Abstract: In most of the edge computing applications including Internet of Things (IoT) devices,the application programming interface (API) based on Internet application technologies,which are commonly known as Web Technologies,is the core of information interaction between devices and remote servers.Compared with traditional web applications,most users cannot directly access APIs used by edge devices,which makes them suffer fewer attacks.However,with the popularity of edge computing,the attack based on API has gradually become a hot spot.Therefore,this paper proposes a web attack vector detection method for IoT service providers.It can be utilized to detect malicious traffic against its API services and provide security intelligence for the security operation center (SOC).Based on the feature extraction of text sequence requested by hypertext transfer protocol (HTTP),this method combines bidirectional long short-term memory (BLSTM) to detect the attack vector of web traffic according to the relatively fixed format of API request message.Experimental results show that,compared with the rule-based Web application firewall (WAF) and traditional machine learning methods,the proposed method has better recognition ability for attacks on IoT service APIs.

Key words: BLSTM, Edge computing, Internet of Things, Threat awareness, Web attack