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 substantially lowering false positives relative to baseline models, and maintains strong recall across critical attack categories such as DDoS, brute force, and web-based exploits. Validation workshops involving industry professionals and university students confirmed that the Traffic Light visualisation system and conversational AI assistant are both intuitive and effective for users without technical backgrounds. SentinelSphere illustrates that coupling intelligent threat detection with adaptive, LLM-driven security education can meaningfully address both technical and human-factor cybersecurity vulnerabilities within a single, cohesive framework.

翻译：网络安全领域面临两大相互关联的挑战：全球范围内合格从业者的短缺，以及导致大部分安全事件的人为因素弱点。为解决这些问题，我们提出SentinelSphere——一个由人工智能驱动的平台，它将基于机器学习的威胁检测与基于大型语言模型（LLM）的安全培训统一起来。检测模块采用在CIC-IDS2017和CIC-DDoS2019基准数据集上训练的增强型深度神经网络（DNN），并通过新颖的HTTP层特征工程（捕获应用层攻击特征）进行增强。对于教育组件，我们部署了Phi-4模型（Q4_K_M）的量化变体，该变体针对网络安全领域进行了微调，可在仅需16 GB RAM且无需专用GPU资源的商用硬件上部署。实验结果表明，增强型DNN在实现高检测精度的同时，相对于基线模型大幅降低了误报率，并在DDoS、暴力破解和基于Web的攻击等关键攻击类别中保持了强大的召回率。涉及行业专业人士和大学学生的验证工作坊证实，交通灯可视化系统和对话式AI助手对于无技术背景的用户而言既直观又有效。SentinelSphere表明，将智能威胁检测与自适应的、基于LLM的安全教育相结合，能够在一个统一的框架内有意义地解决网络安全中的技术性和人为因素漏洞。