Biblio

Most searchable attribute-based encryption schemes only support the search for single-keyword without attribute revocation, the data user cannot quickly detect the validity of the ciphertext returned by the cloud service provider. Therefore, this paper proposes an authorization of searchable CP-ABE scheme with attribute revocation and applies the scheme to the cloud computing environment. The data user to send the authorization information to the authorization server for authorization, assists the data user to effectively detect the ciphertext information returned by the cloud service provider while supporting the revocation of the user attribute in a fine-grained access control structure without updating the key during revocation stage. In the random oracle model based on the calculation of Diffie-Hellman problem, it is proved that the scheme can satisfy the indistinguishability of ciphertext and search trapdoor. Finally, the performance analysis shows that the scheme has higher computational efficiency.

For the occurrence of network attacks, the most important thing for network security managers is how to conduct attack security defenses under low-risk control. And in the attack risk control, the first and most important step is to choose the defense node of risk control. In this paper, aiming to solve the problem of network attack security risk control under complex networks, we propose a game attack risk control node selection method based on game theory. The method utilizes the relationship between the vulnerabilities and analyzes the vulnerability intent information of the complex network to construct an attack risk diffusion network. In order to truly reflect the different meanings of each node in the attack risk diffusion network for attack and defense, this paper uses the host vulnerability attack and defense income evaluation calculation to give each node in the network its offensive and defensive income. According to the above-mentioned attack risk spread network of offensive and defensive gains, this paper combines game theory and maximum benefit ideas to select the best Top defense node information. In this paper, The method proposed in this paper can be used to select network security risk control nodes on complex networks, which can help network security managers to play a good auxiliary role in cyber attack defense.

At present, with the increase of automated attack tools and the development of the underground industrial chain brought by network attack, even well-managed network is vulnerable to complex multi-step network attack, which combines multiple network vulnerabilities and uses the causal relationship between them to achieve the attack target. The detection of such attack intention is very difficult. Therefore, in order to solve the problem that the real attack intention of the attackers in complex network is difficult to be recognized, this paper proposes to assume the possible targets in the network according to the important asset information in the network. By constructing the hierarchical attack path graph, the probability of each hypothetical attack intention target is calculated, and the real attack intention and the most likely attack path of the attacker are deduced. The hierarchical attack path graph we use can effectively overcome the cognitive difficulties caused by network complexity and large scale, and can quantitatively and qualitatively analyze the network status. It is of great importance to make the protection and strategy of network security.