Forthcoming Articles

International Journal of High Performance Systems Architecture

International Journal of High Performance Systems Architecture (IJHPSA)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of High Performance Systems Architecture (3 papers in press)

Regular Issues

  • Evaluation of Supervised Machine Learning Methods in Detection of Phishing Threats   Order a copy of this article
    by Ivan Cviti?, Dragan Perakovi? 
    Abstract: This paper describes the development of seven machine learning models using the publicly available ISCX-URL2016 dataset. The models were designed to per-form multiple classifications, and their performance was evaluated using J48, Random Forest, Random Tree, Lazy IBk, BayesNet, and Naive Bayes algorithms. The dataset underwent preprocessing, resulting in 77 attributes, and the number of attributes for each model was determined using the InfoGain method. The results indicate that malicious website URLs can be classified into five predefined classes based on their features with high accuracy. The Random Forest, J48, Random Tree, and Lazy IBk algorithms achieved the highest accuracy rates, ranging from 94.52% to 98.43%. The Random Forest algorithm was further evaluated using machine learning metrics such as sensitivity, specificity, precision, recall, and f-measure, which demonstrated its effectiveness.
    Keywords: Cybersecurity; Data preprocessing; Decision tree; Malware detection; Feature se-lection.
    DOI: 10.1504/IJHPSA.2025.10075046
     
  • Application of Deep Learning in Internet of Things Intrusion Detection   Order a copy of this article
    by Yanzhen Wang, DengBiao Zhu 
    Abstract: Internet of Things (IoT) intrusion detection faces challenges due to complex, high-dimensional, and redundant data, making it difficult to capture deep features and respond quickly. To address this, a deep learning-based method is proposed. IoT data is segmented using a sliding window, and principal component analysis (PCA) reduces dimensionality by extracting key features. The processed data is then fed into a deep belief network (DBN), which utilises stacked restricted Boltzmann machines (RBMs) to learn high-level representations for identifying complex intrusions like DDoS, phishing, and DoS attacks. To enhance performance, a bacterial colony optimisation algorithm adaptively optimises the DBNs hidden layer weights and biases. Experiments show that a DBN with five hidden layers and eight units per layer achieves optimal detection, with delays under 5 ms, intrusion identification accuracy over 98%, and system stability above 95%. While performance may fluctuate with extremely high-dimensional dynamic streams and applicability in other IoT domains requires further validation, this method demonstrates strong potential for improving IoT security and reliability.
    Keywords: Deep Learning; Internet of Things (IoT); Intrusion Detection; Feature Extraction; Principal Component Analysis; Deep Belief Network; Bacterial Colony Optimization Algorithm; Parameter Optimization.
    DOI: 10.1504/IJHPSA.2025.10076203
     
  • Performance Optimised Architectures of TinyJambu Cipher for Low Resource IoT Applications   Order a copy of this article
    by Bibhudendra Acharya, Bijayananda Patnaik, Pulkit Singh 
    Abstract: Ensuring data security and integrity is vital for achieving optimal protection and performance in modern cyber-physical systems (CPS). Authenticated encryption with associated data (AEAD) provides an efficient and secure method for data encryption, guaranteeing both confidentiality and authenticity. TinyJambu cipher is a lightweight authenticated encryption algorithm tailored for resource-constrained environments such as IoT devices, RFID tags, and embedded systems. This paper presents optimized architectures of TinyJambu cipher, focusing on round-based and area-optimized implementations to enhance performance and efficiency on various FPGA platforms. These designs achieve a balance between high throughput and minimal resource usage. Comparative analysis with other AEAD ciphers highlights the superior efficiency of TinyJambu cipher and performance, confirming its suitability for modern lightweight cryptographic applications.
    Keywords: IoT(Internet of things); RFID(Radio Frequency Identification); TinyJambu; FPGA(Field Programmable Gate Array); Cryptography; Pipelining.
    DOI: 10.1504/IJHPSA.2025.10076298
     

 

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