Accelerate Black-Box Attack with White-Box Prior Knowledge

A Hybrid Secure Storage Scheme to Avoid EDOS Attacks in Cloud Computing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5861.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1236-1242

Keyword(s):

Cloud Computing ◽

Problem Solving ◽

Prior Knowledge ◽

Private Information ◽

Black Box ◽

Cloud Environment ◽

Secure Storage ◽

Storage Scheme ◽

Edos Attacks ◽

Box Method

In advent of cloud environment, cloud operator is not a completely trusted to put on private information, because of lack of consumer to cloud control. To assurance privacy, documents sharer deploy their encipher documents. Encipher documents dispense to among consumers using CP-ABE scheme. But it is not completely safe in opposition to different assaults. The prior knowledge cannot offer any verification ability to cloud operator whether the user can decipher or not. Various invaders may obtain lot of document by initiate EDoS assaults. The consumer of cloud abides cost. To handle above issues, this article suggests a problem solving plan to safe encipher cloud repository from EDoS assaults and maintain supply utilization. It utilizes CP-ABE tactics in a black-box method furthermore accomplish impulsive entryway contract epithetical CP-ABE. We tend to present 2 mechanisms for various styles, observed via achievement and shield research.

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Gray-box identification with regularized FIR models

at - Automatisierungstechnik ◽

10.1515/auto-2018-0026 ◽

2018 ◽

Vol 66 (9) ◽

pp. 704-713 ◽

Cited By ~ 2

Author(s):

Tobias Münker ◽

Timm J. Peter ◽

Oliver Nelles

Keyword(s):

Dynamic System ◽

Prior Knowledge ◽

Cross Validation ◽

Finite Impulse Response ◽

Black Box ◽

Model Structure ◽

Linear Dynamic ◽

Novel Approach ◽

Box Models ◽

Validation Criterion

Abstract The problem of modeling a linear dynamic system is discussed and a novel approach to automatically combine black-box and white-box models is introduced. The solution proposed in this contribution is based on the usage of regularized finite-impulse-response (FIR) models. In contrast to classical gray-box modelling, which often only optimizes the parameters of a given model structure, our approach is able to handle the problem of undermodeling as well. Therefore, the amount of trust in the white-box or gray-box model is optimized based on a generalized cross-validation criterion. The feasibility of the approach is demonstrated with a pendulum example. It is furthermore investigated, which level of prior knowledge is best suited for the identification of the process.

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Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers

Sensors ◽

10.3390/s21196484 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6484

Author(s):

Han Li ◽

Linling Qiu ◽

Meihong Wang

Keyword(s):

Machine Learning ◽

Prior Knowledge ◽

Black Box ◽

Training Data ◽

System Framework ◽

Related Field ◽

Cancer Prediction ◽

Knowledge Based ◽

Noticeable Improvement ◽

Assisted Diagnosis

Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put forward, but many of them have failed to become widely utilised due to some crucial problems. For example, most methods require too much training data, which is not always applicable to institutes, and the complicated genetic mutual effects of cancers are generally ignored in many proposed methods. Moreover, a majority of these assist models are actually not safe to use, as they are generally built on black-box machine learners that lack references from related field knowledge. We observe that few machine-learning-based cancer predictors are capable of employing prior knowledge (PrK) to mitigate these issues. Therefore, in this paper, we propose a generalisable informed machine learning architecture named the Informed Attentive Predictor (IAP) to make PrK available to the predictor’s decision-making phases and apply it to the field of cancer prediction. Specifically, we make several implementations of the IAP and evaluate its performance on six TCGA datasets to demonstrate the effectiveness of our architecture as an assist system framework for actual clinical usage. The experimental results show a noticeable improvement in IAP models on accuracies, f1-scores and recall rates compared to their non-IAP counterparts (i.e., basic predictors).

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NONLINEAR IDENTIFICATION USING PRIOR KNOWLEDGE OF FIXED POINTS: A MULTIOBJECTIVE APPROACH

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127403007187 ◽

2003 ◽

Vol 13 (05) ◽

pp. 1229-1246 ◽

Cited By ~ 13

Author(s):

ERIVELTON G. NEPOMUCENO ◽

RICARDO H. C. TAKAHASHI ◽

GLEISON F. V. AMARAL ◽

LUIS A. AGUIRRE

Keyword(s):

Dynamical System ◽

Fixed Points ◽

Prior Knowledge ◽

Model Building ◽

Nonlinear Dynamical System ◽

Black Box ◽

Model Structure ◽

Nonlinear Identification ◽

Nonlinear Dynamical ◽

Multiobjective Approach

This paper is devoted to the problem of model building from data produced by a nonlinear dynamical system. Unlike most published works that address the problem from a black-box perspective, in the present paper a procedure is developed that permits the use of prior knowledge about the location of fixed-points in addition to the data thus resulting in a gray-box approach. Numerical results using Chua's double-scroll attractor and the sine map are presented. As discussed, the suggested procedure is useful as a means to partially compensate for the loss of information due to noise and to improve dynamical performance in the presence of model structure mismatches. Preliminary results have indicated that the procedure outlined in this paper is a systematic way of searching for models in the vicinity of the black-box solution. This could have important consequences not only in model building but also in model validation.

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An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

Volume 11B: 46th Design Automation Conference (DAC) ◽

10.1115/detc2020-22116 ◽

2020 ◽

Author(s):

Arpan Biswas ◽

Christopher Hoyle

Keyword(s):

Objective Function ◽

Prior Knowledge ◽

Design Space ◽

Black Box ◽

Training Data ◽

Bayesian Optimization ◽

Design Parameters ◽

Classification Error ◽

True Solution ◽

Classification Tool

Abstract The paper presents a novel approach to apply Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is an emerging field of study in the Sequential Design Methods where we learn and update our knowledge from prior evaluated designs, and proceed to the selection of new designs for future evaluation. It has been considered as a low-cost global optimization tool for design problems having expensive black-box objective functions. However, BO is mostly suited to problems with the assumption of a continuous objective function, and does not guarantee true convergence if the objective function has a discontinuity. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. Therefore, in this paper, we have proposed to predict the discontinuity of the objective function using a BO algorithm which can be considered as the pre-stage before optimizing the same unknown objective function. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transitions between those regions; therefore, the discontinuity has also been treated as an unknown constraint. The paper focuses on developing BO framework with maximizing the reformulated objective function on the same design space to predict the transition regions as a design methodology or classification tool between safe and unsafe designs, where we start with very limited data or no prior knowledge and then iteratively focus on sampling most designs near the transition region through better prior knowledge (training data) and thereby increase the accuracy of prediction to the true boundary while minimizing the number of expensive function evaluations. The converged model has been compared with the true solution for different design parameters and the results provided a classification error rate and function evaluations at an average of < 1% and ∼150, respectively. The results in this paper show some future research directions in extending the application of BO and considered as the proof of concept on large scale problem of complex diffusion bonded hybrid Compact Heat Exchangers.

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