Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate multiple attacks satisfying the same generic exploit goals for a given vulnerability. In this work, multi-obj...
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Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate multiple attacks satisfying the same generic exploit goals for a given vulnerability. In this work, multi-objective Genetic programming is used to establish a ldquoblack-boxrdquo approach to mimicry attack generation. No knowledge is made of internal data structures of the target anomaly detector, only the anomaly rate reported by the detector. Such a ldquoblack boxrdquo methodology enables a vulnerability testing approach where both open-source and commodity anomaly detection systems can be tested. The approach successfully identifies exploits when benchmarked over four detectors and four applications.
The objective of this work is to develop and implement a fuzzy controller and fuzzy fault detection for centralized chilled water system. Both controller and fault detector are implemented in supply air dampers of air...
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The objective of this work is to develop and implement a fuzzy controller and fuzzy fault detection for centralized chilled water system. Both controller and fault detector are implemented in supply air dampers of air handling unit (AHU). A few cases are tested in this paper to investigate the effectiveness of the developed systems. All simulation is carried out using MATLAB/SIMULINK. Results illustrate that the fuzzy controller is able to maintain rooms' temperature according to desired temperature whereas the fault detection can detect unusual behavior in supply air flow rate.
In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its q...
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We present the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator. It is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. The IGOWLA operator uses ...
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ISBN:
(纸本)9781509042418
We present the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator. It is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. The IGOWLA operator uses order-induced variables that modify the reordering mechanism of the arguments to be aggregated. The main advantage of the induced process is the consideration of the complex attitude of the decision makers. We study some properties of the IGOWLA operator, such as idempotency, commutativity, boundedness and monotonicity. Finally we present an illustrative example of a group decision-making procedure using a multi-person analysis and the IGOWLA operator in the area of innovation management.
In this study, a novel method for identification and control of nonlinear systems is developed. The method proposed realizes the dynamics of a system by employing the Runge-Kutta method at the upper level. The interme...
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In this study, a novel method for identification and control of nonlinear systems is developed. The method proposed realizes the dynamics of a system by employing the Runge-Kutta method at the upper level. The intermediate level of the strategy constructs the architecture utilizing an adaptive neuro fuzzy inference system. The overall system is able to imitate the behavior of a complex dynamic system with a few rules or to control the system with high accuracy. The proposed method has been applied to a two degrees of freedom direct drive SCARA robot.
Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algo...
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ISBN:
(纸本)9781728190495
Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones. This problem has so far not been discussed in the corresponding literature. We discuss ideas how to select promising datasets and propose three very simple indicators: Estimated relative return improvement (ERI) and estimated action stochasticity (EAS), as well as a combination of the two (COI), and empirically show that despite their simplicity they can be very effectively used for dataset selection.
The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Her...
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The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Here what we mean by robustness is that the classifiers are trained on data from one network but tested on data from an entirely different network. To this end, five learning algorithms - adaboost, support vector machine, Nai¿e Bayesian, RIPPER and C4.5 - are evaluated using flow based features, where IP addresses, source/destination ports and payload information are not employed. Results indicate the C4.5 based approach performs much better than other algorithms on the identification of both SSH and Skype traffic on totally different networks.
ExSAIS: Workshop on Extreme Scaling of AI for Science, brings together researchers in Artificial intelligence (AI) and High Performance Computing (HPC) to enable scientific discovery at scale. The evolution of machine...
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ISBN:
(数字)9781665497473
ISBN:
(纸本)9781665497480
ExSAIS: Workshop on Extreme Scaling of AI for Science, brings together researchers in Artificial intelligence (AI) and High Performance Computing (HPC) to enable scientific discovery at scale. The evolution of machine perception to machine learning and reasoning, and ultimately machine intelligence, has a potential to significantly impact acceleration and advancement of autonomous scientific discovery and the operation of scientific instruments. While machine reasoning will enable intelligent systems to better understand and interact with their physical world, machine intelligence through modeling, simulation and automation, closes the gap between experiments, extreme computing, and scientific discovery. In order to usher in this new era of autonomous science, advances in several areas of artificial intelligence and other disciplines e.g., high-performance computing, data engineering need to come together.
The Advanced Remote Tower project (ART) studies enhancements to an existing LFV prototype facility for a remotely operated tower: projection on a 360 degrees panorama screen, adding synthesized geographic information ...
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ISBN:
(纸本)9783902661944
The Advanced Remote Tower project (ART) studies enhancements to an existing LFV prototype facility for a remotely operated tower: projection on a 360 degrees panorama screen, adding synthesized geographic information and meteorological information, video tracking, fusion of video and radar tracks, labelling, visibility enhancement and surveillance operations with a remotely controlled Pan Tilt Zoom camera. The ART functions have been embedded in the existing Swedish test facility for remote tower operations in Malmö airport Sturup observing Ängelholm traffic about 100 km to the North. They were tuned and validated by 15 tower controllers. Emphasis was on the traffic and situation awareness of tower controllers using remote cameras and projection system for safe operational tower control, replacing direct view on the airport and its traffic. The validation results give valuable information for further development and operational application even outside the Remote Tower application area.
In this paper, an experimental study on robust control of bending and torsional vibrations and contact force of a one-link flexible arm is discussed. The flexible arm carries a symmetric rigid tip body, of which the m...
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In this paper, an experimental study on robust control of bending and torsional vibrations and contact force of a one-link flexible arm is discussed. The flexible arm carries a symmetric rigid tip body, of which the mass center lies on the center axis of the arm. We derive dynamic equations of the joint angle, the vibrations of the flexible link, and the contact force. On the basis of a finite-dimensional modal model of a distributed-parameter system, robust controllers are constructed. Some experimental results are shown.
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