Towards Named Data Networking for Internet of Things Search Engines

The advance of internet of things (IoT) techniques enables a variety of smart-world systems in energy, transportation, home, and city infrastructure, among others. To provide cost-effective data-oriented service, internet of things search engines (IoTSE) have received growing attention as a platform to support efficient data analytics. There are a number of challenges in designing efficient and intelligent IoTSE. In this chapter, the authors focus on the efficiency issue of IoTSE and design the named data networking (NDN)-based approach for IoTSE. To be specific, they first design a simple simulation environment to compare the IP-based network's performance against named data networking (NDN). They then create four scenarios tailored to study the approach's resilience to address network issues and scalability with the growing number of queries in IoTSE. They implement the four scenarios using ns-3 and carry out extensive performance evaluation to determine the efficacy of the approach concerning network resilience and scalability. They also discuss some remaining issues that need further research.

Effectiveness of Increasing Realism Into Cybersecurity Training

This article describes how cybersecurity is a field that is growing at an exponential rate. In light of many highly publicized incidences of cyber-attacks against organizations, the need to hire experienced cybersecurity professionals is increasing. The lack of available workforce to fill open positions is alarming and organizations are finding that potential candidates with academic degrees and certifications alone are not as valuable as those with experience. Gaining rapid experience requires immersion into realistic virtual environments that mimic real-world environments. Currently, cybersecurity competitions leverage many technologies that immerse participants into virtual environments that mimic real-world systems to improve experiential learning. These systems are expensive to build and maintain, and to continuously improve realism is difficult. However, the training value of cyber competitions in which the participants cannot distinguish from real-world systems will ultimately develop highly experience cybersecurity professionals.

Bayesian on-line anticipation of critical transitions

The design of reliable indicators to anticipate critical transitions in complex systems is an important task in order to detect a coming sudden regime shift and to take action in order to either prevent it or mitigate its consequences. We present a data-driven method based on the estimation of a parameterized nonlinear stochastic differential equation that allows for a robust anticipation of critical transitions even in the presence of strong noise levels like they are present in many real world systems. Since the parameter estimation is done by a Markov Chain Monte Carlo approach we have access to credibility bands allowing for a better interpretation of the reliability of the results. By introducing a Bayesian linear segment fit it is possible to give an estimate for the time horizon in which the transition will probably occur based on the current state of information. This approach is also able to handle nonlinear time dependencies of the parameter controlling the transition. In general the method could be used as a tool for on-line analysis to detect changes in the resilience of the system and to provide information on the probability of the occurrence of a critical transition in future.

Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

Terrorism in the Modern World-System: Towards the Problem of Prospects for Interdisciplinary Research

Introduction. The variety of approaches and topics in the study of terrorism, as well as the obvious difference in axiological grounds for assessing terrorist activity, allows the authors to raise the question of an interdisciplinary study of this problem. The authors understand terrorism as an illegal political confrontation in the struggle for power with the use of violence in order to intimidate or physically eliminate the enemy.Methodology and sources. The methodological basis of the work is the world-system concept of I. Wallerstein. The authors reveal the advantages of the world-system approach by comparing it with the paradigm of political realism in the theory of international relations. They indicate the boundaries of the paradigm of political realism, which operates at the level of the concepts of “States” and “International Coalitions”, while the phenomenon of terrorism includes structures at the level of groups and organizations. The world-systems approach allows researchers to see terrorism as an anti-system movement generated by the contradictions in the development of the system itself, to distinguish between pro-system and anti-system terrorism, to analyze this phenomenon at all societal levels. One of the essential advantages of the world-systems approach is its ability to accumulate different approaches and related disciplines in order to describe the dynamics of modern societies. In their theoretical constructions, the authors rely on the typology of terrorist organizations by O. Lizardo and A. Bergesen, as well as on the concept of waves of terrorism by D. Rapport. The authors conduct a critical analysis of the typology of terrorism by O. Lizardo, A. Bergesen and note that this typology helps to see the structural source (core, semi-periphery, periphery) and the main structural goal of terrorist organizations, but leaves behind such a phenomenon as state terrorism.Results and discussion. The authors describe terrorism in its interrelation with processes in the world system at different societal levels. At the super-macro level, the world-systems conditions for the emergence of waves of terrorist activity are described, and the links between terrorism and the struggle to establish a global order are indicated. At the macro level (the level of political confrontation for the establishment of some form of order within the state), the authors investigate the differences between terrorism in “closed” and “open” societies. They note the connection between bursts of terrorist activity and the transition from a “closed” to an “open” state and vice versa. The authors consider the connection of terrorism with the processes of the peripheralization of societies as a meso-level phenomenon. Such terrorism, as a rule, is local and is inspired by the national liberation slogans of the societies of the internal periphery, the authors note that the struggle with the state here can go for both sovereignty and disputed territories. The authors refer to the meso-level the activities of terrorist organizations aimed at migrants who come from the outer periphery. The authors note that the subject of terrorism research at the micro level is, as a rule, the personality of the terrorist.Conclusion. The use of a world-systems approach to consider terrorism seems promising, and allows researchers to consider structural relations that are not available to other approaches. The authors express the hope that the interdisciplinary capabilities of the world-systems approach, its methodological potential woul be able to form a reliable basis for subsequent studies of terrorism as one of the means of illegitimate political violence in the modern world.

Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics

Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the segmentation procedure. In the considered case, the transition between parts corresponding to homogeneous segments is smooth and non-linear. This causes that the segmentation algorithm is more complex than in the classical case. We propose to apply the divergence measures that are based on the distance between the probability density functions for the two examined distributions. The novel segmentation algorithm is applied to real acoustic signals acquired during coffee grinding. Justification of the methodology has been performed experimentally and using Monte-Carlo simulations for data from the model with heavy-tailed distribution (here the stable distribution) with time-varying parameters. Although the methodology is demonstrated for a specific case, it can be extended to any process with time-changing characteristics.

Detection of illicit cryptomining using network metadata

Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems.

Recent Trends of Controlling Chaotic Resonance and Future Perspectives

Stochastic resonance is a phenomenon in which the effects of additive noise strengthen the signal response against weak input signals in non-linear systems with a specific barrier or threshold. Recently, several studies on stochastic resonance have been conducted considering various engineering applications. In addition to additive stochastic noise, deterministic chaos causes a phenomenon similar to the stochastic resonance, which is known as chaotic resonance. The signal response of the chaotic resonance is maximized around the attractor-merging bifurcation for the emergence of chaos-chaos intermittency. Previous studies have shown that the sensitivity of chaotic resonance is higher than that of stochastic resonance. However, the engineering applications of chaotic resonance are limited. There are two possible reasons for this. First, the stochastic noise required to induce stochastic resonance can be easily controlled from outside of the stochastic resonance system. Conversely, in chaotic resonance, the attractor-merging bifurcation must be induced via the adjustment of internal system parameters. In many cases, achieving this adjustment from outside the system is difficult, particularly in biological systems. Second, chaotic resonance degrades owing to the influence of noise, which is generally inevitable in real-world systems. Herein, we introduce the findings of previous studies concerning chaotic resonance over the past decade and summarize the recent findings and conceivable approaches for the reduced region of orbit feedback method to address the aforementioned difficulties.

Data Management Framework & UAV Simulation Testbed for the Study of System-level Prognostics Technologies

Prognostics-enabled technologies have emerged over the last few years, primarily for Condition Based Maintenance (CBM+) applications, which are used for maintenance and operational scheduling. However, due to the challenges that arise from real-world systems and safety concerns, they have not been adopted for operational decision making based on system end of life estimates. It is typically cost-prohibitive or highly unsafe to run a system to complete failure and, therefore, engineers turn to simulation studies for analyzing system performance. Prognostics research has matured to a point where we can start putting pieces together to be deployed on real systems, but this reveals new problems. First, a lack of standardization exists within this body of research that hinders our ability to compose various technologies or study their joint interactions when used together. The second hindrance lies in data management and creates hurdles when trying to reproduce results for validation or use the data as input to machine learning algorithms. We propose an end-to-end object-oriented data management framework & simulation testbed that can be used for a wide variety of applications. In this paper, we describe the requirements, design, and implementation of the framework and provide a detailed case study involving a stochastic data collection experiment.