Projects
Enhancing Privacy in Wireless Communications: AP-Initiated Dynamic MAC Address Re-Randomization
MSc Thesis, KTH Royal Institute of Technology, Stockholm, Sweden
Mar 2024 – Nov 2024
Modern wireless networks rely on MAC addresses for device identification, but once a device completes authentication, its MAC address remains static, making it susceptible to long-term tracking. Existing MAC randomization techniques only protect devices before association and fail to provide privacy during an active connection. My research proposes an AP-triggered MAC re-randomization protocol, ensuring that MAC addresses change dynamically even after key negotiation, mitigating tracking risks in real-time while preserving seamless connectivity.
To validate this approach, I implemented kernel-level modifications in mac80211 and conducted extensive simulations using Mininet-WiFi, mac80211_hwsim, and wmediumd. Results showed that without re-randomization, devices were 100% trackable in all network conditions. With the proposed AP-triggered scheme, MAC linkability was reduced from 100% (1 station) to 15% (11 stations), making large-scale device tracking infeasible. Performance evaluations demonstrated minimal overhead, with an average UDP packet loss below 0.1% and stable throughput (~30 Mbps), confirming the protocol's practical feasibility.
This work bridges the gap between security research and real-world implementation, providing a lightweight, scalable solution to improve WiFi privacy at scale. Future work includes hardware validation, adaptive re-randomization intervals, and exploring AI-driven anomaly detection to counter evolving tracking techniques.
Enterprise Network Penetration Testing (Capture The Flag)
Course Project, KTH Royal Institute of Technology
Aug 2023 – Oct 2023
Conducted a penetration test on a simulated corporate network, exploiting vulnerabilities in web applications,
databases, and network services. Captured 20+ security flags by performing reconnaissance, password cracking,
SQL injection, remote code execution, and privilege escalation. Successfully compromised Windows and Linux
environments using Metasploit, Hydra, Burp Suite, SQLMap, and Mimikatz, demonstrating real-world offensive security
techniques. The project emphasized post-exploitation tactics, lateral movement, and credential dumping, reflecting
industry-standard red teaming methodologies.
Scalable and Secure Network Infrastructure for Distributed Offices
Course Project, KTH Royal Institute of Technology
Jan 2023 – Mar 2023
As part of the Building Networked Systems Security (BNSS) course at KTH, this project involved designing and implementing a
robust, enterprise-level secure network infrastructure for ACME Scandinavia. The goal was to provide secure remote access,
identity-based authentication, intrusion detection, and encrypted communication across distributed office branches.
The solution utilized a layered security approach by integrating multiple security components. A
site-to-site OpenVPN tunnel was deployed to establish a secure connection between Stockholm and London branches, ensuring
encrypted traffic between the two locations. Additionally, remote workers were provided certificate-based authentication via
FreeRADIUS, preventing unauthorized access.
To protect against network intrusions, an Intrusion Detection System (IDS) using SNORT was implemented. Two separate SNORT
instances were deployed to monitor both the physical network traffic and VPN connections, allowing real-time detection of malicious
activity. All alerts were logged and could be further analyzed using Wireshark.
The system also featured a reverse proxy with strict firewall rules, ensuring that only authorized users could access internal
resources. Secure file exchange and communication were facilitated through a self-hosted Nextcloud instance, which was protected by
two-factor authentication (2FA) to prevent unauthorized access. Additionally, Secure DNS (DoH via Cloudflare) was implemented to
mitigate DNS spoofing attacks.
Key Outcomes: The designed architecture successfully established a scalable, high-security enterprise network that prevented
unauthorized access, secured sensitive data, and allowed seamless remote access without compromising security.
Facial Recognition-Based Classroom Attendance System
B.Tech Thesis, Amrita Vishwa Vidyapeetham, Bangalore, India
Feb 2022 – Jul 2022
Developed an automated attendance system using facial recognition and OpenCV.
The system was implemented as a product within the university to streamline attendance tracking efficiently.
