Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methodsthe k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniquesas well as three unsupervised methodsthe local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

With the development of Internet of Things (IoT) technology, the demand for secure communication by smart devices has dramatically increased, and the security of the IoT protocol has become the focus of cyberspace. Recently, some scholars have attempted to extend the IPSec protocol to IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) to ensure end-to-end security, which makes it essential to analyze the vulnerability of the IPSec protocol to enhance the security of the IoT. In this study, we use a method combining model learning and model checking to analyze the dynamic vulnerability of IPSec protocol implementations. This method automatically infers the black-box model and compares it with the relevant specifications to expose the defects of the system implementation and search its logic vulnerabilities. We first employ model learning on three IPSec implementations to infer state machine models; then, we use model checking to verify that these models satisfy basic security properties and conform to the RFCs. Our analysis reveals three new security issues: a wrong interaction causing server exception and two violations of the standard.

The Industrial Internet of Things (IIoT) improves productivity and intelligent manufacturing process through revolutionary technology. Due to the complexity of the manufacturing process, cross-domain access is inevitable. Recently, Meng et al. proposed a secure and efficient blockchain-assisted entity authentication mechanism BASA for IIoT cross-domain. In the BASA scheme, the authors utilized identity-based signature (IBS) to realize mutual authentication and the Ephemeral Elliptic Curve Diffie-Hellman (ECDHE) exchange mechanism to negotiate the session key. Due to the inherent key escrow problem of identity-based cryptography (IBC), the key generation center (KGC) can obtain the session key negotiated between two entities distributed in different domains. When KGC is threatened, the security of the session key is worrying. Considering this security concern, based on the BASA scheme, in this article, we first show a secure and efficient certificateless public-key signature (CL-PKS) scheme with anonymity. Then, combined with the ECDHE key exchange mechanism, we give an efficient cross-domain authentication and key agreement scheme CL-BASA with the aid of consortium blockchain. After that, we make security verification by the formal analysis tool, Tamarin, which shows that our CL-BASA is secure. The evaluation demonstrates that our CL-BASA may have a slight disadvantage in storage overhead, but it has obvious advantages than competitor schemes in terms of communication overhead and computational overhead.

The encryption of network traffic promotes the development of encrypted traffic classification and identification research. However, many existing studies are only effective for closed-set experimental data, that is to say, only for traffic of known classes, while there are often lots of unknown classes traffic in the real environment of open sets, and many studies have difficulty identifying the traffic of unknown classes and can only misclassify them as known classes. How to identify unknown traffic and classify known traffic in an open-collection environment is one of the focuses of traffic analysis research. Considering these problems, this paper proposes a novel solution, which applies the open-set recognition method to the unknown traffic identification, and constructs a model based on deep learning and ensemble learning. The method constructs a model based on a convolutional neural network and a transformer encoder and then uses a three-stage training and testing process, combined with a novel loss function, to generalize to the open space to form OpenCBD. Experiments on public datasets show that the proposed method is significantly better than other open-set identification methods. It can not only distinguish known traffic from unknown traffic but also identify specific classes of known traffic.

As a category of algorithms to solve the shortest lattice vector problem, sieve algorithms have drawn more and more attention due to the prominent performance in recent years. Enumeration algorithms used to perform better in practice even though sieve algorithms are asymptotically faster. Combined with techniques like locality-sensitive hashing and rank reduction, sieve algorithms now are capable of competing with enumeration algorithms. In this work, we study sieve algorithms in solving the shortest vector problem on lattices by categorizing various sieve algorithms and elaborating on ideas and techniques used to improve sieve algorithms. In addition, we present several prospective directions worth future research.