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2nd International Conference on Networking, Information Systems and Security (NISS)

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450366458

In recent years cloud computing has revolutionized the IT world with rapidly emerging and widely accepted paradigm for computing systems, and it has become popular among users in organizations and companies. Nowadays, numerous organizations have begun to upload their massive amount of prominent data into public cloud. Nevertheless, uploading sensitive data to open and distributed public cloud storage services poses security risks such as availability, confidentiality and integrity to organizations. Moreover, non-stop cloud services have caused high levels of intrusion and abuse. Thereby, protecting network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is imperative to develop a powerful Network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. In this work, we propose a smart approach using an Improved Genetic algorithm (IGA) to build a Deep Neural Network (DNN) based anomaly NIDS. Genetic algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Our approach consists to use IGA in order to search the optimal or near optimal combination of most relevant values of the parameters included in construction of DNN based IDS or impacting its performance, like feature selection, data normalization, architecture of DNN, activation function, learning rate and Momentum term, which ensure high detection rate, high accuracy and low false alarm rate. CloudSim 4.0 simulator platform and CICIDS 2017 benchmark dataset were used for simulation and validation of the proposed system. The experimental results obtained demonstrate that in comparison to several traditional and recent approaches, our proposed IDS achieves higher detection rate and lower false positive rate.

关键词： Cloud computing Network intrusion detection system Anomaly detection Deep neural network back propagation learning algorithm Optimization Genetic algorithm CICIDS2017 dataset

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Novel Framework Based on Genetic algorithm and Simulated Annealing algorithm for Optimization of BP Neural Network Applied to Network IDS 18

Novel Framework Based on Genetic Algorithm and Simulated Ann...

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3rd International Conference on Smart City Applications (SCA')

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450365628

Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely back propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic algorithm (GA) and Simulated Annealing algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.

关键词： Network intrusion detection system back propagation neural network back propagation learning algorithm Genetic algorithm Simulated annealing algorithm Anomaly detection learning rate Momentum term KDD CUP '99 dataset

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Novel Network IDS in Cloud Environment Based on Optimized BP Neural Network Using Genetic algorithm 18

Novel Network IDS in Cloud Environment Based on Optimized BP...

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3rd International Conference on Smart City Applications (SCA')

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450365628

Nowadays, Cloud Computing (CC) had become an integral part of IT industry. It represents the maturing of technology and is a pliable, cost-effective platform which provides business/IT services over the Internet. Although there are several benefits of adopting this technology, there are some significant hurdles to it and one of them is security. In fact, due to the distributed and open nature of the cloud, resources, applications, and data are vulnerable and prone to intrusions that affect confidentiality, availability and integrity of Cloud resources and offered services. Network Intrusion Detection System (NIDS) has become the most commonly used component of computer system security and compliance practices that defends network accessible Cloud resources and services from various kinds of threats and attacks, while maintaining performance and service quality. In this work, in order to detect intrusions in CC environment, we propose a novel anomaly NIDS based on back propagation Neural Network (BPNN) classifier optimized using Genetic algorithm. Since, learning rate and Momentum term are among the most relevant parameters that impact the performance of BPNN classifier, we have employed Genetic algorithm to find the optimal values of these two parameters which ensure high detection rate, high accuracy and low false alarm rate. Experimental results on KDD CUP' 99 dataset indicate that in comparison to several traditional and new techniques, our proposed approach achieves higher detection rate and lower false positive rate.

关键词： Cloud computing Network intrusion detection system back propagation neural network back propagation learning algorithm Genetic algorithm Anomaly detection learning rate Momentum term KDD CUP '99 dataset

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Predicting Cryptocurrencies Prices with Neural Networks 6

Predicting Cryptocurrencies Prices with Neural Networks

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6th International Conference on Control Engineering and Information Technology (CEIT)

作者： Almasri, Emad Arslan, Emel Istanbul Univ Comp Engn Dept Istanbul Turkey

ISBN: (纸本)9781538676417

A cryptocurrency is a digital asset designed to work as a medium of exchange that uses cryptography to secure its transactions, to control the creation of additional units, and to verify the transfer of assets. Cryptocurrencies are a type of digital currencies, alternative currencies and virtual currencies. Cryptocurrencies use decentralized control as opposed to centralized electronic money and central banking systems. The decentralized control of each cryptocurrency works through a blockchain, which is a public transaction database,functioning as a distributed ledger. Neural Networks field has many techniques to perform predictions. They are widely used to predict the future values of stock exchange indicators variables. In this paper we will try to use Artificial Neural Network to predict cryptocurrencies close prices, and we'll study the difference in price change with the normal stock exchanges.

关键词： Cryptocurrency Bitcoin Neural Networks Feed-Forward Artificial Neural Network Resilient algorithm back propagation learning algorithm

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A Novel Fuzzy Modeling Structure-Decomposed Fuzzy System

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2017年第8期47卷 2311-2317页

作者： Su, Shun-Feng Chen, Ming-Chang Hsueh, Yao-Chu Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Natl Chung Shan Inst Sci & Technol Taoyuan 32546 Taiwan Adv Co Ltd Taipei 114 Taiwan

Decomposed fuzzy system (DFS) is a fuzzy system with a novel structure. Due to its excellent learning performance, DFS is originally proposed for an online learning control scheme and is shown to have effective learning performance. This paper is about the use of DFS for modeling dynamic systems. Since the learning mechanism used in online learning control is not suitable for modeling tasks, a commonly used back propagation learning algorithm is adapted for the use of DFS in modeling dynamic systems. The structure of DFS is to decompose each fuzzy variable into fuzzy subsystems that are called component fuzzy systems. Owing to the independency among component fuzzy systems, the learning for those parameters is also independent among different component fuzzy systems and thus, the learning can become more efficient. From the simulation results, it is evident that the proposed DFS can have much faster convergent speed. In addition, the DFS has a smaller testing error than those of other fuzzy systems.

关键词： back propagation learning algorithm decomposed fuzzy system (DFS) identification

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GA-based learning for rule identification in fuzzy neural networks

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APPLIED SOFT COMPUTING 2015年 35卷 605-617页

作者： Dahal, Keshav Almejalli, Khaled Hossain, M. Alamgir Chen, Wenbing Univ West Scotland Sch Engn & Comp Paisley PA1 2BE Renfrew Scotland Univ Bradford Dept Comp Bradford BD7 1DP W Yorkshire England Anglia Ruskin Univ IT Res Inst Cambridge England Nanjing Univ Informat Sci & Technol Nanjing Jiangsu Peoples R China

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Fuzzy rule identification Genetic algorithm back propagation learning algorithm Mamdani-type fuzzy neural network

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Discrimination of bio-crystallogram images using neural networks

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NEURAL COMPUTING & APPLICATIONS 2014年第5期24卷 1221-1228页

作者： Unluturk, Sevcan Unluturk, Mehmet S. Pazir, Fikret Kuscu, Alper Izmir Inst Technol Dept Food Engn Izmir Turkey Izmir Univ Econ Dept Software Engn TR-35330 Izmir Turkey Ege Univ Dept Food Engn Izmir Turkey Suleyman Demirel Univ Fac Agr TR-32200 Isparta Turkey

This study utilized a unique neural network model for texture image analysis to differentiate the crystallograms from pairs of fresh red pepper fruits from conventional and organic farms. The differences in visually analyzed samples are defined as the distribution of crystals on the circular glass underlay, the thin or thick structure of crystal needles, the angles between branches and side needles, etc. However, the visual description and definition of bio-crystallogram images has major disadvantages. A novel methodology called an image neural network (INN) has been developed to overcome these shortcomings. The 1,488 x 2,240 pixel bio-crystallogram images were acquired in a lab and cropped to 425 x 1,025 pixel images. These depicted either a conventional sweet red pepper or an organic sweet red pepper. A set of 19 images was utilized to train the image neural network. A new set of 4 images was then prepared to test the INN performance. Overall, the INN achieved an average recognition performance of 100 %. This high level of recognition suggests that the INN is a promising method for the discrimination of bio-crystallogram images. In addition, Hinton diagrams were utilized to display the optimality of the INN weights.

关键词： back propagation learning algorithm Bayes optimal decision rule Gram-Charlier series Hinton diagrams Neural network Probability density function Bio-crystallogram images VLSI

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A soft computing approach to projecting locational marginal price

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NEURAL COMPUTING & APPLICATIONS 2013年第6期22卷 1115-1124页

作者： Nwulu, Nnamdi I. Fahrioglu, Murat Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa Middle E Tech Univ Dept Elect & Elect Engn TR-10 Kalkanli Mersin Turkey

The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.

关键词： Locational marginal price Artificial neural networks Support vector machines back propagation learning algorithm Radial basis function

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A Neural Network Model for Optimal Demand Management Contract Design

A Neural Network Model for Optimal Demand Management Contrac...

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International Conference on Environment and Electrical Engineering

作者： Nnamdi I. Nwulu Murat Fahrioglu Department of Electrical & Electronic Engineering Near East University Department of Energy Systems Engineering Bahcesehir University

ISBN: (纸本)9781424487790

The ever increasing need for energy efficient systems has led to various ingenious ideas about energy management. A major offshoot of this search for energy efficient solutions is demand management in power systems. The goal of any demand management program is to control the demand for electric power among customers thereby creating load relief for electric utilities and improving system security. Typically demand management contract formulations reward customers who willingly sign up for load interruption with incentives. These forms of contracts are termed incentive compatible contracts and the incentive offered the customer should exceed interruption cost and at the same time should be beneficial to the utility. There are different systems to design these kind of contracts and in the past mechanism design from Game theory, has been used in the design of such contracts. In this work we propose an artificial neural network which is trained to determine the optimal contract. The learning algorithm utilized by the artificial neural network is the back propagation learning algorithm where useful power system parameters serve as the neural networks input while the neural systems output is the contract value. Game theory's mechanism design serves as the target for results obtained from the artificial neural network. Our proposed neural system is tested on the IEEE 14 bus test system.

关键词： Mechanism design Game Theory Demand Management Artificial neural networks back propagation learning algorithm

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Performance Improvement in Satellite Networks Based on Markovian Weather Prediction

Performance Improvement in Satellite Networks Based on Marko...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Harb, Kamal Yu, F. Richard Dhakal, Pramod Srinivasan, Anand Carleton Univ 1125 Colonel Dr Ottawa ON K1S 5B6 Canada EION Wireles Ottawa K2K 2E3 ON Canada

ISBN: (纸本)9781424456383

Prediction of channel characteristics can be of immense value in improving the quality of signals in high frequency satellite systems. Making prediction of rainfall rate (RR) using Markov theory and using that prediction in an intelligent system (IS) to maintain the quality of service (QoS) in channels impacted by attenuation due to weather is the object of this paper. The paper describes the method of prediction rainfall rate (RRp) using weather collected by environment agencies and applying the predictions to gateway and ground terminal for optimal control of channel characteristics. This novel method of predicting weather characteristics using Markov theory supplies valuable data to develop an enhanced back propagation-learning algorithm to iteratively tune the IS to adapt to changing weather conditions. The effectiveness of the algorithm was tested on a simulated model for activating the weighted modulation and codepoint control. It demonstrated marked improvements in channel parameter tuning and signal quality.

关键词： Markov processes Markov theory Markovian weather prediction back propagation learning algorithm backpropagation channel characteristics codepoint control geophysical signal processing high frequency satellite system intelligent system optimal control perf

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