Browsing by Author "Rahman, Tahsinur"

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PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework
(BRAC University, 2023-01) Rahman, Tahsinur; Ahmed, Nusaiba; Monjur, Shama; Haque, Fasbeer Mohammad; Kabir, Naweed; Hossain, Dr. Muhammad Iqbal
As the world is moving more and more towards a digital era, a great majority of data is transferred through a famous format known as PDF. One of its biggest obstacles is still the age-old problem: malware. Even though several anti-malware and anti-virus software exist, many of which cannot detect PDF Malware. Emails carrying harmful attachments have recently been used in targeted cyber attacks against businesses. Because most email servers do not allow executable files to be attached to emails, attackers prefer to use non-executable files like PDF files. In various sectors, machine learning algorithms and neural networks have been proven to successfully detect known and unidentified malware. However, it can be difficult to understand how these models make their decisions. Such lack of transparency can be a problem, as it is important to understand how an AI system is making decisions in order to ensure that it is acting ethically and responsibly. In some cases, machine and deep learning models may make biased or discriminatory decisions or have unintended consequences. Hence, Explainable AI comes into play. To address this issue, this paper suggests using machine learning algorithms SGD(Stochastic Gradient Descent), XGBoost Classifier, and deep learning algorithms Single Layer Perceptron, ANN(Artificial Neural Network) and check their interpretability using Explainable AI (XAI)’s SHAP framework to classify a PDF file being malicious or clean for a global and local understanding of the models.
Prediction of diabetes induced complications using different machine learning algorithms
(BRAC University, 2018-08) Rahman, Tahsinur; Farzana, Sheikh Mastura; Khanom, Aniqa Zaida; Alam, Md. Ashraful
Machine Learning is an ever expanding field of Artificial Intelligence which uses huge amount of data to develop algorithms that can detect patterns and systems. One such application of Machine Learning is developing predictive models for disease prediction. On the other hand, in spite of huge advancements in Medical Science and discovery of complex diseases making everyone more health conscious, there is no way in Medical Science to predict prevalence of diseases. However, upon having relevant data Machine Learning methods can predict onset of many diseases. This paper presents the comparative analysis of different Machine Learning algorithms and their results in predicting the health complications related to Diabetes Mellitus. Diabetes Mellitus is a medical condition of the Pancreas in which the body‘s ability to produce or respond to the hormone, Insulin, diminishes. As a result, over time it damages other organs in the body- primarily Kidney, Liver, Eyes, Heart and Brain. Since in most cases the threats posed by Diabetes are not known before it is too late, hence it requires a great amount of consciousness in order to prevent onset of other related diseases. To this day, there is no prevention of Diabetes, since it is largely dependent on the genetics of a person. However, if a person is monitored closely it is possible to indicate Diabetes related complications. This proposed model uses time series data of a year that contains 164 features including results of different pathological tests. Methods such as Logistic Regression, SVM, Naïve Bayes, Decision Tree and Random Forest have been used in a supervised environment to predict the probability of Diabetes induced Nephropathy and Cardiovascular disease. PCA was applied beforehand to reduce the dimensionality of the dataset. Decision Tree without PCA produced the best results for Nephropathy with an AUC score of 0.87. While Naïve Bayes without PCA produced the best results for Cardiovascular disease, with an AUC score of 0.74. In summary, the model proposed in this paper predicts the risk of Nephropathy better than the risk of Cardiovascular disease.