Abstract
In more and more popular computer systems, industry protects the network on top of them via scanning malware (malicious software or applications) through some generic properties. It is useful but not accurate enough – even 0.01% of accuracy gain can cause millions of malicious software or applications over internet to steal privacy or break down computer systems. To address this problem, the paper proposes building independent intelligent systems to predict possibilities of malware through different angles: Generic properties, Import table properties, Opcode properties etc. Each single intelligent system does not have highest prediction accuracy; whereas, collaboration of independent intelligent systems can bring accuracy improvements over single ones in experiments, which brought tremendous value on helping improving computer information security.
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Diao, L., Xu, H. (2024). Collaboration of Intelligent Systems to Improve Information Security. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_6
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