Dr. Mahdi Daghmehchi Firoozjaei
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Academic Career Length
8Years (2019-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 March 2022 in International Journal of Critical Infrastructure Protection
Journal Article
Mahdi Daghmehchi FiroozjaeiNMNastaran Mahmoudyar
Nastaran Mahmoudyar
YBYaser Baseri
Yaser Baseri
AGAli A. Ghorbani
Ali A. Ghorbani
5 October 2020 in Security and Privacy
Journal Article
Mahdi Daghmehchi FiroozjaeiRLRongxing Lu
Rongxing Lu
AGAli Ghorbani
Ali GhorbaniBlockchain‐based applications provide many promising opportunities to overcome the challenges associated with the Internet of Things (IoT) ecosystems (eg, centralized architecture, data integrity, and reliability). In particular, blockchain technology offers many desirable features for IoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. However, while logging all transactions in a distributed blockchain ledger provides transparency, it also makes it possible to compromise user's privacy, thus posing a grand challenge to IoT architects and implementers. Over the past years, a set of solutions have been proposed for various scenarios, to address these privacy issues. In this paper, we survey these solutions, classify, and analyze their advantages and disadvantages. We also introduce an evaluation framework to evaluate the quality of the privacy‐preserving based on an adjustable weighting scheme. Finally, we rate the analyzed solutions based on their privacy ranks, and hope our evaluation can shed light on the future design of privacy‐preserving solutions applicable for blockchain‐based IoT platforms.
3 July 2022 in Journal of Cyber Security Technology
Journal Article
Mahdi Daghmehchi Firoozjaei
Arash Habibi LashkariAGAli Ghorbani
Ali GhorbaniAs part of the incident response process, the memory forensics tools extract forensic artifacts and display them. Many memory forensics analysis tools are being developed to address the challenges of modern cybercrimes. Investigations are successful when they have an accurate analysis provided by a memory forensics tool that consumes resources reasonably. This paper presents a comparative analysis of three dominant memory forensics tools: Volatility, Autopsy, and Redline. We consider three malware behaviour scenarios and evaluate the forensics capabilities of these tools in each. We also experimentally measure the CPU and memory consumption of each for memory analysis in other operational states. We find that Volatility provides the most accurate memory analysis. Our measurements show that Redline consumes more CPU resources, and Autopsy needs more memory resources to analyze a memory image file. The results of our investigation will hopefully lead to tool improvement and more informed choices by users.
1 August 2019
Conference Paper
Mahdi Daghmehchi FiroozjaeiAGAli Ghorbani
Ali Ghorbani
Hyoungshick KimJSJaeSeung Song
JaeSeung SongThe Digital economy is based on confidence in its trustworthiness. Blockchain distributed consensus provides a reliable and trustful network for financial and non-financial transactions. Blockchain-based electric vehicles (EVs) charging applications benefit blockchain features to provide automated and verifiable services for EV charging market. Requirements for feasible charging operation and privacy concerns are challenging issues with blockchain-based EV charging approaches. To provide a feasible charging ability and preserve EV owner's privacy, we introduce EVChain. The EVChain is a trustful and decentralized platform based on blockchain technology to share charging credits in the EV charging market. To share credits, the main blockchain in EVChain is connected to one or more subnetwork blockchains. We introduce an interconnection position to preserve EV owners' privacy with k-anonymity protection. We simulate and evaluate the privacy protection it provides, based on an example EV charging scenario.
Journal Article
MKMinChang Kim
MinChang KimMahdi Daghmehchi FiroozjaeiHyoungshick KimMEMohamad El-Hajj
Mohamad El-HajjPower consumption data play a crucial role in demand management and abnormality detection in smart grids. Despite its management benefits, analyzing power consumption data leads to profiling consumers and opens privacy issues. To demonstrate this, we present a power profiling model for smart grid consumers based on real-time load data acquired from smart meters. It profiles consumers’ power consumption behavior by applying the daily load factor and the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features can be extracted. By this model, two load types are defined and the related load patterns are extracted for classifying consumption behavior by DTW. The classification methodology is discussed in detail. To evaluate the performance of the proposed model for profiling, we analyze the time-series load data measured by a smart meter in a real case. The results demonstrate the effectiveness of the proposed profiling method, achieving an F-score of for load type clustering in the best case and an overall accuracy of for power profiling.
27 June 2024 in Journal of Information Security and Applications
Journal Article
YBYaser Baseri
Yaser BaseriAHAbdelhakim Hafid
Abdelhakim Hafid
Mahdi Daghmehchi FiroozjaeiSCSoumaya Cherkaoui
Soumaya CherkaouiIRIndrakshi Ray
Indrakshi Ray
About Me
Mahdi Daghmehchi Firoozjaei is an Assistant Professor in the Department of Computer Science at MacEwan University, Canada. Previously, he served as an Assistant Professor at the University of Windsor and as a Postdoctoral Research Fellow at the Canadian Institute for Cybersecurity (CIC). He earned a Ph.D. in Computer Engineering from Sungkyunkwan University (SKKU), Korea, and has over a decade of industry experience in telecom and cybersecurity. His experience includes leading R&D projects in OT forensics, IoT blockchain, and DNS firewall development at CIC, Canada, as well as working as a senior engineer in telecommunication systems at the Telecommunication Company of Iran (TCI). Dr. Firoozjaei’s research interests primarily focus on cybersecurity, covering areas such as malware analysis, digital forensics, network security, privacy-preserving techniques, and blockchain. His contributions to research have been widely recognized, earning him the Best Researcher Award 2023 from SFConferences and the Best Paper Award (AINA-2015) at the AINA Conference. Additionally, he has received prestigious accolades, including the SKKU Superior Research Award 2017 (runner-up) and the SKKU Scholarship for Outstanding International Students during his Ph.D. studies.