17509795
Collaborative attacks in WiMAX networks
Summary In this paper, we discuss security problems, with a focus on collaborative attacks, in the Worldwide Interoperability for Microwave Access (WiMAX) scenario. The WiMAX protocol suite, which includes but is not limited to DOCSIS, DES, and AES, consists of a large number of protocols. We present briefly the WiMAX standard and its vulnerabilities. We pinpoint the problems with individual protocols in the WiMAX protocol suite, and discuss collaborative attacks on WiMAX systems. We present several typical WiMAX attack scenarios, including: bringing a large number of attackers to increase their computation power and break WiMAX protocols; assembling a sufficient number of attackers to influence the decision-making of core machines, which includes routing attacks and Sybil attacks; and exploiting implementations that do not conform to the WiMAX specification completely, causing interoperability problems among various protocols, including the ones in typical WiMAX/WiFi/LAN deployment scenarios. We present theoretical models and practical solutions to profile, model, and analyze collaborative attacks in WiMAX. We employ attack graphs to do vulnerability analysis. Experimental results verify our models and validate our analysis. Copyright © 2009 John Wiley & Sons, Ltd.
6059000
Preserving User Location Privacy in Mobile Data Management Infrastructures
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user's location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.
13946371
Self-Learning Disk Scheduling
Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: change-sensing Round-Robin, feedback learning, per-request learning, and two-layer learning. experiments show that the novel two-layer learning scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the two-layer learning scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: decision tree, logistic regression, naive Bayes, neural network, and support vector machine algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.
15016600
Addressing Collaborative Attacks and Defense in Ad Hoc Wireless Networks
Since ad hoc networks rely on nodes cooperation to establish communication, malicious nodes can compromise the entire network. If they collaborate the devastation is even worse. Collaborative attacks may cause more devastating impacts on wireless environments than single and uncoordinated groups of attacks, as they combine efforts of more than one attacker against the target victim. In this paper, we present the most important forms of attacks,discuss possible collaborations among attackers, show how machine learning techniques and signal processing techniques can be used to detect and defend against collaborative attacks in such environments, and discuss implementation issues.
1039556
Indexing Noncrashing Failures: A Dynamic Program Slicing-Based Approach
Recent software systems usually feature an automated failure reporting component, with which a huge number of failures are collected from software end-users. With a proper support of failure indexing, which identifies failures due to the same fault, the collected failure data can help developers prioritize failure diagnosis, among other utilities of the failure data. Since crashing failures can be effectively indexed by program crashing venues, current practice has seen great success in prioritizing crashing failures. A recent study of bug characteristics indicates that as excellent memory checking tools are widely adopted, semantic bugs and the resulting noncrashing failures have become dominant. Unfortunately, the problem of how to index non-crashing failures has not been seriously studied before. In previous study, two techniques have been proposed to index noncrashing failures, and they are T-Proximity and R-Proximity. However, as T-Proximity indexes failures by the profile of the entire execution, it is generally not effective because most information in the profile is fault-irrelevant. On the other hand, although R-Proximity is more effective than T-Proximity, it relies on a sufficient number of correct executions that may not be available in practice. In this paper, we propose a dynamic slicing-based approach, which does not require any correct executions, and is comparably effective as R-Proximity. A detailed case study with gzip is reported, which clearly strates the advantages of the proposed approach.
4714436
Notice of RetractionCost Comparison and Control Based on GLM and Panel Data Model: Evidence from Major Citrus Producing Areas in China
All the major citrus producing areas in China are facing the intensive competition from international market. The problem of how to find an efficient path to decrease citrus production cost by analyzing production cost of citrus and how to promote effective cooperation between regions to deal with the pressure of international market through integrating resources of the regions, has become an important problem that all the major citrus producing areas focus on. The paper firstly compares citrus production cost of the major citrus producing areas, and then reveals the reason causing cost differentiation in the central and eastern areas according to general linear model and panel data, finally proposes several suggestions to control citrus production cost.
41660705
Fault Diagnosis on Satellite Attitude Control with Dynamic Neural Network
With increasing demands on higher performance, more safety and reliability of dynamic systems, especially on safety-critical systems, fault diagnosis became a research interests in recent years. In this paper, a systematic approach to design fault diagnosis and accommodation with compound approach such as applying improved robust observer to fault diagnosis on linearized system aided by dynamic neural network, which is trained to bridge the gap between simulated system and real system on nonlinear attributes and modeling errors. Using an instance of fault diagnosis on attitude control system of satellite attitude, advantages of new scheme are tested to be effective.
16765186
Insider DoS Attacks on Epidemic Propagation Strategies of Network Reprogramming in Wireless Sensor Networks
Network reprogramming is a crucial service in wireless sensor networks (WSNs) that relies on epidemic strategy for spreading software updates by just having a local view of the networks. Securing the process of network reprogramming is essential in some certain WSNs applications, state-of-the-art secure network reprogramming protocols for WSNs aim for the efficient source authentication and integrity verification of code image, however, due to the resource constrains of WSNs, existing secure network reprogramming protocols are vulnerable to Denial of Service (DoS) attacks when sensor nodes can be compromised (insider DoS attacks). In this paper, we identify different types of DoS attacks exploiting the epidemic propagation strategies used by Deluge and propose corresponding analysis models to attempt to quantify the cost of these attacks damage. Simulation further shows the impact of insider DoS attacks on network reprogramming in WSNs.
29942985
Poisson approximation for significance in genome-wide ChIP-chip tiling arrays
MOTIVATION
A genome-wide ChIP-chip tiling array study requires millions of simultaneous comparisons of hybridization for significance. Controlling the false positive rate in genome-wide tiling array studies is very important, because the number of computationally identified regions can easily go beyond the capability of experimental verification. No accurate and efficient method exists for evaluating statistical significance in tiling arrays. The Bonferroni method is overly conservative and the permutation test is time consuming for genome-wide studies.


RESULT
Motivated by the Poisson clumping heuristic, we propose an accurate and efficient method for evaluating statistical significance in genome-wide ChIP-chip tiling arrays. The method works accurately for any large number of multiple comparisons, and the computational cost for evaluating P-values does not increase with the total number of tests. Based on a moving window approach, we demonstrate how to combine results using various window sizes to increase the detection power while maintaining a specified type I error rate. We further introduce a new false discovery rate control that is more appropriate in measuring the false proportion of binding intervals in tiling array analysis. Our method is general and can be applied to many large-scale genomic and genetic studies.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/pass.tar
2679883
Observation-based proactive communication in team cooperation
Observation and communication are important aspects of agent teamwork. We employ the agent's observability as the major means for individual agents to reason about the environment and other team members. We focus on how to represent agents' observability and how to include it into the basic reasoning for proactive communication. The syntax and the operational semantics of observability are given. Preliminary experiments are carried out to study the effectiveness of different aspects of observability.