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Abstract Attack Intention Inference Using Low-Rank Gated Arithmetic Interactive Attention

Summary:

Internal attack originated from devices in internet of things network is an essential security threat. In this paper We proposed an extended interactive attention based deep architecture that can be trained on the tabular based internet of things network traffic data to precisely identify 4 kinds of abstract attack intentions including disable target devices gathering intelligence for attacks get unauthorized access and normal traffic instead of predict the type of specific attacks. A latest dataset is used to comprehensively evaluate the performance of proposed methods. And by comparing with the current attention based methods our model shows state-of-the-art performance with overall accuracy of 93.6281\% across all proposed abstract attack intentions in external validation. To conclude we have proposed the idea of abstract attack intention inference and the proposed interactive attention framework can effectively identify the abstract attack intentions that deriving from the internal devices of Internet of Things network showcasing

Author(s):

Wanrong Yang    
University of Liverpool
United Kingdom

Manhui Wang    
University of Liverpool
United Kingdom

Dominik Wojtczak    
University of Liverpool
United Kingdom