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IP-Spoofing Vulnerability Protection Software
for Data Communication Network Operators
--Fidelis I Onah
The introduction of malformed or forged (spoofed) IP source addresses into a network by online criminals is increasingly a challenging task for network administrators. Every data breach or costly identity-theft case reported in the media erodes the public’s confidence in the security of online transactions, thereby jeopardizing the ability of organizations to conduct transactions online. The detection and prevention of malicious behavior that can undermine corporate security policies in a large network environment are therefore of primary concern. This paper presents an intelligent system for detecting network IP-spoofing vulnerabilities in real time. A system of cooperative multi-agents using collaborative information and mobile agent technologies is applied to a pool of real estate management business application (implemented in PHP). The agents travel through the distributed system and interpret and apply rule-learning algorithms to the web-based transaction data. The information generated serves as input to a binary file and running the binary file generates a binary output decision that can be used to provide enhanced real-time IP-spoofing vulnerability detection. This enables operators to respond to fraud by detection, service denial and prosecutions against fraudulent users. A proactive login control system is also implemented to monitor IP addresses and the files they are accessing over the Internet. The multi-agent approach is shown to be effective in detecting fraudulent transaction data from Real Estate Management network environments. The positive result obtained during the site testing in an intranet environment will produce the same result for Internet. This software has been tested for special functional features such as search indexes, user input forms and other marketing tools using common browsers such as Microsoft Internet Explorer, Opera and Mozilla Fire Fox. © 2016 IUP. All Rights Reserved.
Particle Swarm Optimization for Feature Selection:
A Study on Microarray Data Classification
--Ajay Kumar Mishra, Subhendu Kumar Pani and Bikram Kesari Ratha
DNA microarray technology allows simultaneous monitoring and measuring of thousands of gene expression activation levels in a single experiment. Data mining techniques such as classification is widely used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, particle swarm optimization technique was used for feature selection, and the classification performance of several popular classifiers was analyzed on a set of microarray datasets. The results conclude that particle swarm optimization technique provides better results compared to genetic algorithm. © 2016 IUP. All Rights Reserved.
Innovative Cooling Strategies
for Cloud Computing Data Centers
--Manju Lata and Vikas Kumar
The growing demand for computational power combined with the shift to cloud computing model has led to the establishment of large-scale data centers around the globe. With the increasing number of cloud deployments, the size of data centers is also increasing, thus increasing the amount of heat generated by these data centers. Consequently, cooling of the data centers becomes vital from the business and environmental perspective. Cloud computing service providers are using a number of innovative cooling strategies to beat this heat. In this paper, a systematic review has been presented for the traditional and new techniques for data center cooling. This will be of great help in the design of energy-efficient data centers. © 2016 IUP. All Rights Reserved.
Risk Perception and Adoption of Mobile Banking Services:
A Review
--Prerna Bagadia and Alok Bansal
Mobile banking enables customers to access their bank account, check their balance or conduct financial transactions through a mobile device. While the convenience of having access to banking information anytime and anywhere provided by mobile banking is empowering to the consumers on the one side, the possibility of loss, theft or exposure to malware of the stored personal financial information presents obvious risks on the other side. Although technology and applications for mobile services offer low costs, increase computational power and provide ease of use to consumers, mobile banking service market is still in its infancy stage. Hence, it becomes necessary for bankers to study the factors that are suitable and adoptable for banking customers, which will also help them in designing mobile services. This paper reviews the mobile banking literature and the factors antecedent to user’s behavioral intentions through the lenses of various adoption theories. It was found that mobile banking adoption is a complex and multifaceted process, and considering customers’ risks (which includes security, financial, privacy, social, time/convenience and performance risks) along with it is also important than analyzing adoption alone. © 2016 IUP. All Rights Reserved.
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