P. V. G. D. Prasad Reddy

Impact of Security Attacks on a New Security Protocol for Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a self-organized wireless short-lived network consisting of mobile nodes. The mobile nodes communicate with one another by wireless radio links without the use of any pre-established fixed communication network infrastructure. The mobile nodes are vulnerable to different types of security attacks that allow interception, injection, and interference of communication among nodes. Possible damages include leaking secret information, message contamination and node impersonation. MANETs need secure routing protocols to prevent possible security attacks. In this paper, we evaluate the performance of a new security protocol against various known and unknown malicious node attacks. Simulation results have shown that the proposed security protocol resists against malicious nodes with low implementation complexity.

Particle swarm optimized multiple regression linear model for data classification

This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets.

Prevention of Cross Site Scripting with E-Guard Algorithm

In this paper, we propose a passive detection system to identify successful XSS attacks. Based on a prototypical implementation, we examine our approach’s accuracy and verify its detection capabilities. We compiled a data-set of HTTP request/response from 20 popular web applications for this, in combination with both real word and manually crafted XSS exploits; our detection approach results in a total of zero false negatives for all tests, while maintaining an excellent false positive rate for more than 80 percent of the examined web applications.

Batch incremental processing for FP-tree construction using FP-Growth algorithm

In the present scenario of global economy and World Wide Web, large sets of evolving and distributed data can be handled efficiently by incremental data mining. Frequent patterns are very important in knowledge discovery and data mining process, such as mining of association rules, correlations. FP-tree is a very versatile data structure used for mining of frequent patterns in knowledge discovery and data mining process. FP-tree is a compact representation of transaction database that contains frequency information of all relevant frequent patterns (FP) of the database. All of the existing incremental frequent pattern mining algorithms, such as AFPIM, CATS, CanTree, CP-tree, and SPO-tree, perform incremental mining by processing one transaction of the incremental part of database at a time and updating it to the FP-tree of initial (original) database. Here, in this paper, we propose a novel method that takes advantage of FP-tree representation of incremental transaction database for incremental mining. We propose a batch incremental processing algorithm BIT_FPGrowth that restructures and merges two small consecutive duration FP-trees to obtain a FP-tree of the FP-Growth algorithm. Our BIT_FPGrowth uses FP-tree as preprocessed data repository to get transactions (i.e., item-sets), unlike other sequential incremental algorithms that read transactions from database. BIT_FPGrowth algorithm takes less time for constructing FP-tree. Our experimental results show that, as the size of the database increases, increase in runtime of BIT_FPGrowth is much less and is least of all the other algorithms.

Particle Swarm Optimization Based Approach for Resource Allocation and Scheduling in OFDMA Systems

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.

Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype Algorithms

The k-means algorithm is well-known for its efficiency in clustering large data sets and it is restricted to the numerical data types. But the real world is a mixture of various data typed objects. In this paper we implemented algorithms which extend the k-means algorithm to categorical domains by using Modified k-modes algorithm and domains with mixed categorical and numerical values by using k-prototypes algorithm. The Modified k-modes algorithm will replace the means with the modes of the clusters by following three measures like “using a simple matching dissimilarity measure for categorical data”, “replacing means of clusters by modes” and “using a frequency-based method to find the modes of a problem used by the k-means algorithm”. The other algorithm used in this paper is the k-prototypes algorithm which is implemented by integrating the Incremental k-means and the Modified k-modes partition clustering algorithms. All these algorithms reduce the cost function value.

A survey of cross-domain text categorization techniques

Text Mining is important, emerging, research area, because plenty of text resources growing rapidly through the internet and digital world. In the text data analysis text categorization is one of the vital techniques. Traditional text categorization methods are not able to handle well with learning across different domains. Cross-domain classification is more challenging problem than single domain classification problem. In this paper survey of cross-domain text categorization techniques have been presented.

Skin based Occlusion Detection and Face Recognition using Machine Learning Techniques

this paper, a detailed experimental study of occlusion detection in the controlled environmentsbased on skin color is proposed.The image is given as an input to the face detection algorithm to detect the faces. Some faces are not detected dueto occlusion, so an occlusion detection technique is implemented to detect all the occluded faces. Those occlusions are detected using skin color of the faces. This is implemented by using circular Hough transform through plotting of circles on the faces present in the image. In order to overcome the illumination problem, extraction of local SMQT features is done. After completion of face detection, occlusions are detected based on skin color and the respective spatial locations of the image are returned.To differentiate the skin colors with other colors, SVM classifier is used. Huge datasets are collected for the purpose of training.From the image database, the occluded faces are recognized by retrieving it through spatial location. This implementation is suitable for all face detection applications in constrained environments the experiment using this technique havegiven 94%accuracy.

A parallel hybridization of clonal selection with shuffled frog leaping algorithm for solving global optimization problems (P-AISFLA)

Shuffled frog leaping Algorithm (SFLA) is a new memetic, local search, population based, Parameter free, meta-heuristic algorithm that has emerged as one of the fast and robust algorithm with efficient global search capability. SFLA has the advantage of social behavior through the process of shuffling and leaping that helps for the infection of ideas. Clonal Selection Algorithms (CSA) are computational paradigms that belong to the computational intelligence family and is inspired by the biological immune system of the human body. CSA has the advantages of Innate and Adaptive Immunity mechanisms to antigenic stimulus that helps the cells to grow its population by the process of cloning whenever required. A hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate. This paper compared the Conventional CLONALG and SFLA approaches with the proposed hybrid algorithm and tested on several standard benchmark functions. Experimental results show that the proposed hybrid approach significantly outperforms the existing CLONALG and SFLA approaches in terms of Mean optimal Solution, Success rate, Convergence Speed and Solution stability.

Fuzzy Based PSO for Software Effort Estimation

Software Effort Estimation is the most important activity in project planning for Project Management. This Effort estimation is required for estimation of resources, time to complete the project successfully. Many models have been proposed, but because of differences in the data collected, type of projects and project attributes, no model has been proven successful at effectively and consistently predicting software development effort due to the uncertainty factors. The Uncertainty in effort estimation controlled by using fuzzy logic and the parameters of the Effort estimation are tuned by the Particle Swarm Optimization with Inertia Weight. We proposed three models for software effort estimation using fuzzy logic and PSO with Inertia Weight. The valuated effort is optimized using the incumbent archetypal and tested and tried on NASA software projects on the basis of three touchstones for assessment of software cost estimation models. A comparison of the all models is done and it is found that the incumbent archetypal cater better values.