With the development of science and technology, the world has become increasingly closely linked. While enjoying the convenience brought by the Internet, we are also facing the danger of risk dissemination. This problem has become more challenging in real-world networks. In this paper, in view of the outbreak of network threats, such as malware, computer viruses, rumors, etc. It is particularly important to identify the source of network threats. In this paper, we have done the following work. Firstly, we draw on the propagation models from epidemiology and design an algorithm partitioned Jordan Center (PJC) to locate the multiple propagation sources. Then, by establishing an extended model originated from propagation sources, we derive the number of sources of estimation. In order to evaluate the performance of the proposed method, a series of experiments were carried out in real-world network topologies. Experimental results show that the method is more accurate than the existing methods.

Cloud platform provides users with shared data storage services. To ensure shared data integrity, it is necessary to validate the data effectively. The audit scheme that supports the group dynamic operations conducts the integrity verification of the shared data, but this approach results in complex calculations for group members. The audit scheme of the designated agent implements the lightweight calculation of the group members, but it ignores the security risks between the group members and the agents. By introducing Hashgraph technology and designing a Third Party Medium (TPM) management strategy, a lightweight secure cloud auditing scheme for shared data supporting identity privacy and traceability (LSSA) is proposed, which realizes the security management of dynamic groups and the lightweight calculations for group members. Meanwhile, a virtual TPM pool is constructed by combining TCP sliding window technology and interconnected functions to improve agent security. Experiments on real data sets show that the theoretical analysis and experimental results are consistent, thereby reflecting the feasibility and efficiency of the scheme.

In recent years, many revocable group signatures schemes were proposed; however, the backward security, which can disable a revoked signer to generate group signatures pertaining to future time periods, was not fully realized through those schemes. In this paper, we present a security model with the definition of backward security and propose a revocable group signatures scheme that is more efficient than previous ones, especially in Sign and Verify algorithms, which are performed much more frequently than others. In addition, considering the heavy workload of group manager in original group signatures, we separate a group into groups by employing a decentralized model to make our scheme more scalable, and thus more practical in real-life applications.

With the widespread use of the Android operating system, the number of applications based on the Android platform is growing. How to effectively identify malware is critical to the security of phones. This paper proposes an Android malware detection method based on the combination of sensitive permissions and API features. This method extracts the permission features and API features by decompiling the APK file, and then uses the mutual information to select sensitive permissions and APIs as feature sets. On this basis, an ensemble learning model based on decision tree classifier and KNN classifier is used to quickly and accurately detect unknown APKs. The experimental results show that the discriminative accuracy of the proposed method is higher than that of the permission set or the API set alone, and the accuracy rate can reach up to 95.5%.

Emerging services such as cloud computing, the Internet of Things, and social networking are driving the growth of human society’s data types and scales at an unprecedented rate. The age of big data has officially arrived. The use of cloud computing technology to bring great convenience to big data processing, solve various deficiencies in traditional processing technology, make big data more application value and service value, but at the same time, it also brings new security problems. By analyzing the security threats faced by cloud computing-based big data platforms, a cloud computing-based big data platform security system framework is proposed, and a security deployment strategy is given.

With the rapid advance of intelligent vehicles, auxiliary driving and automatic driving have been paid more attention to. While vehicle security has become increasingly prominent, which is seriously related to the property and personal safety. The attacker can send abnormal information to the controller through internal CAN bus. Because of the particularity of the vehicle CAN network information communication protocol, the encryption authentication technology cannot effectively solve the safety problem of the vehicle network. In the paper, a novel anomaly detection method based on CAN packet content is proposed. The scheme is effective in preventing in-vehicle ECU attacks caused by malicious modifications. Statistical thinking is adopted to analyze the characteristics of normal message content. Then a confidence interval based on normal features is defined for detecting abnormal network messages. Its detection performance has been demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle.

With the development of Intelligent Connected Vehicles, a large number of automobile cybersecurity incidents also occur. Scientific and reasonable incident response system is the key technology to ensure the successful handling of cybersecurity incidents. From the point of view of management, referring to the construction of incident response system in IT industry and combining with the characteristics of automobile cybersecurity, this paper puts forward the framework of incident response system for automobile cybersecurity. The framework includes five aspects: plan and prepare, detection and reporting, assessment and decision, responses and lessons learnt. Emphasis is laid on the formulation and updating of management policy, the establishment of incident response team, incident coordination mechanism and so on. Then, based on the method of questionnaire survey, the evaluation method of incident response capability is put forward. The research method makes up for the blank of automobile industry in cybersecurity incident response, and has an important positive role in reducing the adverse impact of security incidents.

Biometric credentials have become a popular means of authentication. However, since biometrics are unique and stable, one data breach might cause the user lose some of his biometrics permanently. And the stolen biometrics may be used for identity fraud, posing a permanent risk to the user. There have been many studies addressing this problem, in which the protection of biometric templates is a basic consideration. However, most existing solutions have inefficient security or efficiency. In this paper, we use the ElGamal scheme which shows good performance in applications to construct an efficient, privacy-preserving palmprint authentication scheme. We first construct a palmprint recognition scheme based on palm lines and feature points with good performance. Then, we use the RP (random projection) method to effectively reduce the extracted palmprint features, which greatly reduces the volume of data to be stored. Finally, we design a confidential comparison process based on the ElGamal scheme to perform efficient comparisons of palmprint features while ensuring provable security. Subsequent theoretical analysis/proof and a series of experiments prove the significance and validity of our work.

The traditional cryptosystem is based on the security of private key. While the private key is leaked, the signature information may be exposed. Based on this, a threshold signature scheme with strong forward security based on Chinese remainder theorem is proposed. The signature is generated through the cooperation of members, which solve the problem of authoritative fraud introduced by the dealer. The private key is updated periodically to handle the threat caused by the private key leakage. Security analysis shows that the existing signatures will not be affected by the compromise of the corresponding private keys, and do not allow for forgery of the future signatures, which shows that the new scheme has the forward security and the backward security. The efficiency analysis shows that our scheme is more efficient compared with the well-known existing schemes in the literature.

Differential privacy enables data analysis while protecting individual privacy. However, existing differential privacy database platforms do not defend against repeated attacks. This paper proposes a practical Database Query System for differential privacy protection against repeated attacks with customizable privacy budget. By limiting adversary’s success probability and the number of attacks, the administrator can protect privacy against the repeated attacks. We conduct an evaluation of this solution, and explain the applicability of this system.