基于自适应巡视算法的工业物联网异常行为检测

Abnormal Behavior Detection of Industrial Internet of Things Based on Adaptive Cruise Algorithm

针对当前物联网节点易受攻击的问题,提出一种基于自适应巡视算法的工业物联网异常行为检测方法。根据数据包完整性、数据包传输率和传输延迟等异常行为动态更新物联网节点信誉度,结合信誉阈值自适应调整巡视间隔。该方法能够实现低能耗、高效率的工业物联网安全防护,提升异常行为检测的自适应性。实验证明,该方法与传统LEACH-C机制相比,能更好降低网络能耗、延长节点生存时间,因此该方法具有一定的可用性。

Aiming at the security and vulnerability of current Internet of things(IoT)nodes,this paper provides an abnormal behavior detection research based on adaptive cruise algorithm.According to abnormal behaviors such as packet integrity,packet transmission rate and transmission delay,the reputation of IOT nodes is dynamically updated,and the cruise interval is adaptively adjusted in combination with the reputation threshold.The method can achieve the security protection of industrial IoT with low energy consumption and high efficiency,and improve the adaptability and efficiency of abnormal behavior detection.Experiments show that the proposed method has better performance than the traditional LEACH-C mechanism in terms of reducing network energy consumption and prolonging node lifetime.Therefore,the proposed method has certain usability.