About Mehedi

Greetings, I am a dedicated researcher specializing in Cybersecurity at Charles Sturt University, New South Wales, Australia, where I am actively pursuing a Ph.D. in Computing, Mathematics, and Engineering. With a robust background in Network Analysis and a focus on fortifying Network Intrusion Detection Systems against Adversarial Attacks using Deep Learning, my academic journey has been rich and diverse.

My passion for research blossomed during my tenure as a research assistant at the Time-Triggered Lab in Beihang University, Beijing, China, where I contributed for three years while pursuing my master's degree. I hold a Master of Engineering from Beihang University and a Bachelor of Science in Electrical and Electronic Engineering from the University of Chittagong, Bangladesh.

Throughout my career, I've authored and co-authored over 10 research articles, many of which have been published in esteemed journals and conferences, with several earning recognition as best papers. Notably, my contributions were honored with the first prize accolade from Beihang University, underscoring the significance and impact of my work in the field.

My research interests encompass a spectrum of topics including Adversarial Attacks, Intrusion Detection Systems, and the application of Generative AI in combating Cyber Attacks. Through my ongoing endeavors, I aim to advance the frontiers of Cybersecurity, making pivotal contributions to safeguarding digital infrastructures in an ever-evolving threat landscape.

I love to explore and learn new things ,especially in the network communication field. I am an introvert by nature except for that time when I take a class teach to my students. I like to travel,meet new people, and explore new places.

Current Research

Adversarial Attack for Network Intrusion Detection System

Adversarial attacks pose a significant challenge to Network Intrusion Detection Systems (NIDS) in safeguarding computer networks from malicious activities. These attacks involve crafting carefully designed inputs to deceive or evade the NIDS, allowing attackers to bypass security measures and carry out unauthorized actions. Adversarial attacks exploit the limitations and vulnerabilities of NIDS by manipulating network traffic patterns, payload contents, or system behaviors in ways that appear legitimate to the detection algorithms. Attackers may employ techniques such as obfuscation, mimicry, or poisoning to generate adversarial samples that mislead the NIDS into misclassifying malicious traffic as benign. The resilience of NIDS against adversarial attacks is crucial to maintain the integrity and security of computer networks. Researchers and cybersecurity professionals are actively exploring advanced techniques, such as adversarial machine learning and adaptive defense mechanisms, to enhance the robustness of NIDS against these evolving threats. Developing effective countermeasures against adversarial attacks requires a deep understanding of the attack strategies, the limitations of existing detection approaches, and the adaptation of novel technologies to strengthen the resilience of NIDS in the face of increasingly sophisticated adversaries.

On Going Project

Project 1: An Adaptive Lightweight Adversarial Attack Resistant IDS

I am currently working on an exciting research project titled "An Adaptive Lightweight Adversarial Attack Resistant IDS." This project aims to develop a cutting-edge Intrusion Detection System (IDS) that can effectively defend against adversarial attacks while maintaining a lightweight and efficient architecture. Adversarial attacks have emerged as a significant threat to traditional IDS, as they can cunningly manipulate network traffic to evade detection and compromise system security. To address this challenge, my research focuses on designing an adaptive and resilient IDS that incorporates advanced machine learning techniques and dynamic defense strategies. By leveraging the power of adversarial machine learning, the proposed IDS can learn and adapt to evolving attack patterns, enabling it to detect and mitigate adversarial attacks in real-time. The lightweight nature of the system ensures efficient resource utilization and scalability, making it suitable for deployment in various network environments. Through this project, I aim to contribute to the field of cybersecurity by developing a robust and practical solution that enhances the security of computer networks against sophisticated adversarial threats. The outcomes of this research have the potential to significantly improve the resilience and effectiveness of intrusion detection systems in safeguarding critical digital assets.

Project 2: Adversarial Attack Data Collection

In addition to my research on developing an adaptive and lightweight adversarial attack resistant IDS, I am also actively engaged in a project focused on "Adversarial Attack Data Collection." This project aims to curate a comprehensive dataset specifically designed to facilitate research and development in the field of adversarial attacks on Intrusion Detection Systems (IDS). Adversarial attacks pose a significant challenge to the effectiveness and reliability of IDS, as they involve crafting malicious inputs that can deceive and bypass traditional detection mechanisms. To address this challenge, a robust and diverse dataset is crucial for understanding the characteristics and patterns of adversarial attacks. Through this project, I am collaborating with a team of researchers to collect, analyze, and annotate a wide range of adversarial attack samples across various network scenarios and attack vectors. By leveraging advanced data collection techniques and engaging with the cybersecurity community, we aim to create a valuable resource that can support the development and evaluation of adversarial attack detection and mitigation strategies. The resulting dataset will be made available to the research community, fostering collaboration and accelerating advancements in the field of adversarial attack defense. This project underscores my commitment to contributing to the broader cybersecurity research ecosystem and promoting the development of more resilient and secure intrusion detection systems.

RESEARCH EXPERIENCES

MSc Researcher

Supervisor: Dr.He Feng,Associate Professor & Vice Dean,School of Electronic & Information Engineering,Beihang University,Beijing,China

Laboratory:BUAA TTTEthernet Time Triggered Technology Joint Laboratory

Institution: Beihang University,Beijing, China.

Topics:Time Sensitive Network analysis using several scheduling models

Duration:September 2019 to 2022.

BSc Researcher

Institution: University of Chittagong,Chittagong,Bangladesh

Research Group to Aid Child Development
Work for Physically Challenge Children

WORK EXPERIENCES

Research Assistant

Supvisor: Dr.He Feng,Associate Professor & Vice Dean,School of Electronic & Information Engineering,Beihang University,Beijing,China

Laboratory:BUAA TTTEthernet Time Triggered Technology Joint Laboratory

Institution: Beihang University,Beijing, China.

Topics:"Performance Analysis of Deterministic System using Time Sensitive Network"(Current Research Work)

Duration:September 2019 to Present.

Responsibilities: Analysis of Time Sensitive Network using several scheduling mechanisms as well as ensuring quality of service by using Network calculus.

Lecturer

Designation: Physics Teacher

Institution: Presidency International School, Chittagong,Bangladesh

Duration:July 2018 – July 2019

Responsibilities: Conducing A level and O level Classes along with corresponding Lab Classes

Industrial Work Expericence

Designation: Microsoft Technical Support Engineer

Company: Shanghai Wicresoft Co. Ltd (Microsoft Partner)

Duration: August 2022-September 2023

Responsibilities: Fixing Microsoft Exchange Online issues of end users

Educational Background

Doctor of Philosophy

Research Topic: A Lightweight Adaptive Adversarial Attack-Resistant IDS

Duration: 2023- Expected passing year 2026

School of Computing, Mathematics and Engineering

Faculty of Business, Justice and Behavioural Science

Charles Sturt University, New South Wales, Australia

Master of Engineering

Thesis Topic: Performace Analysis for Deterministic System using Time Sensitive Network

Duration: 2019-2022

CGPA: 3.76 out of 4.00

School of Electronic and Information Engineering

Beihang University, Beijing, China

Bachelor of Science in Engineering

Project Topic: A Smart Device to Identify the Bangladeshi Bank Notes for Blind People using Advanced Machine Learning Algorithm

Duration: 2014-2018

CGPA: 3.64 out of 4.00

Department of Electrical and Electronic Engineering

University of Chittagong, Chittagong,Bangladesh

Research Skills

Programming Skills

Python: Classical Machine Learning, Deep Learning

MATLAB: Used for Network Calculus,Network Communication,Real-Time Communication Simulation,RTC Tools

Programming C: Algorithm Analysis

Programming C++: Network Setup using C++

Android Application: Using for building Application

Software Skills

Omnet++

Proteus

PSpice

Hardware Skills

Standard Network Switch

TSN Switch

TTEthernet Switch

Arduino IDE

NodeMCU

Research Interests

Intrusion Detection System
Adversarial cyberAttack
Etheical Hacking
Network Calculus for Time Sensitive Network
Time Sensitive Network
Deterministic Network
Time-Triggered Traffic Analysis
Audio and Video Traffic Analysis
Scheduling Mechanism(CBS,TAS,ATS and so on)
Data Shrinking for real-time Communication
Industrial Internet of Things (IIoT)
Machine Learning using for data communcation

Publications

Q. Mamun,M. M. Hasan, R. Islam, M. Z. Islam and J. Gao, "Adversarial Attacks on Machine Learning-Based IDS for V2X Networks: A CICIoV2024 Study," 2025 VTC-Spring, OSLA, Norway [Accepted]
M. M. Hasan, R. Islam, Q. Mamun, M. Z. Islam and J. Gao, "Cross-Domain Adversarial Attacks: Translating Network Intrusion to CAN Bus Evasion in V2X Environments," 2025 VTC-Spring, OSLA, Norway [Accepted]
M. M. Hasan, R. Islam, Q. Mamun, M. Z. Islam and J. Gao, "Enhancing Network Intrusion Detection Systems: A Real-time Adaptive Machine Learning Approach for Adversarial Packet-Mutation Mitigation," 2024 22nd International Symposium on Network Computing and Applications (NCA), Bertinoro, Italy, 2024, pp. 227-235, doi: 10.1109/NCA61908.2024.00042.
Hasan M.M,Khan S,Ullah I, Hasan M.T,Feasibility Study on Time Triggered and Audio & Video Traffic over Standard Ethernet, Journal of Shanghai Jiao Tong University (Science) (Under Review)
Hasan M.M, Feng H, Khan S,Rahman M.S,Hasan M.T,Time-Triggered Shrinking Over Standard Ethernet Switch,IEEE CS BDC Summer Symposium 2022
Md Mehedi Hasan, He Feng, Shahrukh Khan, Md Ibrahim Ullah, Md Tanvir Hasan, Bipro Gain,” Improve Service Curve using Non-Overlapped Gate in Time Sensitive Network Switch”, in 2021 IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, China, Oct, 2021,DOI: 10.1109/ICCT52962.2021.9657974
Md Mehedi Hasan, He Feng, Shahrukh Khan, Md Ibrahim Ullah, Md Tanvir Hasan, Bipro Gain, “Timing Analysis for Optimal Points in Credit-based Shaper of Time-Sensitive Network”,2021 6th International Conference on Signal and Image Processing (ICSIP 2021), Nanjing, China, Oct, 2021DOI:10.1109/ICSIP52628.2021.9688723
Md Mehedi Hasan, He Feng, Md Tanvir Hasan, Bipro Gain, Md Ibrahim Ullah,” Improved and Comparative Endto-End Delay Analysis in CBS and TAS using Data Compression for Time Sensitive Network”, in IEEE The 3rd International Conference on Applied Machine Learning (ICAML 2021), in Changsha, Hunan, July,2021 DOI:10.1109/ICAML54311.2021.00049
Md Mehedi Hasan, Md Ibrahim Ullah, He Feng, Md Tanvir Hasan, Bipro Gain, “Convolutional Neural Network Based Smart System to Aid an Epileptic Patient”, in IEEE The 3rd International Conference on Applied Machine Learning (ICAML 2021),in Changsha,Hunan,July,2021 DOI 10.1109/ICAML54311.2021.00087
Md Mehedi Hasan, Shahrukh Khan,He Feng,Qiao Li,Syed Mohammad Masum,Md Tanvir Hasan,” Improved Endto-End Delay in CBS using Data Compression for Time Sensitive Network”, in IEEE 2021 2nd Information Communication Technologies Conference (ICTC 2021), Nanjing, May2021, DOI:10.1109/ICTC51749.2021.9441572
Alamgir Hossain, Faisal Bin Kashem, Md Mehedi Hasan, Sabikun Naher,Md Ismail Rahman,” A smart system for driver’s fatigue detection, remote notification and semi-automatic parking of vehicles to prevent road accidents”, in IEEE 2016 International Conference on Medical Engineering, Health Informatics and Technology(Medi Tech),Dhaka, December 2016 DOI:10.1109/MEDITEC.2016.7835371
A. M. Mahmud Chowdhury, Faisal Bin Kashem, Alamgir Hossain, Md Mehedi Hasan,” Brain Controlled Assistive Buzzer System for Physically Impaired People”, in IEEE 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Febuary,2017 DOI: 10.1109/ECACE.2017.7912988
Mohsan S.A.H,Hasan M.M, Mazinani Alireza, Sadiq M.A,Akhtar M.H, Islam A, Rokia L.S,A Systematic Review on Practical Considerations, Recent Advances and Research Challenges in Underwater Optical Wireless,International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020. (DOI) : 10.14569/IJACSA.2020.0110722
Mohsan S.A.H,Akhtar M.H,Aziz M.I,Hasan,M.M,Pervez M,Islam A, Banoori Impact of circular Field in Underwater Wireless Sensor Networks, International Conference on Intelligent and Interactive Systems and Applications,(IISA 2020) Emerging Trends in Intelligent and Interactive Systems and Applications pp 803-81,DOI: 10.1007/978-3-030-63784-2_99