Abstract
With the increasingly extensive applications of the network, the security of internal network of enterprises is facing more and more threats from the outside world, which implies the importance to master the network risk assessment skills. To improve the accuracy is an importent issue. In the big data era, there are various security protection techniques and different types of group data. Meanwhile, Online Social Networks (OSNs) and Social Internet of Things (SIoT) are becoming popular patterns of meeting people and keeping in touch with friends (Jiang et al. ACM Comput Surv 49:10:1–10:35 2016; Shen et al. 2017). Risk assessment, as a bridge between security experts and network administrators, whose accuracy can influence the judgment of administrators to the entire network state. In order to solve this problem, this essay uses the Baum Welch algorithm to optimize the risk assessment process by establishing the HMM model, which can improve the accuracy of the evaluation value. Firstly, behavior of the attacker is described in-depth by the attack graph generated through MulVAL framework. Then, the nodes on the attack path can will be evaluated and the value will be further evaluated by the Bayesian model. Finally, by establishing the hidden Markov model, the corresponding parameters can be defined and the most likely probabilistic state transition sequence can be calculated by using the Viterbi algorithm and Baum Welch algorithm to deduce the attack intent with the highest possibility.
Highlights
Increasing cyber attacks have attracted high attention in contemporary data security and network security studies.In wireless sensor network, target tracking [10] and data gathering and aggregating [22]have became more and more concerned
Parameters of the model are redefined through the Baum Welch algorithm and the maximum probability state transition sequence is further calculated by using the Viterbi algorithm
Assuming that the frequency of the sample moving from the hidden state qi to qj is Aij, the state transition matrix is evaluated as Eq 8: 4.3 HMM and Baum Welch algorithm
Summary
Increasing cyber attacks have attracted high attention in contemporary data security and network security studies. In order to explain the process of cyber attack, a number of researchers have proposed risk assessment methods by building security models of network systems through paradigms such as attack graphs. Parameters of the model are redefined through the Baum Welch algorithm and the maximum probability state transition sequence is further calculated by using the Viterbi algorithm. Based on these processes, the intention of attackers has been inferred. The attack intention can be accurately inferred by this comprehensive model This method provides a good representation of network security administrators and equips them with some security strategies to overcome existing shortcoming in the enterprise network
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