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111.
Arun Lakhotia Andrew Walenstein Craig Miles Anshuman Singh 《Journal in Computer Virology》2013,9(3):109-123
VILO is a lazy learner system designed for malware classification and triage. It implements a nearest neighbor (NN) algorithm with similarities computed over Term Frequency $\times $ Inverse Document Frequency (TFIDF) weighted opcode mnemonic permutation features (N-perms). Being an NN-classifier, VILO makes minimal structural assumptions about class boundaries, and thus is well suited for the constantly changing malware population. This paper presents an extensive study of application of VILO in malware analysis. Our experiments demonstrate that (a) VILO is a rapid learner of malware families, i.e., VILO’s learning curve stabilizes at high accuracies quickly (training on less than 20 variants per family is sufficient); (b) similarity scores derived from TDIDF weighted features should primarily be treated as ordinal measurements; and (c) VILO with N-perm feature vectors outperforms traditional N-gram feature vectors when used to classify real-world malware into their respective families. 相似文献
112.
Manpreet Singh UCOE Manjeet Singh Patterh UCOE 《Information Security Journal: A Global Perspective》2013,22(6):332-343
ABSTRACT It is difficult to define reliable security policy components that should be applied to validate a secure computing environment. The job gets further complicated when one has to deal with multiple policies in single computing environment. This paper demonstrates how we can overcome the difficulties of defining reliable security components by using evaluation criteria. In this paper we use common criteria to derive the security functional components for a multipolicy-based network computing environment. In the verification process, the derived policy components are related to the specific security objectives of the network communication environment. The evidence listed in the case study supports the claims that the proposed network security policy interpretation framework is a complete and cohesive set of requirements. 相似文献
113.
Design of Barrages with Genetic Algorithm Based Embedded Simulation Optimization Approach 总被引:2,自引:2,他引:0
Raj Mohan Singh 《Water Resources Management》2011,25(2):409-429
Barrages are hydraulic structures constructed across rivers to divert flow into irrigation canals or power generation channels.
The most of these structures are founded on permeable foundation. The optimum cost of these structures is nonlinear function
of factors that cause the seepage forces under the structure. There is, however, no procedure to ascertain the basic barrage
parameters such as depth of sheet piles or cutoffs and the length and thickness of floor in a cost–effective manner. In this
paper, a nonlinear optimization formulation (NLOF), which consists of an objective function of minimizing total cost, is solved
using genetic algorithm (GA). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability
of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study shows drastic
cost savings when the proposed NLOF is solved using GA than that of using classical optimization technique and conventional
method. A parametric analysis has also been performed to study the effect of varying soil and hydrological conditions on design
parameters and on over all cost. 相似文献
114.
This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow. 相似文献
115.
Abstract. The Online Maximal Dense Tree problem is as follows: given a weighted directed graph and a source node, users issue online requests for connection to
the source node. A request can either be accepted or rejected (the admission control decision). If the connection request
is accepted, it must be connected to the source or to a node previously connected to the source (the routing decision). The
objective is to maximize the total number of connections while keeping the connection density , i.e. the ratio of accepted requests to the weight of the spanning tree, sufficiently high.
The primary motivation for the Maximal Dense Tree problem is the Online Capacitated Multicast admission control and routing problem. In the Online Capacitated Multicast problem, we are given a communication network with limited link capacities and a set of signal source nodes. Users generate
online requests for connection to the signal sources, and the network administrator has to make the admission control and
routing decisions. The goal of the network administrator is to maximize the total number of users connected subject to the
network capacity constraints.
The Online Maximal Dense Tree problem is also faced by a cable TV operator who wishes to connect as many customers as possible while keeping down the
amount of wiring per customer. Informally, the Online Maximal Dense Tree algorithm must ``gamble' on certain geographic areas, connecting nodes which are unprofitable to start with, in the hope
that eventually enough requests will arrive in its vicinity to make the investment profitable.
In this paper we present a randomized online algorithm for the Maximal Dense Tree problem that guarantees acceptance of a
(1- ɛ) factor of the requests accepted by the optimum offline algorithm with the expectation of density being at most polylogarithmically
lower than that of the offline algorithm. This yields an online capacitated multicast algorithm whose throughput is only poly-logarithmically lower than that of the optimum offline algorithm.
Previous work on multicast routing and maximal dense tree problems either made probabilistic assumptions or resulted in linear performance gaps with the offline algorithm. Attempts to solve the Online Maximal Dense Tree problem have also lead to the development of the first polylogarithmic approximation algorithms for the k -MST and the Prize Collecting Salesman problems [AABV]. 相似文献
116.
The optimum economic operation and planning of electric power generation systems occupies a crucial position in the electric power industry. Unit commitment (UC) is an important function in generation resource management. Moreover, it is nowadays critical to incorporate reliability analysis of the power system into its design of operation strategy. For this purpose, equipment malfunction or failure should be considered in unit commitment. In this paper, first the model for UC considering generator outages is formulated, where the reliability requirement is incorporated into the spinning reserve constraint in the optimization design. Then, a mixed binary- and real-coded particle swarm optimization (PSO) is developed to locate the optimum generation combination. A 10-generator test power system is used to verify the effectiveness of the proposed approach along the 24-h planning horizon. A comparative study is conducted to examine the impact of reliability constraint on the optimal solution obtained. Furthermore, comparison is made between the proposed method and other methods including both analytical and meta-heuristic algorithms. 相似文献
117.
Vimal Singh 《Automatica》2008,44(1):282-285
A novel criterion for the global asymptotic stability of a class of digital filters utilizing single saturation nonlinearity is presented. An example showing the effectiveness of the present criterion is given. 相似文献
118.
Ayman Altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar 《计算机系统科学与工程》2022,42(3):1095-1107
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 相似文献
119.
Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm3/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849. 相似文献
120.
P. Kumar P. K. Singh D. Kumar Ved Prakash M. Hussain 《Materials and Manufacturing Processes》2017,32(5):564-572
This article explains production of nickel nanoparticles through a micro-electrical discharge machining (EDM) process with a combination of different process parameters. The production of nickel nanoparticles was carried out in a dielectric medium (deionized water) with developed micro-EDM while polyvinyl alcohol worked as the stabilizing agent. The characterization of nickel nanoparticle was done by scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), UV–Vis spectroscopy, and Fourier transform infrared (FTIR) analysis. From this investigation, the mean crystal size of the nickel nanoparticles was found to be in the range of 15–20?mm for a pulse-on time variation of 2–0.3?µs and the crystal size was found to decrease with the decrease of pulse-on time. It was also observed that with this decrease, the shape and size of nickel nanoparticles change from spherical to needle-like. The dispersion stability of nickel nanofluid was determined by viscosity measurements and the dynamic viscosity was noted to decrease by decreasing the pulse duration. From the FTIR spectrum results, it was confirmed that the synthesized nickel nanoparticles in deionized water were pure and monolithic. UV–Vis–NIR spectroscopy depicted that the band gap energy increases with a reduction in the pulse-on time and obtains a higher band gap (5.31?eV) for 0.3?µs pulse-on time. 相似文献