591 research outputs found

    Artificial intelligence (AI) methods in optical networks: A comprehensive survey

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    Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

    Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

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    As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

    Multi-paradigm frameworks for scalable intrusion detection

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    Research in network security and intrusion detection systems (IDSs) has typically focused on small or artificial data sets. Tools are developed that work well on these data sets but have trouble meeting the demands of real-world, large-scale network environments. In addressing this problem, improvements must be made to the foundations of intrusion detection systems, including data management, IDS accuracy and alert volume;We address data management of network security and intrusion detection information by presenting a database mediator system that provides single query access via a domain specific query language. Results are returned in the form of XML using web services, allowing analysts to access information from remote networks in a uniform manner. The system also provides scalable data capture of log data for multi-terabyte datasets;Next, we address IDS alert accuracy by building an agent-based framework that utilizes web services to make the system easy to deploy and capable of spanning network boundaries. Agents in the framework process IDS alerts managed by a central alert broker. The broker can define processing hierarchies by assigning dependencies on agents to achieve scalability. The framework can also be used for the task of event correlation, or gathering information relevant to an IDS alert;Lastly, we address alert volume by presenting an approach to alert correlation that is IDS independent. Using correlated events gathered in our agent framework, we build a feature vector for each IDS alert representing the network traffic profile of the internal host at the time of the alert. This feature vector is used as a statistical fingerprint in a clustering algorithm that groups related alerts. We analyze our results with a combination of domain expert evaluation and feature selection

    QoS Provision for Wireless Sensor Networks

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    Wireless sensor network is a fast growing area of research, receiving attention not only within the computer science and electrical engineering communities, but also in relation to network optimization, scheduling, risk and reliability analysis within industrial and system engineering. The availability of micro-sensors and low-power wireless communications will enable the deployment of densely distributed sensor/actuator networks. And an integration of such system plays critical roles in many facets of human life ranging from intelligent assistants in hospitals to manufacturing process, to rescue agents in large scale disaster response, to sensor networks tracking environment phenomena, and others. The sensor nodes will perform significant signal processing, computation, and network self-configuration to achieve scalable, secure, robust and long-lived networks. More specifically, sensor nodes will do local processing to reduce energy costs, and key exchanges to ensure robust communications. These requirements pose interesting challenges for networking research. The most important technical challenge arises from the development of an integrated system which is 1)energy efficient because the system must be long-lived and operate without manual intervention, 2)reliable for data communication and robust to attackers because information security and system robustness are important in sensitive applications, such as military. Based on the above challenges, this dissertation provides Quality of Service (QoS) implementation and evaluation for the wireless sensor networks. It includes the following 3 modules, 1) energy-efficient routing, 2) energy-efficient coverage, 3). communication security. Energy-efficient routing combines the features of minimum energy consumption routing protocols with minimum computational cost routing protocols. Energy-efficient coverage provides on-demand sensing and measurement. Information security needs a security key exchange scheme to ensure reliable and robust communication links. QoS evaluation metrics and results are presented based on the above requirements

    Real-time Power Management of Hybrid Power Systems in All Electric Ship Applications.

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    Motivated by the need for achieving flexible shipboard arrangement and meeting future on-board power demand, the concept of all-electric ships (AES) has been pursued. The integrated power systems enable this initiative by providing a common electrical platform for the propulsion and ship-service loads and are a classic example of hybrid power systems (HPS). In order to leverage the complementary dynamic characteristics of the diverse sources, effective power management (PM) is essential to coordinate the sources and energy storage to achieve efficient power generation and fast load following. Although extensive research has been done on the PM of hybrid land vehicles for commercial applications, this problem for shipboard military applications remains largely unaddressed, leading to its exclusive focus in this dissertation. While HPS brings in many opportunities for power management, there are many associated challenges for systems used in military applications since both performance as well as survivability criteria have to be satisfied. While the on-demand goal for the power management problem makes real-time control a key requirement, leveraging the look-ahead opportunities for the shipboard missions makes it difficult to attain this goal. Furthermore, the nonlinearity and the complexity of hybrid power systems, make the optimal control of HPS challenging. In this dissertation, we address real-time power management for the AES and general hybrid power systems targeting military applications. The central theme of this work is the development of power management schemes with real-time computational efficiency by exploring HPS dynamic properties, for improved performance (namely fuel economy and fast load following) during normal mode conditions as well as increased survivability during component failure. A reduced order dynamic HPS model and a scaled test bed is developed as a numerical tool for controller design and validation. The power management (PM) schemes for both normal as well as failure mode conditions are proposed and implemented on a real-time simulator which demonstrated the real-time performance of the proposed method. While the normal mode PM leverages the complementary dynamic characteristics of the HPS for real-time look-ahead control and performance, the failure mode PM uses a reference governor approach for real-time constraint enforcement.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77863/1/gseenuma_1.pd

    Semantic-based adaptive mission planning for unmanned underwater vehicles

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    Current underwater robotic platforms rely upon waypoint-based scripted missions which are described by the operator a-priori. This renders systems incapable of reacting to the unexpected. In this thesis, we claim that the ability to autonomously adapt the decision making process is the key to facilitating the change over from human intervention to intelligent autonomy. We identify goal-based declarative mission planning as an attractive solution to autonomous adaptability because it combines autonomous decision making with higher levels of human interaction. Goal-based mission planning requires the use of abstract knowledge representation and situation awareness to link the prior knowledge provided by the operator with the information coming from the processed sensor data. To achieve this, we propose a semantic-based knowledge representation framework that allows this integration of prior and processed information among all different agents available in the platform. In order to evaluate adaptive mission planning techniques, we also introduce a novel metric which measures the proximity between plans. We demonstrate that this metric is better informed than previous metrics for measuring the adaptation process. In this thesis we implement three different approaches to goal-based mission planning in order to investigate which approach is most appropriate under different circumstances. The first approach, continuous mission planning, focusses on long-term deployment. This approach is based on a continuous re-assessment of the status of the mission environment. Using our proximity metric, we evaluated this approach and show that there is a high degree of similarity between our approach and the humanly driven adaptation, both in a known static environment and in a partially-known dynamic discoverable environment. The second, service-oriented mission planning, makes use of the semantic framework to provide autonomous mission planning for the dynamic discovery of the services published by the different agents in the system. This allows platform independence, easing the manual creation of mission plans, and robustness to changes. We show that this approach produces the same plans as the baseline which was explicitly provided with the platform configuration. The last approach, mission plan repair, handles the scenario where small changes occur in the mission environment and there are limited resources for planning. We develop and deploy a mission plan repair approach within a semantic-based autonomous planning system in a real underwater vehicle. Experiments demonstrate that the integrated system is capable of providing mission adaptation for maintaining the operability of the host platform in the face of unexpected events
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