VOLUME 15, ISSUE 6, JUNE 2026

Abstract: Considering the increasing popularity of MAAI solutions for providing a promising alternative to automating the process of security operations in complex network environments. The paper presents a scalable multi- agent solution that involves autonomous intelligent agents working together to discover threats, recognize security gaps, take decisions, and respond to security attacks. Unlike other solutions, which involve conventional and centralized security frameworks [9–13], our multi-agent-based approach makes use of several agents, providing more flexibility due to the possibility of running tasks in parallel, adapting quickly by learning agents from experience, and making the whole system less vulnerable to advanced cyberattacks. Our multi-agent system uses agents with unique features and works using the communication layer, in which agents exchange information about detected threats and optimize their actions to achieve the best results in always-on protection. The experimental prototype is tested for its efficiency in detecting DDoS attack, phishing or malware intrusions. The research results revealed high levels of efficiency demonstrated through high detection rates, low response latency, and fewer false positives. As far as MAAI systems go, this work proves a considerable potential for automation and scalability.

Keywords: Multi-Agent Systems; Cybersecurity Automation; Artificial Intelligence; Intrusion Detection System (IDS); Distributed Security; Autonomous Agents; Threat Detection; Security Orchestration Network Security Intelligent Systems.

Abstract: The ever-shifting landscape of cyber threats is always on the move, with APTs, insider attacks, and zero- day attacks at the forefront. Conventional, rule-based SIEMs—until now, the workhorse of many security operations— demonstrate their limitations in the face of such threats. They can wade through massive amounts of security data, but they tend to vomit out lots of false positives and lack the ability to predict what’s around the corner. This research investigates how SIEMs might do more than simply respond to threats: it examines the use of AI and other forms of advanced analytics to predict future intrusions. To improve the signal-to-noise ratio, add context, and accelerate response times, the proposed solution relies on behavioral analytics, anomaly detection, machine learning, and automated threat intelligence enrichment. The research describes an analytics workflow, an AI-based SIEM solution, and a methodology for comparing these AI-infused systems to traditional systems. The findings indicate that as AI continues to evolve, AI-based SIEM solutions enable organizations to concentrate on threats that matter, minimize the need for continuous human interaction, and identify complex or unexpected attacks earlier.

Keywords: Intrusion Detection Systems (IDS), anomaly detection, alert prioritization, cyber threat intelligence, security information and event management (SIEM), predictive threat hunting, behavioral analytics, data analytics, and machine learning.

Abstract: Many Internet of Things (IoT)-based flood prediction systems deployed in developing regions fail to deliver reliable early warnings due to unreliable sensors, fragmented datasets, and limited operational resilience. While numerous frameworks have been proposed, few studies systematically evaluate their design limitations or validate enhanced solutions under realistic failure conditions. This paper presents the design evaluation, enhancement, and validation of a resilient IoT-based flood prediction framework. Using Design Science Research principles, existing IoT flood prediction frameworks were evaluated using ITIL-aligned governance criteria to identify deficiencies in data reliability, service continuity, and system governance. An enhanced framework was then designed and validated through simulation using CHIRPS rainfall data and controlled sensor failure scenarios. Simulation results indicate that the enhanced framework maintains prediction accuracy between 82.4% and 91.6% under increasing data-loss conditions and improves alert timeliness compared to baseline approaches. The findings indicate that resilience-oriented, data-centric IoT design significantly improves flood prediction performance in resource-constrained environments.

Keywords: Flood prediction; Internet of Things; design science research; data reliability; sensor unreliability; early warning systems.