CONFERENCE/JOURNAL PAPERS:
G. Srikar, M. T. Hossain, Binh Nguyen, Thang Nguyen, Truong X. Nghiem and Hung M. La, "CRADLE: Cooperative Adaptive Decentralized Learning and Execution for UAV network to monitor Wildfire Front," 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE 2025), LA, USA.
M. T. Hossain, S. Badsha, H. (Jim) La, S. Islam, and I. Khalil, “Exploiting Gaussian Noise Variance for Dynamic Differential Poisoning in Federated Learning,” IEEE Transactions on Artificial Intelligence, 2024.
M. T. Hossain, H. (Jim) La, and S. Badsha, “RAMPART: Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation,” ACM Journal on Autonomous Transportation Systems (ACM JATS) 2023.
M. T. Hossain, and H. La, "Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks," The 22th International Conference on Machine Learning and Cybernetics (ICMLC).
M. T. Hossain, H. La, S. Badsha, and Anton Netchaev, “BRNES: Byzantine Robust Neighbor Experience Sharing in Differentially Private Multiagent Reinforcement Learning,” The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023).
M. T. Hossain, S. Islam, S. Badsha and H. Shen, “DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning,” 17th International Conference on Mobility, Sensing and Networking (IEEE MSN 2021), 2021.
M. T. Hossain, S. Badsha and H. Shen, “Privacy, Security and Utility Analysis of Differentially Private CPES Data,” 9th IEEE Conference on Communications and Network Security (CNS), 2021.
M. T. Hossain, S. Badsha, and H. Shen, "PoRCH: A novel consensus mechanism for blockchain enabled future SCADA systems in Smart Grids and Industry 4.0", Proceeding of IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS 2020).
Bhattacharjee, Arpan, et al. "Vulnerability characterization and privacy quantification for cyber-physical systems," 2021 IEEE International Conference on Cyber, Physical and Social Computing, 2021.
Islam, Shafkat, et al. "A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid," 2023 IEEE ISGT North America, (2023)
M.T. Hossain, Kazi Sharfuddin Nayem, Md.Hasibul Jamil and Prasenjit Mazumder," Design and Implementation of a RFID-based Train Ticketing System: Perspective Bangladesh", Proceeding of 2nd International Conference on Mechanical Engineering and Renewable Energy (ICMERE), ICMERE2013-PI-152.
Md. Hasibul Jamil, Rakesh Ghosh, and M. T. Hossain," Design and Performance evaluation of An Automatic Indoor temperature control system", Proceeding of 2nd International Conference on Mechanical Engineering and Renewable Energy (ICMERE), ICMERE2013-PI-150."
Md. Hasibul Jamil, Prasenjit Mazumder, M. T. Hossain, and Pranab Chakma, "Design and Performance Evaluation of Vehicle In-Front Obstacle Detection & Safety Alert System", Proceeding of 2nd International Conference on Mechanical Engineering and Renewable Energy (ICMERE), ICMERE2013-PI-153."
M. T. Hossain, Md.Hasibul Jamil, Pranab Chakma, Md. Rezwan Ferdoush, and Azwad Tamir, "Design and Performance Evaluation of a Wireless Power Transmission System", in review, Submitted to International Conference on Electrical, Computer and Communication Engineering (ECCE 2017). Submitted paper id: 265.
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning
Federated learning (FL) has become an emerging machine learning technique lately due to its efficacy in safeguarding the client's confidential information. Nevertheless, despite the inherent and additional privacy-preserving mechanisms (e.g., differential privacy, secure multi-party computation, etc.), the FL models are still vulnerable to various privacy-violating and security-compromising attacks (e.g., data or model poisoning) due to their numerous attack vectors which in turn, make the models either ineffective or sub-optimal. Existing adversarial models focusing on untargeted model poisoning attacks are not enough stealthy and persistent at the same time because of their conflicting nature (large scale attacks are easier to detect and vice versa) and thus, remain an unsolved research problem in this adversarial learning paradigm. Considering this, in this paper, we analyze this adversarial learning process in an FL setting and show that a stealthy and persistent model poisoning attack can be conducted exploiting the differential noise. More specifically, we develop an unprecedented DP-exploited stealthy model poisoning (DeSMP) attack for FL models. Our empirical analysis on both the classification and regression tasks using two popular datasets reflects the effectiveness of the proposed DeSMP attack. Moreover, we develop a novel reinforcement learning (RL)-based defense strategy against such model poisoning attacks which can intelligently and dynamically select the privacy level of the FL models to minimize the DeSMP attack surface and facilitate the attack detection.
Privacy, Security, and Utility Analysis of Differentially Private CPES Data
Differential privacy (DP) has been widely used to protect the privacy of confidential cyber-physical energy systems (CPES) data. However, applying DP without analyzing the utility, privacy, and security requirements can affect the data utility as well as help the attacker to conduct integrity attacks (e.g., False Data Injection(FDI)) leveraging the differentially private data. Existing anomaly-detection-based defense strategies against data integrity attacks in DP-based smart grids fail to minimize the attack impact while maximizing data privacy and utility. To address this challenge, it is nontrivial to apply a defensive approach during the design process. In this paper, we formulate and develop the defense strategy as a part of the design process to investigate data privacy, security, and utility in a DP-based smart grid network. We have proposed a provable relationship among the DP parameters that enable the defender to design a fault-tolerant system against FDI attacks. To experimentally evaluate and prove the effectiveness of our proposed design approach, we have simulated the FDI attack in a DP-based grid. The evaluation indicates that the attack impact can be minimized if the designer calibrates the privacy level according to the proposed correlation of the DP parameters to design the grid network. Moreover, we analyze the feasibility of the DP mechanism and QoS of the smart grid network in an adversarial setting. Our analysis suggests that the DP mechanism is feasible over existing privacy-preserving mechanisms in the smart grid domain. Also, the QoS of the differentially private grid applications is found satisfactory in adversarial presence.
PoRCH: A novel consensus mechanism for blockchain-Enabled future SCADA systems in Smart Grids and Industry 4.0
Smart Grids and Industry 4.0 (I4.0) are neither a dream nor a near-future thing anymore, rather it is happening now. The integration of more and more embedded systems and IoT devices is pushing smart grids and I4.0 forward at a breakneck speed. To cope up with this, the modification of age-old SCADA (Supervisory Control and Data Acquisition) systems in terms of decentralization, near-real-time operation, security, and privacy is necessary. In this context, blockchain technology has the potential of providing not only these essential features of the data acquisition process of future SCADA systems but also many other useful add-ons. On the other side, it is evident that various type of security breach tends to take place more during any economic turmoil. These can cause even more serious devastation to the global economy and human life. Thus, it is necessary to make our industries robust, automated, and resilient with secured and immutable data acquiring systems. This paper deals with the implementation scopes of blockchain in the data acquisition part of SCADA systems in the area of the smart grid and I4.0. There are several consensus mechanisms to support blockchain integration in the field of cryptocurrencies, vehicular networks, healthcare systems, e-commerce, etc. But little attention has been paid to developing efficient and easy-to-implement consensus mechanisms in the field of blockchain-enabled SCADA systems. From this perspective, a novel consensus mechanism, which we call PoRCH (Proof of Random Count in Hashes), with a customized mining node selection scheme has been proposed in this paper. Also, a small-scale prototype of a blockchain-enabled data acquisition system has been developed. The performance evaluation of the implemented prototype shows the benefits of blockchain technology.
A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid
Smart grid (SG) systems enhance grid resilience and efficient operation, leveraging the bidirectional flow of energy and information between generation facilities and prosumers. For energy demand management (EDM), the SG network requires computing a large amount of data generated by massive Internet-of-things sensors and advanced metering infrastructure (AMI) with minimal latency. This paper proposes a deep reinforcement learning (DRL)-based resource allocation scheme in a 6G-enabled SG edge network to offload resource-consuming EDM computation to edge servers. Automatic resource provisioning is achieved by harnessing the computational capabilities of smart meters in the dynamic edge network. To enforce DRL-assisted policies in dense 6G networks, state information from multiple edge servers is required. However, adversaries can ``poison'' such information through false state injection (FSI) attacks, exhausting SG edge computing resources. Toward addressing this issue, we investigate the impact of such FSI attacks with respect to abusive utilization of edge resources, and develop a lightweight FSI detection mechanism based on supervised classifiers. Simulation results demonstrate the efficacy of DRL in dynamic resource allocation, the impact of the FSI attacks, and the effectiveness of the detection technique.
Vulnerability characterization and privacy quantification for cyber-physical systems
Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while overprotecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information
Design And Implementation of an RFID-based Train Ticketing System: Perspective Bangladesh
Artificial ticket crisis in Railway is the worst problem the passengers are facing nowadays in Bangladesh. Though Bangladesh Railway (BR) has introduced an online ticketing system (e-ticket) almost a year ago, the fruits have not been ripened yet. Corruption in the railway ticketing system still prevails all over the country. Here comes the need of adopting such a ticketing system that will prevent scalping as well as reduce the pain of collecting tickets. The system should be user-friendly, automated, cost-effective and it should have the ability to store personal data so that no one can get a chance to siphon off tickets to black marketers. This paper deals with the implementation of a system that has all the qualities stated above. The total system includes matching the RFID card with the existing database which should be made up with the help of our national ID cards and delivery of the available automated generated ticket according to the requirements of the users instantly. An RFID card must have a maximum ticket purchasing limit for a certain period of time. As a consequence, no one gets a chance of scalping and if it occurs somehow, the identification of blacker will be just a matter of time via monitoring the usages of cards. Thus, the system reduces black marketing. Moreover, since the system is automatic, the need for manpower as well as the cost behind it is reduced. Setting up a reasonable number of ticket selling booths will make the purchasing of the ticket more comfortable and more importantly time will be saved too. So, the implementation of an RFID-based train ticketing system can bring up a revolutionary change in the field of railway service in this country and both the government and people of Bangladesh will be benefited.
Details can be found HERE
Design And Performance Evaluation Of An Automatic Indoor Temperature Control System
This work proposes a system based on automation. In the present world, the big challenge is to minimize production time and get the final product more efficiently. As a result, we are thinking about the automatic process. This project is based on automatic thermal control of a closed space. The temperature of the environment, the target location, will be shown on an LCD display. We have used a temperature sensor, LM35, which senses the surrounding temperature & converts this temperature to proportional analog voltage & this voltage is given to Micro-controller. When the temperature of the environment exceeds a pre-defined critical level the heater ( working as an actuator) will be switched off automatically. This whole measurement, detection, and action process were programmed in a micro-controller. In the industry, for doing this job, only a few workers are needed and monotony for work can be removed. So, the concern is an Automatic room temperature control which is more economical and has an ergonomic design for mass production.
Details can be found HERE
Design and performance evaluation of vehicle in front obstacle detection & Safety alert system
In today's world safety and security plays an important role, hence we tend to provide a good safety and security system while traveling. Vehicles are important in today's fast-paced society. Hence, acquiring a vehicle nowadays is considered a necessity, compared to the past where it was considered a luxury. In this thriving society, more and more vehicles are produced to meet the increasing demands of people and businesses from all corners of the world. Here comes the necessity to provide more and more safety and security features to them. Hence this project aims to design an embedded system for vehicle cabin safety and security by modifying and integrating the existing modules. This monitors the level of the toxic gasses such as CO, LPG, and the temperature inside the vehicle and provides alert information in the form of an alarm during critical situations. A sonar sensor is used to detect the static obstacle in front of the vehicle and the vehicle gets stopped if any obstacle is detected within 1.5 meters. This may avoid accidents due to the collision of vehicles with any static obstacles.
Details can be found HERE