It is important to avoid information flow from higher levels to lower levels directly in multilevel systems, namely the so called covert channels. Primary studies of information flow analysis were accomplished based o...
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It is important to avoid information flow from higher levels to lower levels directly in multilevel systems, namely the so called covert channels. Primary studies of information flow analysis were accomplished based on time and probability factors. It is well known that systems that are considered to be secure may turn out to be insecure when time or probability is considered. Recently, both probabilistic and timing covert channels have been studied. However, with transitive noninterference formulation, it is difficult to deal with intransitive flow policies like channel control and secure downgrading of information with probabilistic and time constraints. In this paper, we introduce the notion of trust domain into Probabilistic Timed Secure Process Algebra (ptSPA), extending intransitive noninterference to probabilistic and time systems. Based on weak probabilistic and timed bisimulation equivalence, some security properties for intransitive flow are reformulated in a probabilistic and time setting, in particular one property which is persistent, meaning that if a system is secure then all of its reachable states are secure too. Finally, we provide one case study showing that it is possible to model and analyze the probabilistic and time system through our approach.
Conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. However, in many anomaly detection application, we can only get a class samples (commonly, norm...
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Conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. However, in many anomaly detection application, we can only get a class samples (commonly, normal samples) to train detectors. This paper presents an improved negative selection algorithm (NSA) and its applications to anomaly detection. This approach can only use normal samples to train detectors;at the same time, in order to overcome the drawbacks of the current representation of the self-space in NSAs, a new scheme of the representation of the self-space is introduced with variable-sized self-radius. In our experiments, this approach is tested using the wellknown real world datasets;preliminary results show that the new approach improves the overall performance of detectors and without increase in complexity.
The nonlinear features can be extracted by the kernel methods. The features play the important roles in pattern recognition and image processing. The researching on kernel methods has the great theoretical and practic...
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The nonlinear features can be extracted by the kernel methods. The features play the important roles in pattern recognition and image processing. The researching on kernel methods has the great theoretical and practical value. In the paper, we propose a method to calculate the pre-images using the kernel methods. And we specifically design an algorithm to obtain the pre-image based on the kernel PCA with Gaussian kernel. In the algorithm, the constructed optimal problem is solved by the iterative method. The method is available to all the learning problems with a large sample set. We use the method to detect the defects on the tickets' surface. The experiments results indicate that the method not only reduce the mistake rate but also cut down the detection time.
Two analytical drain current models for organic thin-film transistors(OTFTs)are *** first one is the surface potential based drain current model considering both deep and tail trap states densities within the energy g...
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Two analytical drain current models for organic thin-film transistors(OTFTs)are *** first one is the surface potential based drain current model considering both deep and tail trap states densities within the energy gap,and the model is valided in both subthreshold and above-threshold *** second one is the polynomial based model valided in above-threshold regime,and the model has the advantage in the simple parameter extraction steps by fitting the experimental effective mobility with the polynomial *** presented models are verified with the experimental data, and good agreements are obtained.
In this paper, the feasibility of co-channel coexistence of Time Division- Synchronous Code Division Multiple Access (TD-SCDMA) and Code Division Multiple Access 2000 (CDMA2000) systems operating in a macro cell envir...
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Detecting unknown malicious executables is a challenging task. Traditional anti-virus systems use signatures to detect malicious executables. However, the method cannot detect unseen instances or variants. Inspired by...
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The goal of this research is a proposal of a standard model for vehicle system simulations. Recently, the CO 2 reduction and efficiency improvement of the energy consumption are important subjects in development of t...
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The goal of this research is a proposal of a standard model for vehicle system simulations. Recently, the CO 2 reduction and efficiency improvement of the energy consumption are important subjects in development of the vehicle systems. The new technologies related to vehicle development have continued to increase in complexity. For these reasons, the energy management simulation by the coordinated design of an electric system and vehicle system is required. The electrical behavior of the batteries becomes important to improve the energy management in the vehicle systems. The battery modeling technologies and the application example for vehicle system simulation are reported.
The performance of an fingerprint recognition system is measured by its accuracy in recognition. For a feature-based fingerprint recognition system, the accuracy is heavily depend on the chosen feature set. A fingerpr...
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Finding road intersections in advance is crucial for navigation and path planning of moving autonomous vehicles, especially when there is no position or geographic auxiliary information available. In this paper, we in...
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Finding road intersections in advance is crucial for navigation and path planning of moving autonomous vehicles, especially when there is no position or geographic auxiliary information available. In this paper, we investigate the use of a 3D point cloud based solution for intersection and road segment classification in front of an autonomous vehicle. It is based on the analysis of the features from the designed beam model. First, we build a grid map of the point cloud and clear the cells which belong to other vehicles. Then, the proposed beam model is applied with a specified distance in front of autonomous vehicle. A feature set based on the length distribution of the beam is extracted from the current frame and combined with a trained classifier to solve the road-type classification problem, i.e., segment and intersection. In addition, we also make the distinction between +-shaped and T-shaped intersections. The results are reported over a series of real-world data. A performance of above 80% correct classification is reported at a real-time classification rate of 5 Hz.
The object-oriented simulation software of an adaptive optics system is used to evaluate the performance of the adaptive optics system. The software includes distorted wavefront correction and turbulence simulation. A...
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