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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 (DualAC2NN) 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.

Recent years have witnessed a rapid growth of code-reuse attacks in advance persistent threats and cyberspace crimes. Carefully crafted code-reuse exploits circumvent modern protection mechanisms and hijack the execution flow of a program to perform expected functionalities by chaining together existing codes. The sophistication and intricacy of code-reuse exploits hinder the scrutinization and dissection of them. Although the previous literature has introduced some feasible approaches, effectiveness and reliability in practical applications remain severe challenges. To address this issue, we propose Horus, a data-driven framework for effective and reliable detection on code-reuse exploits. In order to raise the effectiveness against underlying noises, we comprehensively leverage the strengths of time-series and frequency-domain analysis, and propose a learning-based detector that synthesizes the contemporary twofold features. Then we employ a lightweight interpreter to speculatively and tentatively translate the suspicious bytes to open the black box and enhance the reliability and interpretability. Additionally, a functionality-preserving data augmentation is adopted to increase the diversity of limited training data and raise the generality for real-world deployment. Comparative experiments and ablation studies are conducted on a dataset composed of real-world instances to verify and prove the prevalence of Horus. The experimental results illustrate that Horus outperforms existing methods on the identification of code-reuse exploits from data stream with an acceptable overhead. Horus does not rely on any dynamic executions and can be easily integrated into existing defense systems. Moreover, Horus is able to provide tentative interpretations about attack semantics irrespective of target program, which further improve system’s effectiveness and reliability.

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