Abstract With rapid development of next-generation mobile networks and communications (5G networks), group-oriented applications in resource-constrained smart environments (RSEs), such as smart homes and smart classrooms, have attracted great attentions. Due to the insecure communications between resource-constrained devices, secure group communications in RSE toward 5G face many challenges. In RSE toward 5G, lightweight communications and low computational overheads are crucial. Besides, the private tokens used to generate the group key are expected to be reused multiple times. However, the conventional frameworks for secure group communications cannot meet these requirements. A practical construction of extremely lightweight constant-round membership authenticated group key establishment framework is proposed in this paper for RSE toward 5G, which not only implements identity authentication among the members and group key establishment but also ensures extremely lightweight computation and communication costs by each group member. In our proposed scheme, the increase in the number of group members will not lead to a linear or logarithmic increase in the communication and calculation costs at the member side. Our framework also resists external and internal attacks and meets all the desirable security features. In this framework, the privacy of tokens can be well protected, so that they can be reused for multiple times. Therefore, our scheme significantly reduces the costs of communication and calculation, and it is more efficient compared with the related schemes in the literature. This proposal is fairly suitable for lightweight membership authentication and group key establishment in RSE toward 5G.

Abstract The Fiat-Shamir with Aborts paradigm of Lyubashevsky has given rise to efficient lattice-based signature schemes. One popular implementation is Dilithium, which has been selected for standardization by the US National Institute of Standards and Technology (NIST). Informally, it can be seen as a lattice analog of the well-known discrete-logarithm-based Schnorr signature. An interesting research question is whether it is possible to combine several unrelated signatures, issued from different signing parties on different messages, into one single aggregated signature. Of course, its size should be significantly smaller than the trivial concatenation of all signatures. Ideally, the aggregation can be done offline by a third party, called public aggregation. Previous works have shown that it is possible to half-aggregate Schnorr signatures, but it was left open if the underlying techniques can be adapted to the lattice setting. In this work, we show that, indeed, we can use similar strategies to obtain a signature scheme allowing for public aggregation whose hardness is proven assuming the intractability of well-studied problems on module lattices. Unfortunately, our scheme produces aggregated signatures that are larger than the trivial solution of concatenating. This is due to peculiarities that seem inherent to lattice-based cryptography. Its motivation is thus mainly pedagogical.

Abstract Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.

Abstract Edge connectivity is an important parameter for the reliability of the inter-connection network. A graph $G$ is strong Menger edge-connected ($SM$-$\lambda $ for short) if there exist min$\{\deg _{G}(u),\deg _{G}(v)\}$ edge-disjoint paths between any pair of vertices $u$ and $v$ of $G$. The conditional edge-fault-tolerance strong Menger edge connectivity of $G$, denoted by $sm_{\lambda }^{r}(G)$, is the maximum integer $m$ such that $G-F$ remains $SM$-$\lambda $ for any edge set $F$ with $|F|\leq m$ and $\delta (G-F)\geq r$, where $\delta (G-F)\geq r$ is the minimum degree of $G-F$. Most of the previous papers discussed $sm_{\lambda }^{r}(G)$ in the case of $r\leq 2$. In this paper, we show that $sm_{\lambda }^{r}(FQ_{n})=2^{r}(n-r+1)-(n+1)$ for $1\leq r\leq n-2$, where $n\geq 4$.

Abstract Android is the dominant operating system in the smartphone market and there exists millions of applications in various application stores. The increase in the number of applications has necessitated the detection of malicious applications in a short time. As opposed to dynamic analysis, it is possible to obtain results in a shorter time in static analysis as there is no need to run the applications. However, obtaining various information from application packages using reverse engineering techniques still requires a substantial amount of processing power. Although some attempts have been made to solve this problem by analyzing binary files without decoding the source code, there is still more work to be done in this area. In this study, we analyzed the applications in bytecode level without decoding the binary source files. We proposed a model using Term Frequency - Inverse Document Frequency (TF-IDF) word representation for feature extraction and Extreme Gradient Boosting (XGBoost) method for classification. The experimental results show that our model classifies a given application package as a malware or benign in 2.75 s with 99.05% F1-score on a balanced dataset, and in 3.30 s with 99.35% F1-score on an imbalanced dataset containing obfuscated malwares.

Abstract Traffic prediction has attracted a lot of attention in recent years. However, it is challenging due to the dynamic and heterogeneous spatial–temporal correlations. Most of the existing methods adopt the attention mechanism to capture the dynamic spatial features, but it is difficult for the attention mechanism to learn the real-time change of spatial dependencies, which restrict accurate spatial dependencies learning. Furthermore, most methods are out at elbows when solving heterogeneous spatial–temporal data. To overcome these problems, we propose a novel traffic prediction model called Dynamic spatial–temporal Heterogeneous Graph Convolution Network. Different from the existing methods, we have designed a dynamic localized graph and a corresponding adaptive localized graph convolution network, which are capable of simultaneously capturing dynamic and heterogeneous spatial correlations. We propose a gated adaptive temporal convolution network to capture the temporal heterogeneity of traffic data and enjoy global receptive fields. Finally, a global correlations fusion network is provided to incorporate the global spatial–temporal correlations. Compared with 11 baselines, our proposed model achieves state-of-the-art performance in the accuracy of prediction.

Abstract Software defined networking (SDN) has brought a novel networking paradigm for achieving the goal of anonymous communication. In this paper, we propose a strong anonymous communication scheme based on segment routing (STAR), which does not need all forwarding devices to support OpenFlow protocol and is easy to deploy in SDNs. In STAR, by expanding the packet format of segment routing via Sphinx protocol, the required routing information is encrypted and hidden in the packet header to prevent the enemy from associating with the communication parties. Moreover, to avoid the adversary connecting the communication parties based on load information, the trust controller is employed as an auxiliary node to negotiate the symmetric key between the communication parties for encrypting the packet load. The theoretical analysis shows that, when the adversary compromises multiple intermediate nodes, a low correct linking probability and effective attack resistance are obtained, which proves the weak correlation and stronger anonymity of STAR. Besides, evaluation results confirm that, compared with existing anonymous systems, the proposed STAR can ensure stronger anonymity and higher throughput (83.7% for that of no anonymity) by just introducing very small communication latency (microseconds) and resource cost. Especially for the large-volume data in large-scale SDNs, the advantages will be more obvious.

Abstract Online education is becoming more and more popular with the development of the Internet. In particular, due to the COVID-19 pandemic, many countries around the world are increasing the popularity of online education, which makes the research on sentiment classification of course reviews of online education websites an important research direction in natural language processing tasks. Traditional sentiment classification models are mostly based on English. Unlike English, Chinese characters are based on pictograms. Radicals of Chinese characters can also express certain semantics, and characters with the same radical often have similar meanings. Therefore, RSCOEWR, a word-level and radical-level based sentiment classification model for course reviews of Chinese online education websites is proposed, which solves the problem of data sparsity of reviews by feature extraction of multiple dimensions. In addition, a deep learning model based on CNN, BILSTM, BIGRU and Attention is constructed to solve the problem of high dimension and assigning the same attention to context of traditional sentiment classification model. Extensive comparative experiment results show that RSCOEWR outperforms the state-of-the-art sentiment classification models, and the experimental results on public Chinese sentiment classification datasets prove the generalization ability of RSCOEWR.

Abstract Redactable signatures allow the signature holder to remove admissible data blocks in the signed data while generating valid signatures about different redacted data without communicating with the primary signer. Now, this sort of signature has attracted widespread attention due to its many application scenarios such as electronic medical records, smart grids and XML files. However, there are rarely redactable signature schemes that can resist quantum attacks so far. In the wake of quantum calculation era, it is essential to blossom more quantum-resistant redactable signatures for different data structures. Moreover, it is popular to use accumulators to design redactable signature schemes. Unfortunately, the existing accumulators do not support tree data structures. Therefore, this paper first gives the definition of accumulator schemes for trees, and designs a lattice-based accumulator scheme for trees. Our accumulator scheme features shorter accumulator values and a faster witness generation algorithm than existing lattice trapdoor accumulators. Second, this paper resorts to approximate trapdoors and the preimage sampleable technique, and presents a lattice-based redactable signature scheme for trees using our accumulator scheme. Meanwhile, this scheme fulfills unforgeability, transparency and privacy under adaptive chosen-message attacks. Furthermore, the experiment results show that the redactable signature scheme meets actual revision requirements well.