Saqib hayat Bhutta

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