Observability of complex systems/networks is the focus of this paper, which is shown to be closely related to the concept of contraction. Indeed, for observable network tracking it is necessary/sufficient to have one node in each contraction measured. Therefore, nodes in a contraction are equivalent to recover for loss of observability, implying that contraction size is a key factor for observability recovery. Here, using a polynomial order contraction detection algorithm, we analyze the distribution of contractions, studying its relation with key network properties. Our results show that contraction size is related to network clustering coefficient and degree heterogeneity. Particularly, in networks with power-law degree distribution, if the clustering coefficient is high there are less contractions with smaller size on average. The implication is that estimation/tracking of such systems requires less number of measurements, while their observational recovery is more restrictive in case of sensor failure. Further, in Small-World networks higher degree heterogeneity implies that there are more contractions with smaller size on average. Therefore, the estimation of representing system requires more measurements, and also the recovery of measurement failure is more limited. These results imply that one can tune the properties of synthetic networks to alleviate their estimation/observability recovery. ! 2. See [1], [6], [15] for extension to nonlinear case. 3. It should be noted that structural observability and graph theoretic method applied as a tool to solve network observability problem. See reference [1], [6] for more information.4. Note that many of stated references deal with dual problem of network controllability. The graph properties and notions can be simply redefined for network observability.
Single time-scale distributed estimation of dynamic systems via a network of sensors/estimators is addressed in this letter. In single time-scale distributed estimation, the two fusion steps, consensus and measurement exchange, are implemented only once, in contrast to, e.g., a large number of consensus iterations at every step of the system dynamics. We particularly discuss the problem of failure in the sensor/estimator network and how to recover for distributed estimation by adding new sensor measurements from equivalent states. We separately discuss the recovery for two types of sensors, namely α and βsensors. We propose polynomial order algorithms to find equivalent state nodes in graph representation of system to recover for distributed observability. The polynomial order solution is particularly significant for large-scale systems.
The rapid growth of distributed computing systems that heavily communicate and interact with each other has raised the importance of confrontation against cyber intruders, attackers, and subversives. With respect to the emergence of cloud computing and its deployment all over the world, and because of its distributed and decentralized nature, a special security requirement is needed to protect this paradigm. Intrusion detection systems could differentiate usual and unusual behaviors by means of supervising, verifying, and controlling the configurations, log files, network traffic, user activities, and even the actions of different processes by which they could add new security dimensions to the cloud computing systems. The position of the intrusion detection mechanisms in cloud computing systems as well as the applied algorithms in those mechanisms are the 2 main factors in which many researches have focused on. The goal of those researches is to uncover intrusions as much as possible and to increase the rate and accuracy of detections while reducing the false warnings. Those solutions, however, mainly have high computational loads, low accuracy, and high implementation costs. In this paper, we present a comprehensive and accurate solution to detect and prevent intrusions in cloud computing systems by using a hybrid method, called HIDCC. The implementation results of the proposed method show that the intrusion coverage, intrusion detection accuracy, reliability, and availability in cloud computing systems are considerably increased, and false warnings are significantly reduced. KEYWORDS cloud computing, intrusion detection systems, signature-based detection, Snort, unusual behavior based detection, warning management
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