605 research outputs found

    Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

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    Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

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    This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

    Stochastic arrays and learning networks

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    This thesis presents a study of stochastic arrays and learning networks. These arrays will be shown to consist of simple elements utilising probabilistic coding techniques which may interact with a random and noisy environment to produce useful results. Such networks have generated considerable interest since it is possible to design large parallel self-organising arrays of these elements which are trained by example rather than explicit instruction. Once the learning process has been completed, they then have the potential ability to form generalisations, perform global optimisation of traditionally difficult problems such as routing and incorporate an associative memory capability which can enable such tasks as image recognition and reconstruction to be performed, even when given a partial or noisy view of the target. Since the method of operation of such elements is thought to emulate the basic properties of the neurons of the brain, these arrays have been termed neural 'networks. The research demonstrates the use of stochastic elements for digital signal processing by presenting a novel systolic array, utilising a simple, replicated cell structure, which is shown to perform the operations of Cyclic Correlation and the Discrete Fourier Transform on inherently random and noisy probabilistic single bit inputs. This work is then extended into the field of stochastic learning automata and to neural networks by examining the Associative Reward-Punish (A(_R-P)) pattern recognising learning automaton. The thesis concludes that all the networks described may potentially be generalised to simple variations of one standard probabilistic element utilising stochastic coding, whose properties resemble those of biological neurons. A novel study is presented which describes how a powerful deterministic algorithm, previously considered to be biologically unviable due to its nature, may be represented in this way. It is expected that combinations of these methods may lead to a series of useful hybrid techniques for training networks. The nature of the element generalisation is particularly important as it reveals the potential for encoding successful algorithms in cheap, simple hardware with single bit interconnections. No claim is made that the particular algorithms described are those actually utilised by the brain, only to demonstrate that those properties observed of biological neurons are capable of endowing collective computational ability and that actual biological algorithms may perhaps then become apparent when viewed in this light

    Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

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    To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect

    A survey of AI in operations management from 2005 to 2009

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    Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

    Detection of Rogue Access Point in WLAN using Hopfield Neural Network

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    The serious issue in the field of wireless communication is the security and how an organization implements the steps against security breach. The major attack on any organization is Man in the Middle attack which is difficult to manage. This attack leads to number of unauthorized access points, called rogue access points which are not detected easily. In this paper, we proposed a Hopfield Neural Network approach for an automatic detection of these rogue access points in wireless networking. Here, we store the passwords of the authentic devices in the weight matrix format and match the patterns at the time of login. Simulation experiment shows that this method is more secure than the traditional one in WLAN
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