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.
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.
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.
17429523
A Novel Trust-based Cooperative Node Selection Scheme in Wireless Sensor Networks
For communication between the cluster head node (CHN) and the distant data fusion center (DFC) with the cooperation of intra-cluster nodes in wireless sensor networks (WSNs), reliable handling of packets by the cooperative node (CN) is difficult to ensure. In this paper, we propose a novel CN selection scheme based on trust for WSNs. This scheme can choose one of high trust nodes to assist in the transmission of CHN, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. First, we establish a trust model to improve the reliability of transmission by CN in WSNs, which is based on both the direct trust and the indirect trust of nodes. Then the CHN selects a node based on the proposed trust model to aid the CHN in transmission. Simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.
21919804
Logical Relations for Dynamic Name Creation
Pitts and Stark’s nu-calculus is a typed lambda-calculus which forms a basis for the study of interaction between higher-order functions and dynamically created names. A similar approach has received renewed attention recently through Sumii and Pierce’s cryptographic lambda-calculus, which deals with security protocols. Logical relations are a powerful tool to prove properties of such a calculus, notably observational equivalence. While Pitts and Stark construct a logical relation for the nu-calculus, it rests heavily on operational aspects of the calculus and is hard to be extended. We propose an alternative Kripke logical relation for the nu-calculus, which is derived naturally from the categorical model of the nu-calculus and the general notion of Kripke logical relation. This is also related to the Kripke logical relation for the name creation monad by Goubault-Larrecq et al. (CSL’2002), which the authors claimed had similarities with Pitts and Stark’s logical relation. We show that their Kripke logical relation for names is strictly weaker than Pitts and Stark’s. We also show that our Kripke logical relation, which extends the definition of Goubault-Larrecq et al., is equivalent to Pitts and Stark’s up to first-order types; our definition rests on purely semantic constituents, and dispenses with the detours through operational semantics that Pitts and Stark use.
206504813
Effective Filtering Scheme against RREQ Flooding Attack in Mobile Ad Hoc Networks
In Mobile Ad hoc Networks (MANET), various types of Denial of Service Attacks (DoS) are possible because of the inherent limitations of its routing protocols. The attack of initiating / forwarding fake Route Requests (RREQs) can lead to hogging of network resources and hence denial of service to genuine nodes. This type of attack is hard to detect since malicious nodes mimic normal nodes in all aspects except that they do route discoveries much more frequently than the other nodes. A distrusted filtering mechanism is proposed to mitigate such situations and reduce the loss of throughput. The proposed mechanism could prevent this specific kind of DoS attack and does not use any additional network bandwidth.
39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
12263549
Tree-guided Bayesian inference of population structures
MOTIVATION
Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.


RESULT
We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/tips.tar.