摘要
由一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。据NewsRx记者来自芬兰拉彭兰塔的新闻报道，研究表明：“特定智能城市的消费者互联网(IoT)应用程序大多配备了互联网连接的网络设备，通过提供大量有价值的信息来改善城市运营。然而，在SMA RT城市环境中，这些智能设备大多使用公共渠道在不同参与者之间访问和共享数据。”我们的新闻记者从LUT大学的研究中获得了一句话:“人们对基于人工智能(AI)技术的认证和密钥协商(AK A)机制和入侵检测系统(IDS)产生了极大的兴趣,但大多数AKA机制计算量大,通信成本高,不能完全可信。”基于ai的IDS被安全分析师视为黑盒，因为它们无法解释决定背后的原因。在这个方向上，我们集成了基于区块链的AKA机制和可解释的人工智能(XAI)，以保护基于智能城市的消费者应用程序。参与实体之间通过基于区块链的AKA机制以安全的方式相互通信以交换数据，另一方面，我们使用d SHapley加法解释(SHAP)机制来解释和解释对决策最有影响的主要特征。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Artificial In telligence have been published. According to news originating from Lappeenranta, Finland, by NewsRx correspondents, research stated, "The consumer Internet of T hings (IoT) applications in particular smart cities are mostly equipped with Int ernet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a sma rt city environment mostly use public channels to access and share data among di fferent participants." Our news journalists obtained a quote from the research from LUT University, "Th is has introduced a great interest in using authentication and key agreement (AK A) mechanisms and intrusion detection systems (IDS) based on artificial intellig ence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inabi lity to explain the reasons behind the decision. In this direction, we have inte grated blockchain-based AKA mechanism with explainable artificial intelligence ( XAI) for securing smart city-based consumer applications. Specifically, first, t he participating entities communicate with each other in a secure manner to exch ange data using a blockchain-based AKA mechanism. On the other hand, we have use d SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the pr ominent features that are most influential in the decision."
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