Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

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Author: search.filters.author.Dr. Atif SaeedSubject: search.filters.subject.Information technology

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    Securing Email Communications: Advanced Approaches to Detecting Phishing Through Spam
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Amna Asif; SP23-RCS-006; Dr. Atif Saeed; LHR TP 9484
    The growing complexity of email-based communication has made it difficult to correctly identify the spam emails which provide significant hazards such as phishing, malware distribution and data breaches. Conventional spam detection algorithms that use static rule-based techniques like Keyword-Based Filtering, Blacklist Filtering, or rely on individual machine learning algorithms like Naïve Bayes, Logistic Regression, K-Nearest Neighbors, usually fall behind in increasing false positive and decreasing accuracy. This study used an optimal ensemble-based approach for spam email identification using Gradient Boosting Machine and Extreme Gradient Boosting algorithms and aims to enhance spam email detection accuracy through efficient hyper parameter tuning of these machine learning algorithms. Primarily, baseline models were trained on default parameters and then performance of these models was improved through randomized search cross validation method to examine the tuning space of hyper-parameters for efficient hyper-parameter values. By using the Enron dataset, a publicly available extensive collection of actual email data containing 33639 labeled emails, models were assessed using key metrics such as accuracy, F1-score, recall, precision, and ROC-AUC. The experimental results of this study presented that the performance of both algorithms was enhanced by the hyper-parameter tuning when contrasted with the baseline models. The modified XGBoost model outperformed the baseline version and other competitive models with an accuracy of 98.65%. Furthermore, on tuned parameters GBM performed well, demonstrating the effectiveness of ensemble algorithm approach. The results highlight the superiority of ensemble algorithms for challenging classification problems and validate the significance of hyper parameters modification in improving model performance. This research offers useful insights for enhancing cybersecurity measures in email communication systems by developing strong spam detection frameworks.
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    Cricket Squad Formation using Machine Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Shaukat Ali; SP19-RCS-022; Dr. Atif Saeed
    Cricket is primarily played in three formats around the world: test match, one day international (ODI), and twenty-twenty (T20). T20 has made a great revolution in the world of cricket. The Pakistan Cricket Board (PCB) arranges a tournament named the Pakistan Super League (PSL) every year, which is in T20 format. PSL is liked and watched by a large number of people, and it has a greater amount of statistical data. PSL is based on the Draft system for selecting players for making teams. This draft-based method for selecting players has different categories, each with its own constraints. 16 player squad must have five foreign players, and an 18 players squad could have six or five foreign players. A larger amount of money is used in the draft system. Players’ selection is one of the most important tasks for team formation. PSL team selection is made by team management, and it is very complex for humans to analyze all the previous statistics of the players for better selection. It is also true that human-based systems are not very efficient. It is very important to analyze players' performances for ease of selection and to make the right decision for the selection of players for teams by team management, coaches, and captains. In this thesis, machine learning techniques are used for squad selection in our model, which is named SFPML (Squad Formation in Pakistan Super League using Machine Learning). Important features of a batsman and bowler are used. Our model ranks the batsmen and bowlers based on their previous performances. If a new player enters the PSL tournament, his position in the league is determined by finding similarities among PSL players. Our thesis also attempts to predict the performances of players, such as how many runs a batsman will score, and how many wickets a bowler will take