A
Asad Arfeen
Researcher at NED University of Engineering and Technology
Publications - 28
Citations - 97
Asad Arfeen is an academic researcher from NED University of Engineering and Technology. The author has contributed to research in topics: Computer science & Internet traffic. The author has an hindex of 3, co-authored 19 publications receiving 23 citations. Previous affiliations of Asad Arfeen include University of Engineering and Technology, Lahore.
Papers
More filters
Journal ArticleDOI
A generalized machine learning‐based model for the detection of DDoS attacks
TL;DR: An integrated feature selection (IFS) method which consists of three stages and integration of two different methods to select features which highly contribute to the detection of various types of DDoS attacks, and shows that the performance of the model improves if feature space is reduced by 77%.
Journal ArticleDOI
Smart Design of Surgical Suture Attachment Force Measurement Setup Using Tactile Sensor
TL;DR: A novel, low-cost, and smart design setup to measure the needle attachment force of surgical suture in order to check attachment quality and verify the surgical sutures manufacturing standards is presented.
Journal ArticleDOI
A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks
Maraj Uddin Ahmed Siddiqui,Faizan Qamar,Syed Hussain Ali Kazmi,Rosilah Hassan,Asad Arfeen,Quang Ngoc Nguyen +5 more
TL;DR: In this article , the authors analyzed the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks and identified various relevant topics in each section that may help to make the future wireless systems robust.
Journal ArticleDOI
The role of the Weibull distribution in modelling traffic in Internet access and backbone core networks
TL;DR: The results of this article will help researchers use simple renewal processes as a better alternate to complex self-similar or modulated stochastic processes for modelling all structural components of Internet traffic at any time scale with physical justifications.
Journal ArticleDOI
Process based volatile memory forensics for ransomware detection
TL;DR: A framework for volatile memory acquisition at regular time intervals to analyze the behavior of individual processes in memory to classify malicious and benign processes efficiently through machine learning as compared to conventional techniques is developed.