K
Kamran Shaukat
Researcher at University of Newcastle
Publications - 53
Citations - 1190
Kamran Shaukat is an academic researcher from University of Newcastle. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 9, co-authored 42 publications receiving 264 citations. Previous affiliations of Kamran Shaukat include University of the Punjab & College of Information Technology.
Papers
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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
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Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
Kamran Shaukat,Suhuai Luo,Vijay Varadharajan,Ibrahim A. Hameed,Shan Chen,Dongxi Liu,Jiaming Li +6 more
TL;DR: A brief review of different machine learning techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks, and the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity.
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A Review of Content-Based and Context-Based Recommendation Systems
TL;DR: This study has concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, the system can also recommend items according to the user’s interests.
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An Investigation of Credit Card Default Prediction in the Imbalanced Datasets
Talha Mahboob Alam,Kamran Shaukat,Ibrahim A. Hameed,Suhuai Luo,Muhammad Umer Sarwar,Shakir Shabbir,Jiaming Li,Matloob Khushi +7 more
TL;DR: A model is developed for credit default prediction by employing various credit-related datasets and the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset, and the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers.
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A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data
Matloob Khushi,Kamran Shaukat,Talha Mahboob Alam,Ibrahim A. Hameed,Shahadat Uddin,Suhuai Luo,Xiaoyan Yang,Maranatha Consuelo Reyes +7 more
TL;DR: In this article, the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) was used to identify the best imbalance techniques suitable for medical datasets.