arXiv subjects

Haleh Amintoosi

Publications and source records attributed to Haleh Amintoosi.

7 recordsLinked to original sources

arXiv2026

ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning

This paper addresses the critical challenge of access control in modern supply chains, which operate across multiple independent and competing organizations. Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts. Blockchain improves decentralization but lacks behavioral intelligence, while centralized machine learning for anomaly detection requires aggregating sensitive data, violating privacy. The proposed solution is ICBAC, an intelligent contract-based access control framework. It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL). Built on Fabric, ICBAC uses a multi-channel architecture and three smart contracts for asset management, baseline access control, and dynamic revocation. To counter insider misuse, each channel deploys an AI agent that monitors activity and dynamically restricts access for anomalies. Federated learning allows these agents to collaboratively improve detection models without sharing raw data. For heterogeneous, competitive environments, ICBAC introduces a game-theoretic client selection mechanism using hedonic coalition formation. This enables supply chains to form stable, strategy-proof FL coalitions via preference-based selection without disclosing sensitive criteria. Extensive experiments on a Fabric testbed with a real-world dataset show ICBAC achieves blockchain performance comparable to static frameworks and provides effective anomaly detection under IID and non-IID data with zero raw-data sharing. ICBAC thus offers a practical, scalable solution for dynamic, privacy-preserving access control in decentralized supply chains.

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arXiv2025

Attack-Centric by Design: A Program-Structure Taxonomy of Smart Contract Vulnerabilities

Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families covering control flow, external calls, state integrity, arithmetic safety, environmental dependencies, access control, input validation, and cross-domain protocol assumptions. Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools. We further cross-map legacy datasets (SmartBugs, SolidiFI) to this taxonomy to reveal label drift and coverage gaps. The taxonomy provides a consistent vocabulary and practical checklist that enable more interpretable detection, reproducible audits, and structured security education for both researchers and practitioners.

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arXiv2025

A Blockchain-Based Quality Control Model for Online Collaboration Systems

Collaborative content generation (CCG) enables collective creation of artifacts like scientific articles. Quality is a paramount concern in CCG, and a multitude of methods have been proposed to evaluate the quality of artifacts. Nevertheless, the majority of these methods are reliant on centralized architectures, which present challenges pertaining to security, privacy, and availability. Blockchain technology proffers a potential resolution to these challenges, by furnishing a decentralized and immutable ledger of quality scores. In this manuscript, we introduce a blockchain-based quality control model for CCG that uses a semi-iterative algorithm to interdependently compute quality scores of artifacts and reputation of nodes. Our model addresses critical challenges in academic informetrics, such as citation manipulation, transparency in collaborative scholarship, and decentralized trust in metric computation. Our model also exhibits sensitivity to processing latency, rendering it more agile in the presence of delays. Our model's quality scores, evaluated against PageRank and HITS baselines, show comparable performance, with additional assessments of throughput, latency, and robustness against malicious nodes confirming its reliability. A theoretical comparison with recent studies validates its feasibility for real world informetric application.

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arXiv2023

Machine Learning in Digital Forensics: A Systematic Literature Review

Development and exploitation of technology have led to the further expansion and complexity of digital crimes. On the other hand, the growing volume of data and, subsequently, evidence is a severe challenge in digital forensics. In recent years, the application of machine learning techniques to identify and analyze evidence has been on the rise in different digital forensics domains. This paper offers a systematic literature review of the research published in major academic databases from January 2010 to December 2021 on the application of machine learning in digital forensics, which was not presented yet to the best of our knowledge as comprehensive as this. The review also identifies the domains of digital forensics and machine learning methods that have received the most attention in the previous papers and finally introduces remaining research gaps. Our findings demonstrate that image forensics has obtained the greatest benefit from using machine learning methods, compared to other forensic domains. Moreover, CNN-based models are the most important machine learning methods that are increasingly being used in digital forensics. We present a comprehensive mind map to provide a proper perspective for valuable analytical results. Furthermore, visual analysis has been conducted based on the keywords of the papers, providing different thematic relevance topics. This research will give digital forensics investigators, machine learning developers, security researchers, and enthusiasts a broad view of the application of machine learning in digital forensics.

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arXiv2019

Cryptanalysis of Khatoon et al.'s ECC-based Authentication Protocol for Healthcare Systems

Telecare medical information systems are gaining rapid popularity in terms of providing the delivery of online health-related services such as online remote health profile access for patients and doctors. Due to being installed entirely on Internet, these systems are exposed to various security and privacy threats. Hence, establishing a secure key agreement and authentication process between the patients and the medical servers is an important challenge. Recently, Khatoon et.al proposed an ECC-based unlink-able authentication and key agreement method for healthcare related application in smart city. In this article, we provide a descriptive analysis on their proposed scheme and prove that Khatoon et al.'s scheme is vulnerable to known-session-specific temporary information attack and is not able to provide perfect forward secrecy.

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arXiv2013

Providing Trustworthy Contributions via a Reputation Framework in Social Participatory Sensing Systems

Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A reputation score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate reputation scores.

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arXiv2013

A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.

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