International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets
Статья научная
In the era of information and communication technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the most prominent fields in this area. Data mining in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published from 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DARPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.
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Database Performance Optimization–A Rough Set Approach
Статья научная
As the sizes of databases are growing exponentially, the optimal design and management of both traditional database management systems as well as processing techniques of data mining are of significant importance. Several approaches are being investigated in this direction. In this paper a novel approach to maintain metadata based on rough sets is proposed and it is observed that with a marginal changes in buffer sizes faster query processing can be achieved.
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Database Semantic Interoperability based on Information Flow Theory and Formal Concept Analysis
Статья научная
As databases become widely used, there is a growing need to translate information between multiple databases. Semantic interoperability and integration has been a long standing challenge for the database community and has now become a prominent area of database research. In this paper, we aim to answer the question how semantic interoperability between two databases can be achieved by using Formal Concept Analysis (FCA for short) and Information Flow (IF for short) theories. For our purposes, firstly we discover knowledge from different databases by using FCA, and then align what is discovered by using IF and FCA. The development of FCA has led to some software systems such as TOSCANA and TUPLEWARE, which can be used as a tool for discovering knowledge in databases. A prototype based on the IF and FCA has been developed. Our method is tested and verified by using this prototype and TUPLEWARE.
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Databases in Cloud Computing: A Literature Review
Статья научная
Information Technology industry has been using the traditional relational databases for about 40 years. However, in the most recent years, there was a substantial conversion in the IT industry in terms of commercial applications. Stand-alone applications have been replaced with electronic applications, committed servers with various appropriate servers and devoted storage with system storage. Lower fee, flexibility, the model of pay-as-you-go are the main reasons, which caused the distributed computing are turned into reality. This is one of the most significant revolutions in Information Technology, after the emergence of the Internet. Cloud databases, Big Table, Sherpa, and SimpleDB are getting to be more familiar to communities. They highlighted the obstacles of current social databases in terms of usability, flexibility, and provisioning. Cloud databases are essentially employed for information-escalated applications, such as storage and mining of huge data or commercial data. These applications are flexible and multipurpose in nature. Numerous value-based information administration applications, like banking, online reservation, e-trade and inventory administration, etc. are produced. Databases with the support of these types of applications have to include four important features: Atomicity, Consistency, Isolation, and Durability (ACID), although employing these databases is not simple for using in the cloud. The goal of this paper is to find out the advantages and disadvantages of databases widely employed in cloud systems and to review the challenges in developing cloud databases.
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Статья научная
Decentralized self-adaptive systems consist of multiple control loops that adapt some local and system-level global goals of each locally managed system or component in a decentralized setting. As each component works together in a decentralized environment, a control loop cannot take adaptation decisions independently. Therefore, all the control loops need to exchange their adaptation decisions to infer a global knowledge about the system. Decentralized self-adaptation approaches in the literature uses the global knowledge to take decisions that optimize both local and global goals. However, coordinating in such an unbounded manner impairs scalability. This paper proposes a decentralized self-adaptation technique using reinforcement learning that incorporates partial knowledge in order to reduce coordination overhead. The Q-learning algorithm based on Interaction Driven Markov Games is utilized to take adaptation decisions as it enables coordination only when it is beneficial. Rather than using unbounded number of peers, the adaptation control loop coordinates with a single peer control loop. The proposed approach was evaluated on a service-based Tele Assistance System. It was compared to random, independent and multiagent learners that assume global knowledge. It was observed that, in all cases, the proposed approach conformed to both local and global goals while maintaining comparatively lower coordination overhead.
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Decoding Optimization Algorithms for Convolutional Neural Networks in Time Series Regression Tasks
Статья научная
Optimization algorithms play a vital role in training deep learning models effectively. This research paper presents a comprehensive comparative analysis of various optimization algorithms for Convolutional Neural Networks (CNNs) in the context of time series regression. The study focuses on the specific application of maximum temperature prediction, utilizing a dataset of historical temperature records. The primary objective is to investigate the performance of different optimizers and evaluate their impact on the accuracy and convergence properties of the CNN model. Experiments were conducted using different optimizers, including Stochastic Gradient Descent (SGD), RMSprop, Adagrad, Adadelta, Adam, and Adamax, while keeping other factors constant. Their performance was evaluated and compared based on metrics such as mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R²), mean absolute percentage error (MAPE), and explained variance score (EVS) to measure the predictive accuracy and generalization capability of the models. Additionally, learning curves are analyzed to observe the convergence behavior of each optimizer. The experimental results, indicating significant variations in convergence speed, accuracy, and robustness among the optimizers, underscore the research value of this work. By comprehensively evaluating and comparing various optimization algorithms, we aimed to provide valuable insights into their performance characteristics in the context of time series regression using CNN models. This work contributes to the understanding of optimizer selection and its impact on model performance, assisting researchers and practitioners in choosing the most suitable optimization algorithm for time series regression tasks.
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Defending against Malicious Threats in Wireless Sensor Network: A Mathematical Model
Статья научная
Wireless Sensor Networks offer a powerful combination of distributed sensing, computing and communication. They lend themselves to countless applications and at the same time constrained by limited battery life, processing capability, memory and bandwidth which makes it soft target of malicious objects such as virus and worms. We study the potential threat for worm spread in wireless sensor network using epidemic theory. We propose a new model Susceptible-Exposed-Infectious-Quarantine-Recovered with Vaccination (SEIQRS-V), to characterize the dynamics of the worm spread in WSN. Threshold, equilibrium and their stability are discussed. Numerical methods are employed to solve the system of equations and MATLAB is used to simulate the system. The Quarantine is a method of isolating the most infected nodes from the network till they get recovered and the Vaccination is the mechanism to immunize the network temporarily to reduce the spread worms.
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Delay Scheduling Based Replication Scheme for Hadoop Distributed File System
Статья научная
The data generated and processed by modern computing systems burgeon rapidly. MapReduce is an important programming model for large scale data intensive applications. Hadoop is a popular open source implementation of MapReduce and Google File System (GFS). The scalability and fault-tolerance feature of Hadoop makes it as a standard for BigData processing. Hadoop uses Hadoop Distributed File System (HDFS) for storing data. Data reliability and fault-tolerance is achieved through replication in HDFS. In this paper, a new technique called Delay Scheduling Based Replication Algorithm (DSBRA) is proposed to identify and replicate (dereplicate) the popular (unpopular) files/blocks in HDFS based on the information collected from the scheduler. Experimental results show that, the proposed method achieves 13% and 7% improvements in response time and locality over existing algorithms respectively.
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Delay and Energy Optimized Safety Information Dissemination Scheme in V2I Networks
Статья научная
Intelligent Transport System (ITS) is a transport system that uses communicating technologies such as cellular network communication, digital video broadcasting and adhoc wireless communication to link people on the road, vehicles with aim of solving various traffic related issues. Vehicle to infrastructure (V2I) communication is an important research area to develop cooperative self-driving support system using DSRC technology. V2I develops an environment friendly system that also accelerates the fuel efficiency by establishing high quality links between vehicles to roadside infrastructure. It is a system to prevent and help drivers to overlooking or missing the red lights at junctions. V2I system along the road side and intersections continuously transmit the traffic signal information to vehicles by warning the driver about red lights and thus help us to prevent road rule violations. ITS helps to prevent drivers' oversight about signals right/left turn collision avoidance and timely activation of brake system. In the proposed work we used a three-layer Vehicle to Infrastructure (V2I) network architecture to collect and disseminate the safety information using static and dynamic agents. These methods help us to quickly selecting high quality error free links to forward the data packets. In a highway road scenario with moderate traffic density, the proposed system gives an improved performance in terms of coverage area, lossless transmission and reduced latency. Finally, qualitative comparison is made with present V2I system and found significance improvement in its performance metrics. The outcome of the proposed system improved by 23%, 13%, 15% compared to the existing system in terms of end-to-end delay, communication overhead and energy consumption respectively considering V2I network architecture.
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Deployment of Coordinated Multiple Sensors to Detect Stealth Man-in-the-Middle Attack in WLAN
Статья научная
The use of wireless devices is increasing tremendously in our day-to-day life because of their portability and ease of deployment. The augmented practices of using these technologies have put the user security at risk. The Stealth Man-In-The-Middle (SMITM) is one of the attacks that has arisen out of the flaw in the wireless technology itself. This attack aims at stealing the data of the network users by redirecting the traffic aimed at a legitimate user towards itself. Moreover the access point or any other detection device connected to the wired media fails to detect this attack. The objective of this work is to develop a technique that would be able to detect SMITM attack efficiently. In this work we present a SMITM detection approach. Our approach detects the SIMTM attack by deploying multiple coordinated sensors. The simulation results witnessed that the proposed scheme is capable of detecting SMITM attack even in case of a mobile attacker.
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Статья научная
Ranking fuzzy numbers has become an important process in decision making. Many ranking methods have been proposed thus far and one of the commonly used is centroid of trapezoid. Here we try to derive detail mathematical derivation of centroids of a Trapezoidal Intuitionistic Fuzzy Number along x and y axis. After that we derive the ranking value from two centroid along x and y axis. At the end of the article ranking value on fuzzy geometric programming is used. Here we are dealing with three strong decision making concepts. Intuitionistic trapezoidal fuzzy system is much more decision oriented approach than normal fuzzy number in real life uncertain environment, where we can apply membership and non membership concept for analyzing any real life situation. Ranking value, based on centroid of any Trapezoidal Intuitionistic Fuzzy Number helps for conclusion derivation in quantitative way. We here choose most powerful non linear optimization tool, geometrical programming technique, for generating any decision, using Trapezoidal Intuitionistic Fuzzy Number with centroid ranking approach.
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Design Adaptive Fuzzy Inference Controller for Robot Arm
Статья научная
Design robust controller for uncertain nonlinear systems most of time can be a challenging work. One of the most active research areas in this field is control of the robot arm. The control strategies for robotics are classified in two main groups: classical and non-classical methods, where the classical methods use the conventional control theory and non-classical methods use the artificial intelligence theory such as fuzzy logic, neural networks and/or neuro-fuzzy. Control robot arm using classical controllers are often having lots of problems because robotic systems are always highly nonlinear. Accurate robot manipulator is difficult because some dynamic parameters such as compliance and friction are not well understood and some robot parameters such as inertia are difficult to measure accurately. Artificial control such as Fuzzy logic, neural network, genetic algorithm, and neurofuzzy control have been applied in many applications. Therefore, stable control of a nonlinear dynamic system such as a robot arm is challenging because of some mentioned issues. In this paper the intelligent control of robot arm using Adaptive Fuzzy Gain scheduling (AFGS) and comparison to fuzzy logic controller (FLC) and various performance indices like the RMS error, and Steady state error are used for test the controller performance.
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Design High Efficiency-Minimum Rule Base PID Like Fuzzy Computed Torque Controller
Статья научная
The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy Computed Torque Controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI controller to have the minimum rule base. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator’s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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Статья научная
Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. Closed loop combustion control is a key technology that is used to optimize the engine combustion process to achieve this goal. In order to conduct research in the area of closed loop combustion control, a control oriented cycle-to-cycle engine model, containing engine combustion information for each individual engine cycle as a function of engine crank angle, is a necessity. This research aims to design a new methodology to fix the fuel ratio in internal combustion (IC) engine. Baseline method is a linear methodology which can be used for highly nonlinear system’s (e.g., IC engine). To optimize this method, new linear part sliding mode method (NLPSM) is used. This online optimizer can adjust the optimal coefficient to have the best performance.
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Design Modified Fuzzy PD Gravity Controller with Application to Continuum Robot
Статья научная
Refer to this research, a position modified parallel error-based fuzzy Proportional Derivative (PD) gravity controller is proposed for continuum robot manipulator. The main problem of the pure conventional nonlinear controller was equivalent dynamic formulation in uncertain systems. The main challenge of linear controllers is linearization techniques and the quality of performance. The nonlinear equivalent dynamic problem in uncertain system is solved by applied fuzzy logic theory to modified PD gravity. To estimate the continuum robot manipulator system’s dynamic, proportional plus modified derivative with 7 rules Mamdani inference system is design and applied to modified PD gravity methodology. The proportional coefficient of controller is tuned by new methodology in limitation uncertainties. The results demonstrate that the proposed controller is a partly model-free controllers which works well in certain and partly uncertain system.
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Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for Continuum Robot
Статья научная
In this paper, a linear proportional derivative (PD) controller is designed for highly nonlinear and uncertain system by using robust factorization approach. To evaluate a linear PD methodology two type methodologies are introduced; sliding mode controller and fuzzy logic methodology. This research aims to design a new methodology to fix the position in continuum robot manipulator. PD method is a linear methodology which can be used for highly nonlinear system’s (e.g., continuum robot manipulator). To estimate this method, new parallel fuzzy sliding mode controller (PD.FSMC) is used. This estimator can estimate most of nonlinearity terms of dynamic parameters to achieve the best performance. The asymptotic stability of fuzzy PD control with first-order sliding mode compensation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.
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Статья научная
This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.
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Design Serial Fuzzy Variable Structure Compensator for Linear PD Controller: Applied to Rigid Robot
Статья научная
In this paper, a PD-serial fuzzy based robust nonlinear estimator for a robot manipulator is proposed by using robust factorization approach. That is, considering the uncertainties of dynamic model consisting of measurement error and disturbances, a PD with fuzzy estimator variable structure nonlinear feedback control scheme is designed to reduce effect of uncertainties. This research aims to design a new methodology to fix the position in robot manipulator. PD method is a linear methodology which can be used for highly nonlinear system’s (e.g., robot manipulator). To estimate this method, new serial fuzzy variable structure method (PD.FVSM) is used. This estimator can estimate the parameters to have the best performance.
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Статья научная
Fuzzy logic controller (FLC) is an important nonlinear controller in an uncertain dynamic system’s parameters. This controller is used to control of nonlinear dynamic systems particularly for spherical motor, because it has a suitable control performance and it is a stable. Conversely pure fuzzy logic controller is a high-quality intelligent nonlinear controller; it has two important problems; reliability and robustness in uncertain dynamic parameter. To increase the reliability and robustness, this research is focused on applied feedback linearization method in pure fuzzy logic controller. In this research the nonlinear equivalent dynamic (equivalent part) formulation problem in uncertain condition is also solved by combine pure fuzzy logic control and feedback linearization method. In this method feedback linearization theorem is applied to fuzzy logic controller to increase the stability, reliability and robustness, which it is based on nonlinear dynamic formulation. To achieve this goal, the dynamic-based formulation feedback linearization method is design. This method is robust and model-based nonlinear control therefore can reduce the nonlinearity term of system and reduce the effect of coupling. In this research MAMDANI fuzzy inference system is used as a main controller. It has minimum rule base to practical implementation. This technique was employed to obtain the desired control behavior with a number of information about dynamic model of system and a feedback linearization control was applied to reinforce system performance.
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Design a Novel SISO Off-line Tuning of Modified PID Fuzzy Sliding Mode Controller
Статья научная
The Proportional Integral Derivative (PID) Fuzzy Sliding Mode Controller (FSMC) is the most widely used control strategy in the Industry (control of robotic arm). The popularity of PID FSMC controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID FSMC controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. This paper analyses the modified PID FSMC controllers based on minimum rule base for flexible robot manipulator system and test the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
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