Browsing by Author "Babatunde, R. S."
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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 A novel approach to outliers removal in a noisy numeric dataset for efficient mining(Ilorin Journal of Computer Science and Information Technology, 2016) Ajiboye, A. R.; Adewole, K. S.; Babatunde, R. S.; Oladipo, I. D.Data pre-processing is a key task in the data mining process. The task generally consumes the largest portion of the total data engineering effort while unveiling useful patterns from datasets. Basically, data mining is about fitting descriptive or predictive models from data. However, the presence of outlier sometimes reduces the reliability of the models created. It is, therefore, essential to have raw data properly pre-processed before exploring them for mining. In this paper, an algorithm that detects and removes outliers in a numeric dataset is proposed. In order to establish the effectiveness of the proposed algorithm, the clean data obtained through the implementation of the proposed approach is used to create a prediction model. Similarly, the clean data obtained through the use of one of the existing techniques is also used to create a prediction model. Each of the models created is simulated using a set of untrained data and the error associated with each model is measured. The resulting outputs from the two approaches reveal that, the prediction model created using the output from the proposed algorithm has an error of 0.38, while the prediction model created using the cleaned data from the clustering method gives an error of 0.61. Comparison of the errors associated with the models created using the two approaches shows that, the proposed algorithm is suitable for cleaning numeric dataset. The results of the experiment also unveils that, the proposed approach is efficient and can be used as an alternative technique to other existing cleaning methods.