arXiv subjects

Mohammad Jabed Morshed Chowdhury

Publications and source records attributed to Mohammad Jabed Morshed Chowdhury.

15 recordsLinked to original sources

arXiv2026

PoCQ: Defending Decentralised Federated Learning Against Model Poisoning and Collusion with Verifiable Evidence

Decentralised federated learning removes the central coordinator but requires participants to establish model update integrity autonomously. Existing frameworks either use inexpensive yet unverifiable proxies, including dataset size and epoch count, or validate updates through retraining, which is computationally costly. This paper introduces Proof of Contribution Quality (PoCQ), a framework in which trust is based on published evidence rather than on private judgement. Updates are committed before disclosure and assessed by small assigned peer committees against the updates each committee already holds. No common validation dataset is required, no validator retrains the evaluated model, and no participant requires a view of all updates in a round. Every vote is published with the values that produced it, so any peer can recompute it and contradict a dishonest validator. Accountability is graded by the strength of that evidence, with permanent removal reserved for provable misconduct and bounded suspensions for statistical anomalies, which honest nodes can produce under non-IID data. Across five model poisoning attacks on PathMNIST, PoCQ improves the average update level detection precision of the best current framework by 49% under non-IID data and by 206% under IID, and improves the precision of malicious node detection by 106% under non-IID data. This is achieved while reducing the time required to validate by half against the fastest existing framework and by 92% against retraining based validation. Colluding bad-mouthing validators are removed by recomputing their published evidence, and free riders who replay or copy an update exactly are identified directly from the ledger.

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arXiv2026

A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources. As traditional endpoint detection mechanisms focus primarily on local system behaviour, a compromised client can impact remote file servers, such as by encrypting shared data, without directly triggering behavioural changes on the servers themselves. In this paper, we propose a hybrid detection framework for detecting crypto-ransomware intrusion within integrated file server and client environments. The framework is based on a new technique referred to as Region of Interest (RoI) to analyse network traffic and extract Indicators of Compromise (IoCs). The IoC repository serves as an additional ruleset to enhance existing security tools such as EDRs and IDSs, while RoI-derived features are used to train an ML model to detect highly evasive variants. This study incorporates a broader set of ransomwares families and carefully selected benign behaviors based on domain expertise, ensuring coverage of common user actions that could interfere with ransomware detection. Beyond IoCs, which operate in a signature-based manner, our machine learning module achieves a detection precision of 99.64%, with a 0% false negative rate (FNR) and a minimal false positive rate (FPR). Furthermore, the proposed method enables early detection, identifying ransomware intrusions before significant damage occurs, achieving an accuracy of 99.44%.

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arXiv2024

Quantum Threat in Healthcare IoT: Challenges and Mitigation Strategies

The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease management. However, this interconnected ecosystem faces significant vulnerabilities with the advent of quantum computing, which threatens to break existing encryption standards protecting sensitive patient data in IoT-enabled medical devices. This chapter examines the quantum threat to healthcare IoT security, highlighting the potential impacts of compromised encryption, including privacy breaches, device failures, and manipulated medical records. It introduces post-quantum cryptography (PQC) and quantum-resistant techniques like quantum key distribution (QKD), addressing their application in resource-constrained healthcare IoT devices such as pacemakers, monitoring tools, and telemedicine systems. The chapter further explores the challenges of integrating these solutions and reviews global efforts in mitigating quantum risks, offering insights into suitable PQC primitives for various healthcare use cases.

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arXiv2024

A Systematic Literature Review on the Use of Blockchain Technology in Transition to a Circular Economy

The circular economy has the potential to increase resource efficiency and minimize waste through the 4R framework of reducing, reusing, recycling, and recovering. Blockchain technology is currently considered a valuable aid in the transition to a circular economy. Its decentralized and tamper-resistant nature enables the construction of transparent and secure supply chain management systems, thereby improving product accountability and traceability. However, the full potential of blockchain technology in circular economy models will not be realized until a number of concerns, including scalability, interoperability, data protection, and regulatory and legal issues, are addressed. More research and stakeholder participation are required to overcome these limitations and achieve the benefits of blockchain technology in promoting a circular economy. This article presents a systematic literature review (SLR) that identified industry use cases for blockchain-driven circular economy models and offered architectures to minimize resource consumption, prices, and inefficiencies while encouraging the reuse, recycling, and recovery of end-of-life products. Three main outcomes emerged from our review of 41 documents, which included scholarly publications, Twitter-linked information, and Google results. The relationship between blockchain and the 4R framework for circular economy; discussion the terminology and various forms of blockchain and circular economy; and identification of the challenges and obstacles that blockchain technology may face in enabling a circular economy. This research shows how blockchain technology can help with the transition to a circular economy. Yet, it emphasizes the importance of additional study and stakeholder participation to overcome potential hurdles and obstacles in implementing blockchain-driven circular economy models.

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arXiv2024

EarlyMalDetect: A Novel Approach for Early Windows Malware Detection Based on Sequences of API Calls

In this work, we propose EarlyMalDetect, a novel approach for early Windows malware detection based on sequences of API calls. Our approach leverages generative transformer models and attention-guided deep recurrent neural networks to accurately identify and detect patterns of malicious behaviors in the early stage of malware execution. By analyzing the sequences of API calls invoked during execution, the proposed approach can classify executable files (programs) as malware or benign by predicting their behaviors based on a few shots (initial API calls) invoked during execution. EarlyMalDetect can predict and reveal what a malware program is going to perform on the target system before it occurs, which can help to stop it before executing its malicious payload and infecting the system. Specifically, EarlyMalDetect relies on a fine-tuned transformer model based on API calls which has the potential to predict the next API call functions to be used by a malware or benign executable program. Our extensive experimental evaluations show that the proposed approach is highly effective in predicting malware behaviors and can be used as a preventive measure against zero-day threats in Windows systems.

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arXiv2024

Blockchain-enabled Circular Economy -- Collaborative Responsibility in Solar Panel Recycling

The adoption of renewable energy resources, such as solar power, is on the rise. However, the excessive installation and lack of recycling facilities pose environmental risks. This paper suggests a circular economy approach to address the issue. By implementing blockchain technology, the end-of-life (EOL) of solar panels can be tracked, and responsibilities can be assigned to relevant stakeholders. The degradation of panels can be monetized by tracking users' energy-related activities, and these funds can be used for future recycling. A new coin, the recycling coin (RC-Coin), incentivizes solar panel recycling and utilizes decentralized finance to stabilize the coin price and supply issue.

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arXiv2024

Deep Learning Models for Detecting Malware Attacks

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Deep learning (DL) is one of the emerging and promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on the Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are presented. We also present feature extraction approaches and a review of recent DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on malware detection including future directions to further advance knowledge and research in this field.

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arXiv2023

Managing health insurance using blockchain technology

Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.

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arXiv2022

MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques

The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been significantly enhanced and as a result, traditional malware detection techniques have become less effective. This work presents MalBehavD-V1, a new behavioural dataset of Windows Application Programming Interface (API) calls extracted from benign and malware executable files using the dynamic analysis approach. In addition, we present MalDetConV, a new automated behaviour-based framework for detecting both existing and zero-day malware attacks. MalDetConv uses a text processing-based encoder to transform features of API calls into a suitable format supported by deep learning models. It then uses a hybrid of convolutional neural network (CNN) and bidirectional gated recurrent unit (CNN-BiGRU) automatic feature extractor to select high-level features of the API Calls which are then fed to a fully connected neural network module for malware classification. MalDetConv also uses an explainable component that reveals features that contributed to the final classification outcome, helping the decision-making process for security analysts. The performance of the proposed framework is evaluated using our MalBehavD-V1 dataset and other benchmark datasets. The detection results demonstrate the effectiveness of MalDetConv over the state-of-the-art techniques with detection accuracy of 96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware from MalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. The experimental results show that MalDetConv is highly accurate in detecting both known and zero-day malware attacks on Windows devices.

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arXiv2022

BlockMeter: An Application Agnostic Performance Measurement Framework For Private Blockchain Platforms

Blockchain Technology is an emerging technology with the potential to disrupt a number of application domains. Though blockchain platforms like Bitcoin and Ethereum have seen immense success and acceptability, their nature of being public and anonymous make them unsuitable for many enterprise level use-cases. To address this issue, Linux Foundation has started an open source umbrella initiative, known as the Hyperledger Platforms. Under this initiative, a number of private blockchain platforms have been developed which can be used for different enterprise level applications. However, the scalability and performance of these private blockchains must be examined to understand their suitability for different use-cases. Recent researches and projects on performance benchmarking for private blockchain systems are very specific to use-cases and are generally tied to a blockchain platform. In this article, we presentBlockMeter, an application agnostic performance benchmarking framework for private blockchain platforms. This framework can be utilised to measure the key performance matrices of any application deployed on top of an external private blockchain application in real-time. In this article, we present the architecture of the framework and discuss its different implementation aspects. Then, to showcase the applicability of the framework, we use BlockMeter to evaluate the two most widely used Hyperledger platforms, Hyperledger Fabric and HyperledgerSawtooth, against a number of use-cases.

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arXiv2021

Blockchain-based Covid Vaccination Registration and Monitoring

Covid-19 (SARS-CoV-2) has changed almost all the aspects of our living. Governments around the world have imposed lockdown to slow down the transmissions. In the meantime, researchers worked hard to find the vaccine. Fortunately, we have found the vaccine, in fact a good number of them. However, managing the testing and vaccination process of the total population is a mammoth job. There are multiple government and private sector organisations that are working together to ensure proper testing and vaccination. However, there is always delay or data silo problems in multi-organisational works. Therefore, streamlining this process is vital to improve the efficiency and save more lives. It is already proved that technology has a significant impact on the health sector, including blockchain. Blockchain provides a distributed system along with greater privacy, transparency and authenticity. In this article, we have presented a blockchain-based system that seamlessly integrates testing and vaccination system, allowing the system to be transparent. The instant verification of any tamper-proof result and a transparent and efficient vaccination system have been exhibited and implemented in the research. We have also implemented the system as "Digital Vaccine Passport" (DVP) and analysed its performance.

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arXiv2021

A Systematic Literature Review on Wearable Health Data Publishing under Differential Privacy

Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical researchers and clinicians that cannot be availed through traditional measures. Researchers have historically relied on survey responses or observed behavior. Interestingly, physiological data can provide a richer amount of user cognition than that obtained from any other sources, including the user himself. Therefore, the inexpensive consumer-grade wearable devices have become a point of interest for the health researchers. In addition, they are also used in continuous remote health monitoring and sometimes by the insurance companies. However, the biggest concern for such kind of use cases is the privacy of the individuals. There are a few privacy mechanisms, such as abstraction and k-anonymity, are widely used in information systems. Recently, Differential Privacy (DP) has emerged as a proficient technique to publish privacy sensitive data, including data from wearable devices. In this paper, we have conducted a Systematic Literature Review (SLR) to identify, select and critically appraise researches in DP as well as to understand different techniques and exiting use of DP in wearable data publishing. Based on our study we have identified the limitations of proposed solutions and provided future directions.

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arXiv2020

Blockchain Consensus Algorithms: A Survey

In recent years, blockchain technology has received unparalleled attention from academia, industry, and governments all around the world. It is considered a technological breakthrough anticipated to disrupt several application domains. This has resulted in a plethora of blockchain systems for various purposes. However, many of these blockchain systems suffer from serious shortcomings related to their performance and security, which need to be addressed before any wide-scale adoption can be achieved. A crucial component of any blockchain system is its underlying consensus algorithm, which in many ways, determines its performance and security. Therefore, to address the limitations of different blockchain systems, several existing as well novel consensus algorithms have been introduced. A systematic analysis of these algorithms will help to understand how and why any particular blockchain performs the way it functions. However, the existing studies of consensus algorithms are not comprehensive. Those studies have incomplete discussions on the properties of the algorithms and fail to analyse several major blockchain consensus algorithms in terms of their scopes. This article fills this gap by analysing a wide range of consensus algorithms using a comprehensive taxonomy of properties and by examining the implications of different issues still prevalent in consensus algorithms in detail. The result of the analysis is presented in tabular formats, which provides a visual illustration of these algorithms in a meaningful way. We have also analysed more than hundred top crypto-currencies belonging to different categories of consensus algorithms to understand their properties and to implicate different trends in these crypto-currencies. Finally, we have presented a decision tree of algorithms to be used as a tool to test the suitability of consensus algorithms under different criteria.

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arXiv2018

Immutable Autobiography of Smart Cars

The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences.These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are sensitive. Besides, there are multiple parties involved in a lifespan of a smart car ,such as manufacturers, car owners, government agencies, and third-party service providers who also produce data about the vehicle. In addition to managing and sharing data amongst these entities in a secure and privacy-friendly way which is a great challenge itself, there exists a trust deficit about some types of data as they remain under the custody of the car owner(e.g. satellite navigation and mileage data) and can easily be manipulated. In this paper, we propose a blockchain supported architecture enabling the owner of a smart car to create an immutable record of every data, called the auto biography of a car, generated within its lifespan. We also explain how the trust about this record is guaranteed by the immutability characteristic of the blockchain. Furthermore, the paper describes how the proposed architecture enables a secure and privacy-friendly sharing of smart car data between different parties in a secure yet privacy-friendly manner.

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arXiv2013

CAPTCHA Based on Human Cognitive Factor

A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an automatic security mechanism used to determine whether the user is a human or a malicious computer program. It is a program that generates and grades tests that are human solvable, but intends to be beyond the capabilities of current computer programs. CAPTCHA should be designed to be very easy for humans but very hard for machines. Unfortunately, the existing CAPTCHA systems while trying to maximize the difficulty for automated programs to pass tests by increasing distortion or noise have consequently, made it also very difficult for potential users. To address the issue, this paper addresses an alternative form of CAPTCHA that provides a variety of questions from mathematical, logical and general problems which only human can understand and answer correctly in a given time. The proposed framework supports diversity in choosing the questions to be answered and a user-friendly framework to the users. A user-study is also conducted to judge the performance of the developed system with different background. The study shows the efficacy of the implemented system with a good level of user satisfaction over traditional CAPTCHA available today.

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