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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Qatawneh, Mohammad
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Electrical & Electronics Engineering

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Linear algorithm for data retrieval performance optimization in self-encryption hybrid data centers M. Al Assaf, Maen; Qatawneh, Mohammad; AlRadhi, AlaaAldin
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9320

Abstract

Contemporary data centers implement hybrid storage systems that consist of layers from solid-state drives (SSDs) and hard disk drives (HDDs). Due to their high data retrieval speed, SSDs layer is used to store important data blocks that have features like high frequency of access. To boost their security level, many of such systems implement self-encryption algorithms like advanced encryption standard (AES), Blowfish, and triple data encryption standard (3DES) with different key sizes that vary in their complexity and their decryption latency whenever a block is requested for read. Frequently accessed data blocks with increased decryption latencies are better to be migrated to the SSDs layer to decrease their retrieval latency. In this paper, we introduce a linear complexity algorithm hybrid self-encryption storage data migration (HSESM) that migrates important data blocks that requires long decryption latencies from the HDDs layer to the SSDs one. Performance evaluation shows that HSESM data migration process can reduce data blocks read latencies in 13.71%-23.61% under worst-case scenarios.
Blockchain for future smart grid: a comprehensive survey Shamaseen, Ala’a; Qatawneh, Mohammad; Elshqeirat, Basima
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8956

Abstract

Due to the unique features and characteristics of blockchain technology, its applications have expanded across various sectors, including finance, banking, supply chains, and smart grids (SGs). Blockchain ensures security and trust in transactions without requiring a third party, making it particularly valuable in decentralized systems. This paper explores the integration of blockchain technology into SG systems. It begins with a comprehensive review of conventional and smart power grids, identifying the key challenges modern SGs face, particularly issues related to trust and fraud. An in-depth analysis of blockchain technology follows, highlighting its potential, advantages, and defining characteristics. The study then examines several blockchain-based SG applications and provides a comparative analysis of prior research. The findings of this review illuminate the critical role of blockchain in enhancing SG performance by addressing trust and fraud prevention challenges. Furthermore, this research has significant implications for the energy sector, as it underscores the potential of blockchain to revolutionize SGs through increased security, transparency, and efficiency. By providing a foundation for future studies, this paper aims to guide the development of unified blockchain frameworks that address scalability, privacy, and energy management, paving the way for a more secure and efficient decentralized energy system
A framework for security risk assessment of blockchain-based applications Qatawneh, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp952-962

Abstract

Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.
Challenges in applying DeepInsight for cyber threat detection AL-Essa, Malik; Qatawneh, Mohammad; Turab, Nidal; Alsarhan, Yazeed
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9649

Abstract

As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.
Co-Authors AL-Essa, Malik AlRadhi, AlaaAldin Alsarhan, Yazeed Elshqeirat, Basima M. Al Assaf, Maen Shamaseen, Ala’a Turab, Nidal