5612729
Adaptive Rule Loading and Session Control for Securing Web-Delivered Services
In this paper, we present Arctic, an adaptive reinforcement learning control technique for web intrusion check. A rule-based model is designed to describe the requirement of vulnerability detection. The whole validation rule set is divided into multiple sections,and each can be enabled in either in-line control mode or off-line monitoring mode based on the observation and analysis of user behaviors, balancing security and system cost. For the different sizes of in-line validation rules, we use the reinforcement learning technique to adjust the session admission control, maintaining the response time in an acceptable level as well as maximizing the utilization of system resources. We design a runtime protection mechanism using a HTTP session listener and servlet filters in the J2EE container to intercept HTTP requests and responses. Preliminary results of our implementation are presented in this paper.
2930673
Adaptive Security Dialogs for Improved Security Behavior of Users
Despite the increasing awareness of the importance of security for daily computer users, we see that many users still fail to behave securely when confronted with a security-related decision. In this paper, we introduce a new approach to security-related dialogs called Adaptive Security Dialogs (ASD). This approach is a combination of a new architecture and a new way of interacting with users to provide them with appropriate and effective security dialogs. ASD realizes this goal by matching the complexity and intrusiveness of security-related dialogs to the risk associated with the decision the user is making. This results in an architecture in which users can focus on their tasks, get (immediate) feedback on their decisions, and interact with dialogs with an appropriate complexity and appearance for the decision's associated risk. This paper makes the following three contributions. First, we introduce a general architecture for handling security-related decisions. Second, through an empirical user study using a web-based e-mail client, we show significant improvement in the care exercised by our participants without sacrificing usability. Third, we describe how the different pieces of existing research fit into the bigger picture of improving users' behavior.
2325109
Exploiting upper and lower bounds in top-down query optimization
System R's bottom-up query optimizer architecture forms the basis of most current commercial database managers. The paper compares the performance of top-down and bottom-up optimizers, using the measure of the number of plans generated during optimization. Top down optimizers are superior according to this measure because they can use upper and lower bounds to avoid generating groups of plans. Early during the optimization of a query, a top-down optimizer can derive upper bounds for the costs of the plans it generates. These bounds are not available to typical bottom-up optimizers since such optimizers generate and cost all subplans before considering larger containing plans. These upper bounds can be combined with lower bounds, based solely on logical properties of groups of logically equivalent subqueries, to eliminate entire groups of plans from consideration. We have implemented such a search strategy, in a top-down optimizer called Columbia. Our performance results show that the use of these bounds is quite effective, while preserving the optimality of the resulting plans. In many circumstances this new search strategy is even more effective than heuristics such as considering only left deep plans.
5930637
Parallelization of IBM mambo system simulator in functional modes
Mambo [4] is IBM's full-system simulator which models PowerPC systems, and provides a complete set of simulation tools to help IBM and its partners in pre-hardware development and performance evaluation for future systems. Currently Mambo simulates target systems on a single host thread. When the number of cores increases in a target system, Mambo's simulation performance for each core goes down. As the so-called "multi-core era" approaches, both target and host systems will have more and more cores. It is very important for Mambo to efficiently simulate a multi-core target system on a multi-core host system. Parallelization is a natural method to speed up Mambo under this situation.
 Parallel Mambo (P-Mambo) is a multi-threaded implementation of Mambo. Mambo's simulation engine is implemented as a user-level thread-scheduler. We propose a multi-scheduler method to adapt Mambo's simulation engine to multi-threaded execution. Based on this method a core-based module partition is proposed to achieve both high inter-scheduler parallelism and low inter-scheduler dependency. Protection of shared resources is crucial to both correctness and performance of P-Mambo. Since there are two tiers of threads in P-Mambo, protecting shared resources by only OS-level locks possibly introduces deadlocks due to user-level context switch. We propose a new lock mechanism to handle this problem. Since Mambo is an on-going project with many modules currently under development, co-existence with new modules is also important to P-Mambo. We propose a global-lock-based method to guarantee compatibility of P-Mambo with future Mambo modules.
 We have implemented the first version of P-Mambo in functional modes. The performance of P-Mambo has been evaluated on the OpenMP implementation of NAS Parallel Benchmark (NPB) 3.2 [12]. Preliminary experimental results show that P-Mambo achieves an average speedup of 3.4 on a 4-core host machine.
2191083
A Model of Context Awareness Agent System Based on Dynamic Fuzzy Logic
One of the main challenges of context-aware computing is how to elicit and represent complex, context-dependent requirements, and use the resulting representation within context-aware applications to support decision-making processes. In this paper, we describe context information with dynamic fuzzy logic and present an Agent-based context-aware system model. Unfortunately, there are often many errors that interfere in the Agent sensors, the process of each Agent communication and the sharing context information of each Agent. In order to robust the context awareness Agent system, we handle these errors with rewards mechanism, so that they do not interfere in the context information learning process. We consider an Agent decider that predicts stock price changes and uses its predictions for selecting actions. The simulation results demonstrate the validity and effectiveness of the proposed algorithm.
14242473
An Experimental Investigation of Wideband MIMO Channel Based on Indoor Hotspot NLOS Measurements at 2.35GHz
In this paper, measurement data from wideband multiple-input multiple-output (MIMO) channel measurements at 2.35 GHz are presented. Measurements were preformed in indoor hotspot scenario with no line of sight (NLOS). Space-alternating generalized expectation (SAGE) algorithm was utilized to estimate angular domain parameters and powers of multipath components (MPCs), which are then used to compute the circular angular spread (CAS) and to construct the power azimuth spectrum (PAS) of transmitter and receiver sides. It is found that PASs of both sides follow multi-cluster truncated Laplacian distribution. The spatial correlation, capacity and multiplexing gain are also presented. Due to rich scatters in the indoor hotspot environment, the correlation space is Q.2lambda and the multiplexing gain is 15, which indicate indoor hotspot MIMO channels facilitate multiplexing and could achieve high capacity. The results provide important basis for future channel modeling and technique evaluation.
52849739
Inverse System Decoupling Control for Induction Motor Based on Neural Network On-Line Learning
The induction motor is a MIMO, nonlinear and high coupling system. The reversibility of the induction motor is testified. Consequently, a pseudo-linear system is completed by constructing a neural network inverse (NNI) system and combining it with the motor system. The inverse can transform the MIMO nonlinear system into two SISO linear subsystems (i.e., rotor speed and flux subsystems). In order to approach the inversion exactly in operation of the motor, the control method online learning based on NNI system is proposed, in which connection value can be amended continuously on-line to make the NN adapt to the changes of environment to strengthen its robustness. Simulation and experiment results have shown that NN can be adjusted in the control process. The good applicability of NN along with the strong stability and robustness of the system can be achieved by using the proposed method.
18825124
A Trajectory Clustering Algorithm Based on Symmetric Neighborhood
Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not discover small and dense clusters and be sensitive to two input parameters. Based on the partition-and-group framework, we propose a simple but effective trajectory clustering algorithm based on symmetric neighborhood named BSNTC, which needs only one input parameter which eases the sensitivity of parameters in a certain extent. The proposed measure considers both neighbors and reverse neighbors of trajectories when estimating its density distribution. Also, we use an accumulator without calculating influence outlier of each trajectory to reduce the computation cost and corresponding storage cost. A comprehensive performance evaluation and analysis shows that our method is not only efficient in the computation but also effective in arbitrary shape and different densities trajectory databases.
17747144
Distributed access control in CROWN groups
Security in collaborative groups is an active research topic and has been recognized by many organizations in the past few years. In this paper, we propose a fine-grained and attribute-based access control framework for our key project, CROWN grid. To avoid single point of failure and enhance scalability of the system, we employ a distributed delegation authorization mechanism. We successfully implement our proposed access control in CROWN grid, and evaluate this approach by comprehensive experiments.