| Summary: | The exponential expansion of Internet interconnectivity has led to a dramatic increase in cyber-attack alerts, which contain a considerable proportion of false positives. The overwhelming number of false positives cause tremendous resource consumption and delay responses to the really severe incidents, namely, alert fatigue. To cope with the challenge from alert fatigue, we focus on enhancing the capability of detectors to reduce the generation of false alerts from the detection perspective. The core idea of our work is to train a machine-learning-based detector to grasp the empirical intelligence of security analysts to estimate the feasibility of an incoming HTTP request to cause substantial threats, and integrate the estimation into the detection stage to reduce false alarms. To this end, we innovatively introduce the concept of attack feasibility to characterize the composition rationality of an inbound HTTP request as a feasible attack under static scrutinization. First, we adopt a fast request-reorganization algorithm to transform an HTTP request into the form of interface:payload pair for further alignment of structural components which can reveal the processing logic of the target program. Then, we build a dual-channel attention-based circulant convolution neural network (DualAC<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>NN) to integrate the attack feasibility estimation into the alert decision, by comprehensively considering the interface sensitivity, payload maliciousness, and their bipartite compatibility. Experiments on a real-world dataset show that the proposed method significantly reduces invalid alerts by around 86.37% and over 61.64% compared to a rule-based commercial WAF and several state-of-the-art methods, along with retaining a detection rate at 97.89% and a lower time overhead, which indicates that our approach can effectively mitigate alert fatigue from the detection perspective.
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