Dr. Saba Mahmood

PhD Theme/Topic: Anomaly based Inside Threat Detection in Smart Cities

Supervisor: Dr. Saba Mahmood, Snr Assistant Professor
Contact #:ext 1292
Email:smahmood.buic@bahria.edu.pk
Campus/School/Dept: BUIC/SEAS/CS
RAC Approved Supervisor for Research Areas: IoT, Security ,Trust framework , Data Sharing, Machine Learning

Supervisory Record:
PhD Produced:
PhD Enrolled:1
MS/MPhil Produced:12
MS/MPhil Enrolled:5

Topic Brief Description:

Anomaly-Based Threat Detection in Smart Cities refers to the use of advanced technologies to identify unusual patterns or behaviors that may indicate potential threats or security risks. With the integration of IoT (Internet of Things), AI, and big data analytics, smart cities aim to ensure safety, security, and resilience in urban environments. Anomaly based systems leverage statistical analysis, machine learning, deep learning and other computational techniques to identify irregularities, which may indicate errors, threats, or unusual activities.

Research Objectives/Deliverables:

• To design and implement machine learning/deep learning algorithms for real-time anomaly detection.
• To test and deploy anomaly detection systems across various smart city domains (e.g., transportation, utilities, healthcare).
• To develop methodologies for identifying and mitigating insider threats in smart city systems.

Research Questions: 

1. Which machine learning or AI models are most effective for detecting anomalies in IoT networks?
2. How can big data analytics enhance anomaly detection in large-scale urban settings?
3. How can anomaly detection improve public safety and crime prevention?

Candidate’s Eligibility Profile:

1. The applicant must have an MS/MPhil/Equivalent degree in Computing or allied areas with CGPA > 3.0. Besides, applicants must have knowledge of machine learning, deep learning with strong experience of programming in python.
2. The candidate must have completed an MS thesis in computing or a related field, with at least one publication in an international conference or a reputable HEC-recognized journal. The ideal candidate should excel in an international setting and possess strong communication skills to actively contribute to collaborative research efforts.
3. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.