| Title | The Burden of Artificial Intelligence on Internal Security Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ho, Tsung-Yu, Chen, Wei-An, Huang, Chiung-Ying |
| Conference Name | 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) |
| Keywords | artificial intelligence, artificial intelligence security, composability, Computer crime, DGA, Human Behavior, learning, Malware, Metrics, Prediction algorithms, Predictive models, pubcrawl, reliability, resilience, Resiliency, security |
| Abstract | Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work. |
| DOI | 10.1109/HONET50430.2020.9322823 |
| Citation Key | ho_burden_2020 |
The Burden of Artificial Intelligence on Internal Security Detection