Novel linguistic steganography based on character-level text generation

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down

Guest Editorial Special Issue on Graph-Powered Machine Learning for Internet of Things

Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph data continuously, which stimulates fertilization and making use of advanced graph-powered methods on the diverse, dynamic, and large-scale graph IoT data

A Novel Design to Minimise the Energy Consumption and Node Traversing in Blockchain Over Cloud Using Ensemble Cuckoo Model

The article outlines the Blockchain’s behavioral model for services. Their reliability is proven through the use of experimental evidence. The authors highlight the major technical aspects and characteristics that are associated with the transmission of data through the network. The authors define the scheme for the network, which works with blockchain transactions, and the relationship between network characteristics on parameters used by the application. They examine the use of this model to identification of the blockchain service and also the likelihood of existing security mechanisms that are based on the technology being bypassed. Additionally, the article provides guidelines to conceal the Blockchain's traffic profile to make it more difficult for its detection in the information network. This study offers a thorough analysis of blockchain-based trust models applied to cloud computing. The paper highlights the challenges that remain unsolved and offers suggestions for future studies in the area based on new cloud-edge trust management system and double-blockchain structure, which is a cloud-based transaction model. The paper also identifies the existing challenges and offers suggestions for future studies in the area based on new cloud-edge trust management system and double-blockchain structure, which is a cloud-based transaction model. The flow of the network will be supported by models that are enhanced by cuckoo to frame the perfect network transform of data from one point to cluster, or alternatively

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

DT-CP: a double-TTPs based contract-signing protocol with lower computational cost

This paper characterizes a contract signing protocol with high efficiency in Internet of Things. Recent studies show that existing contract-signing protocols can achieve abuse-freeness and resist inference attack, but cannot meet the high-efficiency and convenience requirement of the future Internet of things applications. To solve this problem, we propose a novel contract-signing protocol. Our proposed protocol includes two main parts: 1) we use the partial public key of the sender, instead of the zero-knowledge protocol, to verify the intermediate result; 2) we employ two independent Trusted Third Parties (TTPs) to prevent the honest-but-curious TTP. Our analysis shows that our double TTP protocol can not only result in lower computational cost, but also can achieve abuse-freeness with trapdoor commitment scheme. In a word, our proposed scheme performs better than the state of the art in terms of four metrics: encryption time, number of exponentiations, data to be exchanged and exchange steps in one round contract-signing

FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks