An efficient detection mechanism against packet faking attack in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In our previous novel attack (Packet Faking Attack [1]) we presented a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. In this paper, we present an efficient detection mechanism against this type of attack where each node can detect the attack instead of the destination node. Our detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea, that is, the packet creation time of each packet. Simulation results show this robust mechanism achieves a very high accuracy, detection rate and good network traffic reduction.

Catabolism attack and anabolism defense: a novel attack and traceback mechanism in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense.A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a novel attack and traceback mechanism against a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. We call this novel attack a Catabolism Attack and we call our novel traceback mechanism against this attack Anabolism Defense. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.

Crowd at the Lithographic Technical Forum

Several people are looking at sample prints in this photograph of the Lithographic Technical Forum. In the background the booth for the Lithographic Technical Foundation, Inc. can be seen. Black and white photograph.

Especulative (sic) attack on exchange rate target zone regime : the uncertainty case

We present a continuous time target zone model of speculative attacks. Contrary to most of the literature that considers the certainty case, i.e., agents know for sure the Central Bank behavior in the future, we build uncertainty into the madel in two different ways. First, we consider the case in whicb the leveI of reserves at which the central bank lets the regime collapse is uncertain. Alternatively, we ana1ize the case in which, with some probability, the government may cbange its policy reducing the initially positive trend in domestic credito In both cases, contrary to the case of a fixed exchange rate regime, speculators face a cost of launching a tentative attack that may not succeed. Such cost induces a delay and may even prevent its occurrence. At the time of the tentative attack, the exchange rate moves either discretely up, if the attack succeeds, or down, if it fails. The remlts are consistent with the fact that, typically, an attack involves substantial profits and losses for the speculators. In particular, if agents believed that the government will control fiscal imbalances in the future, or alternatively, if they believe the trend in domestic credit to be temporary, the attack is postponed even in the presence of a signal of an imminent collapse. Finally, we aIso show that the timing of a speculative attack increases with the width of the target zone.

Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack

An important challenge in the crime literature is to isolate causal effects of police on crime. Following a terrorist attack on the main Jewish center in the city of Buenos Aires, Argentina, in July 1994, all Jewish institutions (including schools, synagogues, and clubs) were given 24-hour police protection. Thus, this hideous event induced a geographical allocation of police forces that can be presumed to be exogenous in a crime regression. Using data on the location of car thefts before and after the terrorist attack, we find a large deterrent effect of observable police presence on crime. The effect is local, with little or no appreciable impact outside the narrow area in which the police are deployed.

Montagem e operação de um secador pneumático tipo flash

In this work, a pneumatic dryer has been designed and assembled in laboratory scale in order to study and evaluate configurations more efficient for application in drying of important materials of Northeast region in Brazil. The equipment was tested with drying of corn and rice grains, in conditions of temperature and air velocity at 80 oC and 35 m/s, respectively. For this type of dryer, it is recommended to work at temperatures above 200 °C and air velocity with higher dynamic pressure. However, even under operating conditions below what it is recommended, the results obtained with the pneumatic dryer were satisfactory. In addition, experiments of drying were performed by using a cabinet dryer (batch dryer) under the same conditions used in the pneumatic dryer. Flash one curves for the corn were fitted satisfactorily by applying of the Lewis model, while a better agreement was found for rice by using the Page model. The data obtained with both drying processes allowed to compare the performance between pneumatic and batch dryers. In respect to drying rate, the pneumatic dryer presented a similar performance to the batch dryer during processing with corn and a superior performance to the last one during processing with rice. Therefore, it was possible to verify that the pneumatic dryer assembled in this preliminar study can be applied for different materials and under different operating conditions

Optimisation of a continuous flash fermentation for butanol production using the response surface methodology

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.