With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

ACM SIGCOMM computer communication review

On nonsingular terminal sliding-mode control of nonlinear systems

Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey’s structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks.

A comprehensive survey on network anomaly detection

The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters’ increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a promising solution. However, the instability of renewable energy brings about a new challenge since insufficient energy supply may lead to job running interruptions or failures. Though previous works attempt to more accurately predict the amount of produced renewable energy, due to the instability of its influencing factors (e.g., wind, temperature), sufficient renewable energy supply cannot be always guaranteed. To handle this problem, in this paper, we propose allocating jobs with the same service-level-objective (SLO) level to the same physical machine (PM) group, and power each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. It ensures that insufficient renewable energy supply will not lead to SLO violations. We use a deep learning technique to predict the probability of producing amount no less than each value of each renewable energy source and predict the energy demands of each PM area. We formulate an optimization problem: how to match renewable energy resources with different instabilities to different PM groups as energy supply in order to minimize the number of SLO violations (due to interruption from insufficient renewable energy supply), total energy monetary cost and total carbon emission. We then use reinforcement learning method and linear programming method to solve the optimization problem. The real trace driven experiments show that our method can achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods.

Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter

Network anomaly detection is a critical aspect of network management for instance for QoS, security, etc. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Most network anomaly detection systems proposed so far employ a supervised strategy to accomplish the task, using either signature-based detection methods or supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods), the latter requires training and labelled traffic, which is difficult and expensive to produce. Such limitations impose a serious bottleneck to the previously presented problem. We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labelled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of the unsupervised detection is also performed for improving the detection robustness. Characterization is achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances of the unsupervised approach are evaluated on real network traffic.

Sub-space clustering, inter-clustering results association & anomaly correlation for unsupervised network anomaly detection

The behavior of Wireless Sensor Networks (WSN) is nowadays widely analyzed. One of the most important issues is related to their energy consumption, as this has a major impact on the network lifetime. Another important application requirement is to ensure data sensing synchronization, which leads to additional energy consumption as a high number of messages is sent and received at each node. Our proposal consists in implementing a combined synchronization protocol based on the IEEE 1588 standard that was designed for wired networks and the PBS (Pairwise Broadcast Synchronization) protocol that was designed for sensor networks, as none of them is able to provide the needed synchronization accuracy for our application on its own. The main goals of our new synchronization protocol are: to ensure the accuracy of local clocks up to a tenth of a microsecond and to provide an important energy saving. Our results obtained using NS-2 (Network Simulator) show that the performance of our solution (IEEE 1588-PBS) matches our application requirements with regard to the synchronization, with a significant improvement in energy saving.

https://hal.archives-ouvertes.fr/hal-00544972/document

An energy-efficient clock synchronization protocol for Wireless Sensor Networks

https://hal.archives-ouvertes.fr/hal-01930363/document

Green energy efficient scheduling management

This paper deals with the issue of anomaly detection in TCP/IP networks based on a control theory approach. Using a previously developed sliding mode observer, an improvement of the anomaly detection and reconstruction is proposed. More specifically, the ability of distinguishing false/true positives and false/true negatives in a prescribed finite time is ensured thanks to the design of an unknown input observer combined to some low pass filters. A high Quality of Service (QoS) is thus guaranteed to the network. To elucidate the proposed method, a network topology is then tested via Simulink as well as via the Network Simulator NS-2. Finally, detailed results analysis confirm the enhancement brought to the detection of an anomaly flowing through the network.

An unknown input sliding observer for anomaly detection in TCP/IP networks

The use of the control theory tools for traffic control in communication networks, for example, the congestion control in internet protocol (IP) routers, has given rise to challenging issues in the time-delay system framework. In this study, the authors propose to design a linear time-delay observer for traffic monitoring in transmission control protocol/active queue management (TCP/AQM) networks. More precisely, the authors focus on a bottleneck topology consisting of long-lived TCP communications through a router. The developed mechanism, located at the router, aims at supervising the network via TCP flow estimations as well as detecting a class of anomalies. This issue is formulated as a stability problem for multiple delayed systems and appropriate robust control tools such as quadratic separation are adopted to address it. Then, some simulations via the network simulator NS-2 and an emulation experiment support the proposed methodology.

https://hal.archives-ouvertes.fr/hal-01229615/document

Yann Labit

Papers

Sub-space clustering, inter-clustering results association & anomaly correlation for unsupervised network anomaly detection

An energy-efficient clock synchronization protocol for Wireless Sensor Networks

Green energy efficient scheduling management

An unknown input sliding observer for anomaly detection in TCP/IP networks

Traffic monitoring in transmission control protocol/active queue management networks through a time-delay observer