This paper provides heuristic methods for obtaining a burning number, which is a graph parameter measuring the speed of the spread of alarm, information, or contagion. For discrete time steps, the heuristics determine which nodes (centers, hubs, vertices, users) should be alarmed (in other words, burned) and in which order, when afterwards each alarmed node alarms its neighbors in the network at the next time step. The goal is to minimize the number of discrete time steps (i.e., time) it takes for the alarm to reach the entire network, so that all the nodes in the networks are alarmed. The burning number is the minimum number of time steps (i.e., number of centers in a time sequence alarmed “from outside”) the process must take. Since the problem is NP complete, its solution for larger networks or graphs has to use heuristics. The heuristics proposed here were tested on a wide range of networks. The complexity of the heuristics ranges in correspondence to the quality of their solution, but all the proposed methods provided a significantly better solution than the competing heuristic.

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

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Smart devices along with sensors are gaining in popularity with the promise of making life easier for the owner. As the number of sensors in an Internet of Things (IoT) system grows, a question arises as to whether the transmission between the sensors and the IoT devices is reliable and whether the user receives alerts correctly and in a timely manner. Increased deployment of IoT devices with sensors increases possible safety risks. It is IoT devices that are often misused to create Distributed Denial of Service (DDoS) attacks, which is due to the weak security of IoT devices against misuse. The article looks at the issue from the opposite point of view, when the target of a DDoS attack are IoT devices in a smart home environment. The article examines how IoT devices and the entire smart home will behave if they become victims of a DDoS attack aimed at the smart home from the outside. The question of security was asked in terms of whether a legitimate user can continue to control and receive information from IoT sensors, which is available during normal operation of the smart home. The case study was done both from the point of view of the attack on the central units managing the IoT sensors directly, as well as on the smart-home personal assistant systems, with which the user can control the IoT sensors. The article presents experimental results for individual attacks performed in the case study and demonstrates the resistance of real IoT sensors against DDoS attack. The main novelty of the article is that the implementation of a personal assistant into the smart home environment increases the resistance of the user’s communication with the sensors. This study is a pilot testing the selected sensor sample to show behavior of smart home under DDoS attack.

Industry 4.0 collects, exchanges, and analyzes data during the production process to increase production efficiency. Internet of Things (IoT) devices are among the basic technologies used for this purpose. However, the integration of IoT technology into the industrial environment faces new security challenges that need to be addressed. This is also true for a production line. The production line is a basic element of industrial production and integrating IoT equipment allows one to streamline the production process and thus reduce costs. On the other hand, IoT integration opens the way for network cyberattacks. One possible cyberattack is the increasingly widely used distributed denial-of-service attack. This article presents a case study that demonstrates the devastating effects of a DDOS attack on a real IoT-based production line and the entire production process. The emphasis was mainly on the integration of IoT devices, which could potentially be misused to run DDoS. Next, the verification of the proposed solution is described, which proves that it is possible to use the sampled flow (sFlow) stream to detect and protect against DDoS attacks on the running production line during the production process.

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.

Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.