by Moa’ath Sa’ad Al-A’athal  and Qasem Abu Al-Haija

Received: 28 March 2026;  Published: 20 July 2026;  doi: 10.21926/rpse.2603015

The increasing scale, speed, and sophistication of cyberattacks have rendered traditional rule-based intrusion detection systems (IDS) insufficient for modern network environments. While machine learning (ML)-based IDSs have significantly improved detection capabilities, their black-box nature limits trust, interpretability, and practical deployment in real-world security operations. To address this challenge, this paper proposes an explainable machine learning framework for network intrusion de [...]

by Ankit Kumar Singh  , Aastha Singh  , Avanish Kant Agnihotri  , Amit Trivedi  and Mohd Nadeem

Received: 12 March 2026;  Published: 01 July 2026;  doi: 10.21926/rpse.2603014

The advent of cloud computing, the Internet of Things (IoT), and digital services has led to an increase in the number and complexity of cyberattacks. The intrusion detection methods currently used, based on machine learning and artificial intelligence, include SVM, random forest, deep learning, convolutional neural networks, and LSTM. These methods are more advanced than the signature method since they are more efficient. However, despite these advancements, several issues with intrusion detect [...]

by Kadir Aydın  and Çağrı Ün

Received: 11 May 2026;  Published: 01 July 2026;  doi: 10.21926/rpse.2603013

Thermal runaway in lithium-ion batteries (LIBs) is an escalating fire-safety problem in electric vehicles and battery energy-storage systems (BESS), and conventional water-based suppression has repeatedly been shown to delay propagation without preventing pack destruction or re-ignition. Solid, electrically non-conductive cooling agents are a promising but under-characterized alternative. This study addresses the absence of comparative, in-pack data on boron-based salt-hydrate cooling powders. E [...]