scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals A Novel Two-phase Cycle Algorithm for Effective Cyber Intrusion Detection in Edge Computing

2020 ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

Abstract Edge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received more and more attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on multi-objective genetic algorithm (MOGA) and modified back propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build multi-objective optimization model that tries to find Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of average false positive rate (Avg FPR), mean squared error (MSE), and negative average true positive rate (Avg TPR) in the dataset. In the second phase some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. Benchmark dataset namely KDD cup 1999 is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN based solutions. Combining these MBPNN can significantly improve prediction performance, and a GA is used to find the optimal MBPNN combination. The result shows that the proposed approach could reach an accuracy of 98.81% and a detection rate of 98.23%, which outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

2011 ◽  
Vol 22 (4) ◽  
pp. 379-389
Author(s):  
Ricardo C. Silva ◽  
Edilson F. Arruda ◽  
Fabrício O. Ourique
Keyword(s):  
Probability Distributions ◽  
Phase Method ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Term Operation ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.

scholarly journals Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

2019 ◽  
Vol 11 (9) ◽  
pp. 2619 ◽  
Author(s):  
Wei He ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Jili Kong
Keyword(s):  
Service Management ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Multiple Objectives ◽  
Second Phase ◽  
Time Cost ◽  
Two Phase ◽  
Preference Information ◽  
Multi Objective ◽  
Service Selection And Scheduling

Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.

scholarly journals Decision space robustness for multi-objective integer linear programming

2021 ◽  
Author(s):  
Michael Stiglmayr ◽  
José Rui Figueira ◽  
Kathrin Klamroth ◽  
Luís Paquete ◽  
Britta Schulze
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Efficient Solutions ◽  
Second Phase ◽  
Decision Space ◽  
Two Phase ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Linear Programming Problems ◽  
Consistency Properties

AbstractIn this article we introduce robustness measures in the context of multi-objective integer linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the uncertainty with respect to the implementation of a specific solution. An efficient solution is considered to be decision robust if many solutions in its neighborhood are efficient as well. This rather new area of research differs from robustness concepts dealing with imperfect knowledge of data parameters. Our approach implies a two-phase procedure, where in the first phase the set of all efficient solutions is computed, and in the second phase the neighborhood of each one of the solutions is determined. The indicators we propose are based on the knowledge of these neighborhoods. We discuss consistency properties for the indicators, present some numerical evaluations for specific problem classes and show potential fields of application.

scholarly journals Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm

2009 ◽  
Vol 13 (11) ◽  
pp. 2137-2149 ◽  
Author(s):  
M. Shafii ◽  
F. De Smedt
Keyword(s):  
Genetic Algorithm ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Parameters ◽  
Pareto Optimal ◽  
Distributed Hydrological Model ◽  
Nsga Ii ◽  
Multi Objective ◽  
Wetspa Model ◽  
Model Efficiency

Abstract. A multi-objective genetic algorithm, NSGA-II, is applied to calibrate a distributed hydrological model (WetSpa) for prediction of river discharges. The goals of this study include (i) analysis of the applicability of multi-objective approach for WetSpa calibration instead of the traditional approach, i.e. the Parameter ESTimator software (PEST), and (ii) identifiability assessment of model parameters. The objective functions considered are model efficiency (Nash-Sutcliffe criterion) known to be biased for high flows, and model efficiency for logarithmic transformed discharges to emphasize low-flow values. For the multi-objective approach, Pareto-optimal parameter sets are derived, whereas for the single-objective formulation, PEST is applied to give optimal parameter sets. The two approaches are evaluated by applying the WetSpa model to predict daily discharges in the Hornad River (Slovakia) for a 10 year period (1991–2000). The results reveal that NSGA-II performs favourably well to locate Pareto optimal solutions in the parameters search space. Furthermore, identifiability analysis of the WetSpa model parameters shows that most parameters are well-identifiable. However, in order to perform an appropriate model evaluation, more efforts should be focused on improving calibration concepts and to define robust methods to quantify different sources of uncertainties involved in the calibration procedure.

scholarly journals Multi-Task Scheduling Based on Classification in Mobile Edge Computing

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 938 ◽  
Author(s):  
Xiao Zheng ◽  
Yuanfang Chen ◽  
Muhammad Alam ◽  
Jun Guo
Keyword(s):  
Task Scheduling ◽  
Upper Bound ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Edge Computing ◽  
Pareto Optimal ◽  
Mobile Edge Computing ◽  
Multi Objective Optimization ◽  
Multi Objective

In this paper, a dynamic multi-task scheduling prototype is proposed to improve the limited resource utilization in the vehicular networks (VNET) assisted by mobile edge computing (MEC). To ensure quality of service (QoS) and meet the growing data demands, multi-task scheduling strategies should be specially constructed by considering vehicle mobility and hardware service constraints. We investigate the rational scheduling of multiple computing tasks to minimize the VNET loss. To avoid conflicts between tasks when the vehicle moves, we regard multi-task scheduling (MTS) as a multi-objective optimization (MOO) problem, and the whole goal is to find the Pareto optimal solution. Therefore, we develop some gradient-based multi-objective optimization algorithms. Those optimization algorithms are unable to deal with large-scale task scheduling because they become unscalable as the task number and gradient dimensions increase. We therefore further investigate an upper bound of the loss of multi-objective and prove that it can be optimized in an effective way. Moreover, we also reach the conclusion that, with practical assumptions, we can produce a Pareto optimal solution by upper bound optimization. Compared with the existing methods, the experimental results show that the accuracy is significantly improved.

scholarly journals A novel approach for selective feature mechanism for two-phase intrusion detection system

2019 ◽  
Vol 14 (1) ◽  
pp. 101
Author(s):  
B Narendra Kumar ◽  
M S V Sivarama Bhadri Raju ◽  
B Vishnu Vardhan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Computational Time ◽  
Support Vector ◽  
Phase System ◽  
Second Phase ◽  
Two Phase ◽  
Linear Dependency

Intrusion Detection is an important aspect to secure the computing systems from different intrusions. To improve the accuracy and to reduce the computational time, this paper proposes a two-phase hybrid method based on the SVM and RNN. In addition, this paper also had a proposal to obtain a few sets of features with a feature selection technique in which the detection performance increases. For the two-phase system, two different feature selection techniques were proposed which solves both the linear dependency and non-linear dependency between the features. In the first phase, the RNN combines with the proposed Joint Mutual Information Maximization (JMIM) based feature selection and in the second phase, the Support Vector Machine (SVM) combines with correlation based feature selection. Extensive simulations are carried out over the proposed system using two different datasets, NSL-KDD and Kyoto2006+. The performance is measured through the performance metrics such as Detection Rate (DR), Precision, False Alarm Rate (FAR), Accuracy and F-Score. Furthermore, a comparative analysis with few recent hybrid frameworks is also enumerated. The obtained results signify the effectiveness of proposed method.

scholarly journals Prioritized optimization by Nash games : towards an adaptive multi-objective strategy

2021 ◽  
Vol 71 ◽  
pp. 54-63
Author(s):  
Jean-Antoine Désidéri ◽  
Régis Duvigneau
Keyword(s):  
Optimal Solution ◽  
Optimization Method ◽  
Cost Functions ◽  
Second Phase ◽  
Two Phase ◽  
Nash Games ◽  
Multi Objective ◽  
Business Jet ◽  
Decreasing Functions

This work is part of the development of a two-phase multi-objective differentiable optimization method. The first phase is classical: it corresponds to the optimization of a set of primary cost functions, subject to nonlinear equality constraints, and it yields at least one known Pareto-optimal solution xA*. This study focuses on the second phase, which is introduced to permit to reduce another set of cost functions, considered as secondary, by the determination of a continuum of Nash equilibria, {x̅ε} (ε≥ 0), in a way such that: firstly, x̅0=xA* (compatibility), and secondly, for ε sufficiently small, the Pareto-optimality condition of the primary cost functions remains O(ε2), whereas the secondary cost functions are linearly decreasing functions of ε. The theoretical results are recalled and the method is applied numerically to a Super-Sonic Business Jet (SSBJ) sizing problem to optimize the flight performance.

scholarly journals A multi-objective fuzzy mathematical approach for sustainable reverse supply chain configuration

2017 ◽  
Vol 11 ◽  
Author(s):  
Jyoti D. Darbari ◽  
Vernika Agarwal ◽  
Venkata S.S. Yadavalli ◽  
Diego Galar ◽  
Prakash C. Jha
Keyword(s):  
Social Performance ◽  
Fuzzy Model ◽  
Three Dimensions ◽  
Fuzzy Decision Making ◽  
Second Phase ◽  
Reverse Supply Chain ◽  
Two Phase ◽  
Multi Objective ◽  
Cost Differential ◽  
The Cost

Background: Designing and implementation of reverse logistics (RL) network which meets the sustainability targets have been a matter of emerging concern for the electronics companies in India.Objectives: The present study developed a two-phase model for configuration of sustainable RL network design for an Indian manufacturing company to manage its end-of-life and endof-use electronic products. The notable feature of the model was the evaluation of facilities under financial, environmental and social considerations and integration of the facility selection decisions with the network design.Method: In the first phase, an integrated Analytical Hierarchical Process Complex Proportional Assessment methodology was used for the evaluation of the alternative locations in terms of their degree of utility, which in turn was based on the three dimensions of sustainability. In the second phase, the RL network was configured as a bi-objective programming problem, and fuzzy optimisation approach was utilised for obtaining a properly efficient solution to the problem.Results: The compromised solution attained by the proposed fuzzy model demonstrated that the cost differential for choosing recovery facilities with better environmental and social performance was not significant; therefore, Indian manufacturers must not compromise on the sustainability aspects for facility location decisions.Conclusion: The results reaffirmed that the bi-objective fuzzy decision-making model can serve as a decision tool for the Indian manufacturers in designing a sustainable RL network. The multi-objective optimisation model captured a reasonable trade-off between the fuzzy goals of minimising the cost of the RL network and maximising the sustainable performance of the facilities chosen.

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