Everyone connected and using the Internet is concerned regarding the security and also the privacy of their sensitive information available on the Internet. As authentication is the fundamental part of security, there are different authentication mechanisms through which the systems can be secured. The password-based authentication mechanism is a cheap and easy method for enforcing authentication in the systems for many years. The weakest aspect in password security is human, as they choose weak and easy to guess passwords or a highly secure and complex password which might be difficult to remember and recover the password. On the other hand, Dictionary and Brute force attacks are widely used to compromise the passwords of the users over the Internet. In this paper, a password generation system is proposed which generates a password based on the user’s input like, time and location data. The system generates a password that is highly secure, easy to remember, easy to recover, and can effectively defend against Brute force and dictionary attacks. The generated passwords have been checked in three online password checkers, which verifies that the system is generating highly secure and crack resistant passwords. The system is implemented using PHP scripting language with a user-friendly environment.

Due to emerging of amalgam amount of data from variety sources, the data mining has become a hot trend in field of Computer Science. Data mining extracts useful pattern and information from huge amount of existing data with the help of machine learning algorithms that can be helpful in solving many sophisticated problems. Telecommunication companies also generates big amount of data from providing services to their customers, besides that telecommunication companies suffers from many problems like fraud, Customer churn and …etc. The generated amount of data from these companies can help them to address the solution for their problems such as Customer Churn. Customer churn indicates to the event when a customer stops using the service of a company and starts to use the service of another company. Churning of a Customer plays a vital role in having a sustainable business development for a telecommunication company since attracting new customers do not profit a company without retaining the old ones. Data mining can address the problem by predicting the occurrence of customer churn in Telecom Company, which helps the company to be proactive in this event and can have the chance to retain them before the churn occurs. In this study, I have chosen two open Telecom Churn data sets and have applied Support Vector Machine, Logistic Regression and Decision Tree Machine Learning Algorithms on each data sets independently, which conclude my work to six experiments. I have used k-fold cross validation as validation technique during my experiments and confusion matrix for calculating the accuracy of each algorithm, the result of experiments will provide the accuracy of each algorithm in churn prediction for each data set. At the end we will have a general comparison table from all six experiments which will show the algorithms performance summary and will indicate which algorithm will outperform the others.