These days, many eco-systems related to federated learning, blockchain, self-driving cars, and scientific computing have many agents working together, each doing its own part. Using a single central system to check if all the agents are doing their work correctly is slow and gets more expensive as more agents are added. This paper introduces a new way called the Verification Game (VG). The agents don’t depend on a central system. The agents check each other’s work. If the agents are honest, they get rewards, so telling the truth is the best option. This type of method also saves a lot of computing power because it doesn’t check every single task. We also came up with a method called Ad... More >

Blockchain technology offers transformative potential across distributed computing domains; however, dominant consensus mechanisms such as Proof of Work (PoW) impose severe energy and carbon costs, and existing research addresses only isolated components—consensus, storage, or sharding—rather than the full architectural system. This paper introduces the Energy-Optimized Blockchain Architecture (EOBA), the first nine-layer framework in which energy awareness is a foundational, cross-cutting design principle embedded across all architectural layers. EOBA integrates energy monitoring, hybrid energy-aware consensus, energy-guided task scheduling, dynamic sharding, hybrid on-chain/off-chain s... More >

The concept of Return on Security Investment (RoSI) has evolved from a mere financial indicator into a comprehensive system for informed decision-making. Software-intensive organisations face mounting pressure to justify security expenditure in financially rigorous terms. Existing Return-on-Security-Investment (RoSI) models rely on deterministic approximations that ignore probability distributions over threats, temporal decay of vulnerability windows, and intangible cost categories. This paper presents a probabilistic RoSI framework grounded in the FAIR taxonomy that integrates: (i) expected-loss differentials with Bayesian updating; (ii) shift-left cost amplification across the software dev... More >

As cyberattacks grow more advanced and privacy laws become stricter, security systems must be powerful, transparent, and privacy-friendly. This paper introduces SwarmFL-XAI, a new framework that blends nature-inspired intelligence, collaborative learning, and explainable AI to deliver secure, scalable, and trustworthy threat detection. By using an ant-based strategy for sharing and updating models across devices, the system handles uneven data and malicious behaviour while avoiding the risks of a central server. Tools like SHAP and LIME explain why decisions are made, giving analysts clear insights and greater confidence. Tests on the UNSW-NB15 and CICIDS2017 datasets show strong results, wi... More >

The rise of multi-factor authentication (MFA) has significantly enhanced cybersecurity postures, yet its effectiveness is increasingly challenged by sophisticated social engineering attacks, particularly those exploiting MFA fatigue. MFA fatigue, a tactic where attackers inundate users with authentication prompts, aims to induce erroneous approvals, as notably exemplified by the 2022 Uber breach. This phenomenon undermines the very security MFA is designed to provide by leveraging human vulnerabilities. Game theory, a powerful mathematical framework for analyzing strategic decision-making, offers a robust methodology to model the dynamic interactions between attackers and defenders. By apply... More >