Browsing by Author "Abdulsalam, S. O."
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Item Building a Spammer Monitoring System Using Heuristic Rule-Based Approach(International Journal of Engineering and Technology, Centre of Professional Research Publications, 2012) Adewole, K. S.; Babatunde, R. S.; Isiaka, R. M.; Abdulsalam, S. O.Spam is a major problem of electronic mail system that has enjoyed extensive discourse. E-mail has been greatly abused by spammers to disseminate unwanted messages and spread malicious contents. Several anti-spam systems developed have been greatly abused and this is as evident in the proliferation of Spammer’s activities. Observing this fact, a protective mechanism to countermeasure the ever-growing spam problem is indeed inevitable. In this paper, a heuristic approach is proposed which employs a standard normalized Spammer’s languages harvested from Google and Yahoo spam language data set to build the knowledge base. The spam languages were prioritized based on the frequency of occurrence in the two global data sets. A threshold of 5% was established for a user without spamming history while 3% was set for a suspected spammer. A platform independent system was designed and implemented to monitor users’ mail in real time. As soon as the threshold is reached the user will be alerted and the suspected mail will be cancelled. The developed model was evaluated for accuracy and effectiveness using three composed email messages. It is recommended among others that this spam preventive model be incorporated in the architecture of every Internet Service Provider.Item Development of an Intrusion Detection System in a Computer Network(International Journal of Computers & Technology (IJCT), 2014) Babatunde, R. S.; Adewole, K. S.; Abdulsalam, S. O.; Isiaka, R. M.The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.Item Frequent Pattern and Association Rule Mining from Inventory Database using Apriori Algorithm(African Journal of Computing & ICT, 2014) Adewole, K. S.; Akintola, A. G.; Abdulsalam, S. O.; Ajiboye, A. R.Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.Item Predicting Nigeria Budget Allocation Using Regression Analysis: A Data Mining Approach(The Journal of Computer Science and Its Applications, 2014) Adewole, K. S.; Mabayoje, M. A.; Abdulsalam, S. O.; Ajao, J. F.Budget is used by the Government as a guiding tool for planning and management of its resources to aid in effective decision-making. Data mining is one of the most vital areas of research with the objective of finding meaningful information from large datasets. The delay in the preparation of budget of the Federation by the Government has become incessant issue in the running of affairs of the country. This is evident in the delay in implementation of the previous budgets in the country; hence, the need for automated system to tackle the setback. In this paper, regression analysis which is one of the data mining techniques is employed to predict budget allocation from Nigeria budget dataset. 200 records consisting of the budget allocation summary for the year 2008, 2009, 2010, 2011, and 2012 across 40 data points containing Ministries, Departments, Commissions and Agencies (MDCAs) were used. A web-based data mining tool that employed linear regression to predict both Nigeria budget allocation across the 40 data points and the overall budget summary allocation of the Federation is proposed. The proposed data mining software predicted N1,803,196,024,657.40, N1,871,754,338,112.68 and N2,007,780,403,902.98 for the year 2013, 2014 and 2015 respectively. The tool is found capable of discovering interesting patterns in the data and for predicting budget allocation.Item Stock trend prediction using regression analysis–a data mining approach(ARPN Journal of Systems and Software, 2011) Abdulsalam, S. O.; Adewole, K. S.; Jimoh, R. G.Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.