[2604.06900] SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training

Computer Science > Computational Engineering, Finance, and Science arXiv:2604.06900 (cs) [Submitted on 8 Apr 2026] Title:SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training Authors:Nikolaos D. Tantaroudas, Ilias Karachalios, Andrew J. McCracken View a PDF of the paper titled SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training, by Nikolaos D. Tantaroudas and 2 other authors View PDF HTML (experimental) Abstract:The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor weaknesses that account for the bulk of security incidents. To tackle these issues, we present SentinelSphere, a platform driven by artificial intelligence that unifies machine learning-based threat identification with security training powered by a Large Language Model (LLM). The detection module uses an Enhanced Deep Neural Network (DNN) trained on the CIC-IDS2017 and CIC-DDoS2019 benchmark datasets, enriched with novel HTTP-layer feature engineering that captures application level attack signatures. For the educational component, we deploy a quantised variant of Phi-4 model (Q4_K_M), fine-tuned for the cybersecurity domain, enabling deployment on commodity hardware requiring only 16 GB of RAM without dedicated GPU resources. Experimental results show that the Enhanced DNN attains high detection accuracy while substant...