STRATEGIC PROFILING & ANALYTIC MODELLING OF NODE MISBEHAVIOR IN MANET BASED IOT PARADIGM THEORY

Ubiquitous Computing and Internet of Things (IoT) are extremely popular in recent age and therefore imparting high level security mechanism is highly indispensable for such advanced technical systems. Game Theory acts as a suitable tool offering promising solutions to securityrelated concerns in Mobile Ad-Hoc Networks (i.e., MANETs). In MANETs, security forms a prominent concern as it includes nodes which are usually portable and require significant coordination between them. Further, the absence of physical organisation makes such networks susceptible to security breaches, hindering secure routing and execution among nodes. Coordination among nodes during communication and working without control of any central manager truly ensembles them to be applied in IoT. However, the identification and later mitigation of malicious nodes becomes an immensely difficult task especially when Selfish/Erroneous nodes exist along with normal Collaborative nodes in the Regular camp. Game Theory approach has been manipulated in the current study to achieve an analytical view while addressing the security concerns in MANETs. This study considers selfish nodes in the regular node camp while modelling the Regular versus Malicious node game and thereby enhancing the prior mathematical schema of strategical decision making to accommodate for the same. The proposed study performs statistical analysis and presents a mathematical model to mimic the multi-stage game between regular and malicious node using Game Theory. The simulation of the model has proved that the Perfect Bayesian Equilibrium outshines other approaches used in this study, specifically pure strategy and mixed strategy. The utility of both regular and malicious node has improved noticeably when nodes adopt PBE strategy. The framework tries to effectively represent the various unpredictable actions of node cooperation, node declination, node attacks as well as node reporting that can model the strategic profiling of various mobile nodes. Understanding the patterns and then deploying the algorithms in security products can reduce intrusion to a greater extent

MRI reconstruction using discrete fourier transform: a tutorial

The use of Inverse Discrete Fourier Transform (IDFT) implemented in the form of Inverse Fourier Transform (IFFT) is one of the standard method of reconstructing Magnetic Resonance Imaging (MRI) from uniformly sampled K-space data. In this tutorial, three of the major problems associated with the use of IFFT in MRI reconstruction are highlighted. The tutorial also gives brief introduction to MRI physics; MRI system from instrumentation point of view; K-space signal and the process of IDFT and IFFT for One and two dimensional (1D and 2D) data

Optimal model order selection for transient error autoregressive moving average (TERA) MRI reconstruction method

An alternative approach to the use of Discrete Fourier Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction is the use of parametric modeling technique. This method is suitable for problems in which the image can be modeled by explicit known source functions with a few adjustable parameters. Despite the success reported in the use of modeling technique as an alternative MRI reconstruction technique, two important problems constitutes challenges to the applicability of this method, these are estimation of Model order and model coefficient determination. In this paper, five of the suggested method of evaluating the model order have been evaluated, these are: The Final Prediction Error (FPE), Akaike Information Criterion (AIC), Residual Variance (RV), Minimum Description Length (MDL) and Hannan and Quinn (HNQ) criterion. These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). The result for each criterion is compared to result obtained by the use of a fixed order technique and three measures of similarity were evaluated. Result obtained shows that the use of MDL gives the highest measure of similarity to that use by a fixed order techniqu

Big data analysis solutions using mapReduce framework

Recently, data that generated from variety of sources with massive volumes, high rates, and different data structure, data with these characteristics is called Big Data. Big Data processing and analyzing is a challenge for the current systems because they were designed without Big Data requirements in mind and most of them were built on centralized architecture, which is not suitable for Big Data processing because it results on high processing cost and low processing performance and quality. MapReduce framework was built as a parallel distributed programming model to process such large-scale datasets effectively and efficiently. This paper presents six successful Big Data software analysis solutions implemented on MapReduce framework, describing their datasets structures and how they were implemented, so that it can guide and help other researchers in their own Big Data solutions

Increasing the speed of convergence of an artificial neural network based ARMA coefficients determination technique

In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination

A survey on MANETs: architecture, evolution, applications, security issues and solutions

Mobile ad hoc networks or MANETs, also referred to as mobile mesh networks at times, are self-configuring networks of mobile devices that are joined using wireless channels. These represent convoluted distributed systems comprising of wireless mobile nodes which are free to move and self-organise dynamically into temporary and arbitrary, ad hoc topologies. This makes it possible for devices as well as people to internetwork seamlessly in such regions that have no communication infrastructure in place. Conventionally, the single communication networking application following the ad hoc concept had been tactical networks. Lately, new technologies have been introduced such as IEEE 802.11, Hyperlan and Bluetooth that are assisting in the deployment of commercial MANETs external to the military realm. Such topical evolutions infuse a new and rising interest in MANET research and development. This paper provides an overview of the dynamic domain of MANETs. It begins with the discussion on the evolution of MANETs followed by its significance in various fields. Besides, the MANETs have been analysed from the security perspective, particularly the work performed in the node misbehaviour paradigm has been elaborated

Adapting the conventional packet scheduling algorithms for simultaneous support of 5G multimedia traffic mix

Simultaneous support of multimedia traffic mixture with strict and contending Quality of Service (QoS) in the downlink Fifth Generation (5G) mobile wireless network is a challenging issue. In the 5G wireless network, packet scheduling is in charge to deliver multimedia packets to the end users such that the scarce 5G radio resources are effectively used and the strict multimedia QoS is maintained for many users. Given that devising a new packet scheduling algorithm is time-consuming and requires additional effort, this paper slightly modifies several renowned conventional packet scheduling algorithms and evaluates their performance when simultaneously supporting Ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) in the downlink 5G. The efficiency of the Modified Maximum-Largest Weighted Delay First (M-MLWDF) algorithm was demonstrated via computer simulation where the algorithm supports 112.9% more users over Modified Max-Rate (M-Max-Rate) and Modified Round Robin (M-RR) at the uRLLC QoS targets whilst meeting the target eMBB throughput

A novel clustering based genetic algorithm for route optimization

Genetic Algorithm (GA), a random universal evolutionary search technique that imitates the principle of biological evolution has been applied in solving various problems in different fields of human endeavor. Despite it strength and wide range of applications, optimal solution may not be feasible in situations where reproduction processes which involve chromosomes selection for mating and regeneration are not properly done. In addition, difficulty is often encountered when there are significant differences in the fitness values of chromosomes while using probabilistic based selection approach. In this work, clustering based GA with polygamy and dynamic population control mechanism have been proposed. Fitness value obtained from chromosomes in each generation were clustered into two-non-overlapping clusters. The surviving chromosomes in the selected cluster were subjected to polygamy crossover mating process while the population of the offsprings which would form the next generation were subjected to dynamic population control mechanisms. The process was repeated until convergence to global solution was achieved or number of generation elapsed. The proposed algorithm has been applied to route optimization problem. Results obtained showed that the proposed algorithm outperforms some of the existing techniques. Furthermore, the proposed algorithm converged to global solution within few iterations (generations) thus favoring its acceptability for online-realtime applications. It was also observed that the introduction of clustering based selection algorithm guaranteed the selection of cluster with the optimal solution in every generation. In addition, the introduction of dynamic population control with polygamy selection processes enabled fast convergence to optimal solution and diversity in the population respectively

Machine Learning (ML) assisted Edge security framework on FPGAs

Edge computing (EC) is an act of bringing computational and storage capability near data sources. It helps to reduce response times and bandwidth requirements. However, the rapid proliferation of edge devices has expanded the attack surface and opportunity for adversaries to penetrate corporate networks. The limited computational abilities of edge devices and the heterogeneous nature of communication protocols further increase the security challenges of EC. Also, the trustworthiness of hardware devices is challenged due to security and privacy threats like trojan insertion, IP cloning, and hardware counterfeits. The application of Machine Language (ML) models in the edge computing paradigm creates a distributed intelligence architecture. Also, Field Programmable Gate Arrays (FPGAs) can exploit Physical Unclonable Functions (PUFs) characteristics to generate and store authentication keys. The PUF structure deployed with ML models in the edge layer can learn its complex input-output mapping from the Challenge and Response pairs (CRPs) to identify the suspicious and unknown responses. This article discusses the security and privacy issues in various layers of the EC architecture and proposes intrusion detection systems through the integration of FPGA-based edge sever and ML models. A PUF-assisted ML framework of the intrusion detection system is proposed to authenticate and detect potential attacks on the network

Performance Analysis of ARMA based Magnetic Resonance Imaging (MRI) Reconstruction Algorithm

The use of parametric modelling approach for Magnetic Resonance Imaging (MRI) reconstruction has been shown to produce images with higher resolution compared to the use of Fast Fourier Transform (FFT) technique. Despite this success, two problems lessen the use of this technique, these are: non availability of optimal method of estimating model order and the model coefficients determination. In this research work, a new method of Autoregressive Moving Average (ARMA) coefficients using three layer complex valued neural network ARMA techniques (CVNN-CARMA) with split complex-value weight and adaptive linear activation functions is hereby proposed. The proposed model coefficients determination in conjunction with various methods of optimal model order determination were then applied on MRI data using both Transient Error Reconstruction Algorithm (TERA) and modified Transient Error Reconstruction Algorithm to obtain images with improved resolution. Future work include extending this modelling method to two dimensional domain, evaluating the performance of the proposed CVNN-CARMA and using a trained artificial neural network to automatically obtain the model order of a complex valued data. Keywords: Autoregressive Model Algorithm (ARMA), Magnetic Resonance Imaging (MRI), Reconstructio