Scalable P2P bot detection system based on network data stream

P2P Botnets are one of the most malevolent threats to the Internet users due to their resiliency against takedown efforts. In this paper, we propose a bot detection system that is capable of detecting stealthy bots in a network. This system treats network traffic as a data stream, segregating the traffic into two parallel streams. The detection is based on failure traffic and communication traffic. The traffic is analyzed during small time window, and the infected hosts are reported immediately. The network administrator can monitor the status of hosts in the network and can take the necessary action before the infected hosts harm the system or can involve in the attacks. Experiments and evaluation of the proposed system on a variety of P2P data transfer applications and P2P botnets have demonstrated high accuracy of detection. The scalability of the proposed system is exhibited through its implementation on Hadoop MapReduce.     

Trading privacy with incentives in mobile commerce: A game theoretic approach

In mobile commerce, companies provide location based services to mobile users, who report their locations with a certain level of granularity to maintain a degree of anonymity. This level of granularity depends on their perceived risk as well as the incentives they receive in the form of monetary benefits or improved mobile services. This paper formulates a quantitative model in which information theoretic metrics such as entropy, quantify the anonymity level of mobile users. The individual perceived risks of users and the benefits they obtain are defined as functions of their chosen location information granularity. The interaction between the mobile commerce company and its users is investigated using mechanism design techniques as a privacy game. The user best responses and optimal strategies for the company are derived under budgetary constraints on incentives, which are provided to users in order to convince them to share their private information at the desired level of granularity. Information limitations in the system are analyzed to capture more realistic scenarios where the companies do not have access to user utility functions. Iterative distributed algorithm and regression learning methods are investigated to design mechanisms that overcome these limitations. The results obtained are demonstrated with a numerical example and simulations based on real GPS data.     

Geometry based pre-processor for parallel fluid dynamic simulations using a hierarchical basis

The pre-processing stage of finite element analysis of the Navier–Stokes equations is becoming increasingly important as the desire for more general boundary conditions, as well as applications to parallel computers increases. The set up of general boundary conditions and communication structures for parallel computations should be accomplished during the pre-processing phase of the analysis, if possible, to ensure efficient computations for large scale problems in computational fluid dynamics. This paper introduces a general methodology for geometry based boundary condition application and pre-computing of parallel communication tasks. A. K. Karanam was supported by NSF Grant No. 9985340. C. H. Whiting was supported by a grant from NASA LaRC.     

An algorithm for recognizing palm polygons

A polygonP is said to be apalm polygon if there exists a pointxP such that the Euclidean shortest path fromx to any pointyP makes only left turns or only right turns. The set of all such pointsx is called thepalm kernel. In this paper we propose an O(E) time algorithm for recognizing a palm polygonP, whereE is the size of the visibility graph ofP. The algorithm recognizes the given polygonP as a palm polygon by computing the palm kernel ofP. If the palm kernel is not empty,P is a palm polygon.The extended abstract of this paper was reported at the Second Canadian Conference in Computational Geometry, pp. 246–251, 1990     

Welfare of calves - 2. Increase in vertebral artery blood flow following exsanguination by neck sticking and evaluation of chest sticking as an alternative slaughter method

The role of the vertebral arteries in delaying loss of sensibility following neck sticking in slaughter calves was investigated. Vertebral artery blood flow was measured using probes before, during and after electrical stunning and slaughter. Systemic blood pressure, electrocorticogram, visually evoked responses and the occurrence of carotid occlusions were also recorded. When carotid occlusion occurred, the time to onset of brain failure was delayed based on the development of an isoelectric state. In addition, when carotid occlusion occurred the mean arterial blood pressure was sustained for longer following slaughter, and concurrently vertebral artery blood flow could be maintained at about 30% of its initial level for up to 3 min. In some animals vertebral artery flow increased substantially following sticking. When chest sticking was used no occlusion of vessels occurred, mean arterial blood pressure fell promptly (within 8 s) and the onset of an isoelectric state did not extend beyond one minute. In addition, visually evoked responses were not present after 5 s following chest sticking.     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

An Orthogonal frequency part multiplexing suffers from a considerable challenge due to a high peak to average power ratio (PAPR). Hence, an effective method such as partial transmits sequence (PTS) can avert this defiance by limit the design of PAPR. Therefore, an improving PAPR reduction performance via a novel approach is proposed by detaching each subblock into two parts furthermore exchanges the first sample with the final selection in each portion of the subblock to generate a new partitioning scheme. Several typical traditional segmentation schemes are used to analyze and apply the presented algorithm, such as adjacent, interleaving, and pseudo-random schemes. Besides, two scenarios are adopted based on simulation software in which the number of subcarriers is set to 128 and 256. Based on the results, a superior PAPR reduction performance is achieved based on the improved segmentation schemes regarding traditional strategies in both systems. Moreover, the enhanced adjusted PTS scheme poses a low computational complexity compared with that of the conventional schemes.