Manager IT Operations
National University of Science & Technology (NUST)
مجموع سنوات الخبرة :10 years, 8 أشهر
Responsibilities include management of KOHA (Library Management System) and DSpace (Institutional Repository) on VMWare ESXI VMs. Error free functioning of RFID Self Services Kiosk, Books Drop Box system, online Institution’s Library catalogue (OPAC), online Study Room Reservation System and management of contents on website. Routine backups of both KOHA and DSpace.
Management and administration of Active Directory DC and Domain Name Server, filesharing server, printing server, and Kespersky antivirus server. Made sure all the network switches, UPS, and desktops in labs remain functional. Installation of multiple Operating systems and software on user requirements.
As a Cloud Computing trainer under Govt. of Pakistan’sKamyab Jawan Program, participants were trained to workon public and private clouds. Upon ending of this project, many participants got job in different organizations:Technologies worked on: Virtual Box, Hyper-V, VMWare ad
AWS Cloud.
Courses taught:
Computer Applications
Introduction to C
During my Masters I did research on multiple attacks on network. My work was focused on both known and unknown attacks and to capture those attacks machine learning techniques we studied and explored. A novel intrusion detection system was proposed to catpture these anomalies. Abstract of my work is given below: “In today's internet-dependent world, cyber-attacks are a growing concern for organizations. Traditional security measures often fall short against sophisticated intruders. To counter this, Network Intrusion Detection Systems (NIDS) are crucial, with machine learning being a key tool in their development. However, current machine learning-based NIDS face challenges like limited detection capabilities and slow computation times. To address these, we've introduced a novel NIDS using Support Vector Machine (SVM) algorithms. Our model employs advanced signal processing techniques like Discrete Wavelet Transform (DWT) to extract pertinent features from data, while statistical analysis aids in feature selection. Our proposed NIDS boasts high detection rates, accuracy, and efficiency, effectively identifying both common and uncommon cyber-attacks. By leveraging cutting-edge machine learning techniques, our model provides heightened security, offering robust protection against evolving digital threats.”
Designed a Traffic Monitering and Best Path selection project which enabled users to avoid dense traffice areas
