Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative more active and more effective security methods keeps increasing. Such approaches are the adoption of intelligent solutions to prevent, detect and deal with threats or anomalies under the conditions and the operating parameters of the infrastructure in question. This research paper introduces the development of an intelligent Threat Defense system, employing Blockchain Federated Learning, which seeks to fully upgrade the way passive intelligent systems operate, aiming at implementing an Advanced Adaptive Cooperative Learning (AACL) mechanism for smart cities networks. The AACL is based on the most advanced methods of computational intelligence while ensuring privacy and anonymity for participants and stakeholders. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. Learning is achieved through encrypted smart contracts within the blockchain technology, for unambiguous validation and control of the process. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods, in order to identify anomalies that are usually due to Advanced Persistent Threat (APT) attacks.
翻译:鉴于智能城市威胁日益复杂,环境不断变化,传统安全系统薄弱,在大多数情况下无法发现诸如零日袭击等严重威胁,因此,需要采用更积极、更有效的替代安全方法,这些办法是采取智能解决办法,在相关基础设施的条件和运作参数下,预防、发现和应对威胁或异常现象,采用智能威胁防御系统,利用链链联学习,力求充分提升被动智能系统的运作方式,目的是为智能城市网络实施先进的适应合作学习机制(AACL),该工具基于最先进的计算情报方法,同时确保参与者和利益攸关方的隐私和匿名性,拟议框架将联邦学习结合起来,以传播和持续验证追踪算法的学习,通过链链技术内加密智能合同实现学习,对过程进行明确的验证和控制,拟议框架的目的是对通过深层内容检查(DCI)对来自工业IOT(IIoT)的智能城市网络流量进行明智的分类,目的是查明通常属于高级威胁的系统异常现象。