Summary Secure two‐party computation allows a pair of parties to compute a function together while keeping their inputs private. Ultimately, each party receives only its own correct output. In this paper, a post‐quantum secure two‐party computation protocol is proposed that can be used to effectively block malicious parties. The protocol solves the problems of traditional protocols based on garbled circuits, which are vulnerable to quantum attacks, high communication costs and low computational efficiency. The input garbled keys of the circuit constructor is structured as a Learning with Error (LWE) equation, enabling the circuit constructor to employ a zero‐knowledge proof that demonstrates the uniformity of inputs across all circuits.In the key transfer phase, an LWE‐based batch single‐choice cut‐and‐choose oblivious transfer is proposed to avoid selective failure attacks. In addition, the protocol employs a penalty mechanism to detect if the circuit constructor has generated an incorrect circuit. We have compared the communication overhead of this protocol with three other secure two‐party computation protocols based on Cut‐and‐Choose technology. The analytical results show that this protocol has the best error probability and is resilient to quantum attacks under the malicious adversary model. In addition, with appropriate parameters, the protocol is able to reduce its communication bandwidth by an average of 40.41%.

Most current broadcast encryption with optimal parameters is limited to Nick’s class 1 (NC1) circuits and does not support polynomial‐depth circuits (P‐depth circuits), making it difficult to provide flexible access control in broadcast channels among vast user groups. To address this problem, we propose a ciphertext‐policy attribute–based encryption (CP‐ABE) that supports P‐depth circuits on lattices, achieving fully collusion resistance with randomization via the matrix tensors, thereby, making it impossible for unauthorized users to get any details about the plaintext even though they join forces and reducing the security to the evasive learning with errors (evasive LWE). By using matrix tensor–based randomization and evasive LWE, we achieve a new optimal broadcast encryption scheme based on lattice specifically designed to support P‐depth circuits. Since the matrices we choose as tensors have a low‐norm block diagonal structure, the use of evasive LWE is sufficient to ensure security for our scheme. Compared with similar studies, it not only avoids being involved with low‐norm matrices that restrict the system to NC1 circuits, but also eliminates the need for an additional assumption of the unproven tensor LWE. In addition, the use of matrix tensors further expands the dimensionality, which in turn enables the encryption of bit strings rather than a single bit, significantly reducing ciphertext expansion. Meanwhile, the CP‐ABE that we use to achieve the broadcast encryption scheme has a more compact ciphertext with a parameter size of O ( m 2 · d ).

Recently, security policies and behaviour detection methods have been proposed to improve the security of blockchain by many researchers. However, these methods cannot discover the source of typical behaviours, such as the malicious applications in the blockchain environment. Android application is an important part of the blockchain operating environment, and machine learning-based Android malware application detection method is significant for blockchain user security. The way of constructing features in these methods determines the performance. The single-feature mechanism, training classifiers with one type of features, cannot detect the malicious applications effectively which exhibit the typical behaviours in various forms. The multifeatures fusion mechanism, constructing mixed features from multiple types of data sources, can cover more kinds of information. However, different types of data sources will interfere with each other in the mixed features constructed by this mechanism. That limits the performance of the model. In order to improve the detection performance of Android malicious applications in complex scenarios, we propose an Android malicious application detection method which includes parallel feature processing and decision mechanism. Our method uses RGB image visualization technology to construct three types of RGB image which are utilized to train different classifiers, respectively, and a decision mechanism is designed to fuse the outputs of subclassifiers through weight analysis. This approach simultaneously extracts different types of features, which preserve application information comprehensively. Different classifiers are trained by these features to guarantee independence of each feature and classifier. On this basis, a comprehensive analysis of many methods is performed on the Android malware dataset, and the results show that our method has better efficiency and adaptability than others.

The existing anti‐virus methods extract signatures of software by manual analysis. It is inefficient when they deal with a large number of malware. Meanwhile, the limitation of unknown malware detection often is found in them too. By the research on software structure, it has been found that the control flow of software can be divided into many basic blocks by the interior cross‐references, and a feature‐selection approach based on this phenomenon is proposed. It can extract opcode sequences from the disassembled program, and translate them into features by vector space model. The algorithms of data mining are employed to find the classify rules from the software features, and then the rules can be applied to the malware detection. Experimental results illustrate that the proposed method can achieve the 97.0% malware detection accuracy and 3.2% false positive rate with the Random Forest classifier. Furthermore, as high as 94.5% overall accuracy can be achieved when only 5% experimental data are used as training data.

ABSTRACT The traditional malware detection schemes based on specific signature give an unsatisfactory performance as disposing the previously unknown malware, so the general features of binary files should be explored to solve this problem. Recently, classification algorithms were employed successfully to choose the features in unknown malicious code, and most of the works use byte or operation code sequencen‐gram representation of the executables. However, thesen‐gram representations are heavily dependent on the training data. In this paper, we present a graph‐based method to detect unknown malware. The function call graph of an executable, which includes the functions and the call relations between them, is selected as the representation of the executable in this method. The features are defined according to both the statistical information and the topology of the function call graph. They are extracted and processed through machine learning to classify unknown Portable Executable files. For the sake of fixed sum of the features, the graph‐based method can avoid so many features found in other methods. In our experiments, three types of malware datasets were tested, and as high as 96.8% accuracy can be achieved. Furthermore, it can achieve 92.1% accuracy when only 5% of the dataset is served as training set. Copyright © 2012 John Wiley & Sons, Ltd.