Furthermore, the availability of control unit in the system significantly reduces the negative impact of possible failures and hacker attacks

Planning of Autonomous Multi-agent Intersection

In this paper, we propose a traffic management system with agents acting on behalf autonomous vehicle at the crossroads. Alternatively to existing solutions based on usage of semiautonomous control systems with the control unit, proposed in this paper algorithm apply the principles of decentralized multi-agent control. Agents during their collaboration generate intersection plan and determinate the optimal order of road intersection for a given criterion based on the exchange of information about them and their environment. The paper contains optimization criteria for possible routes selection and experiments that perform in order to estimate the proposed model. Experiment results show that this model can significantly reduce traffic density compared to the traditional traffic management systems. Moreover, the proposed algorithm efficiency increases with road traffic density. Furthermore, the availability of control unit in the system significantly reduces the negative impact of possible failures and hacker attacks