| Title | Recommendation Method of Honeynet Trapping Component Based on LSTM |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Tao, Jing, Chen, A, Liu, Kai, Chen, Kailiang, Li, Fengyuan, Fu, Peng |
| Conference Name | 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
| Date Published | oct |
| Keywords | Analytical models, attention mechanism, Big Data, Bridges, Computer hacking, data privacy, Hacker's interests, honeynet, Human Behavior, human factors, LSTM, Metrics, Network security, Pervasive Computing Security, Predictive models, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in. |
| DOI | 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00159 |
| Citation Key | tao_recommendation_2021 |
Recommendation Method of Honeynet Trapping Component Based on LSTM