Rogue Access Points (RAPs) pose a persistent security threat to IEEE 802.11 Wireless Local Area Networks (WLANs). These attacks enable attackers to monitor, manipulate, and tamper with victims’ communications, resulting in significant privacy breaches and financial losses annually. Among the existing methods, client-based RAP detection has demonstrated greater effectiveness compared to admin-based approaches, primarily due to its wider applicability in real-world environments. However, existing series-model RAP detection methods often suffer from limited robustness and scope of application. To address these challenges, we propose SMART, an innovative series-model RAP detection method. SMART leverages special frame length sequences and arrival times as detection metrics, transforming the RAP detection problem into one of identifying malicious forwarding behavior. This is further simplified into the task of searching for specific sequences and analyzing their arrival times. By designing distinct frame length sequences and setting precise time windows, SMART effectively detects RAPs by identifying abnormal forwarding behavior. Extensive experiments demonstrate that SMART achieves a 100% detection rate in low and medium traffic scenarios and a 98.33% detection rate in high-traffic scenarios, performing reliably in both open and encrypted networks.

Most existing privacy-preserving spatial keyword query schemes rely on exact keyword matching, which cannot effectively capture the keyword relevance between user queries and spatial objects. Moreover, they often neglect the individual preferences of users for different keywords. To overcome these limitations, we propose SSKQ-UP, a novel and efficient privacy-preserving spatial keyword query scheme that supports personalized ranking. The index structure of SSKQ-UP is an R-tree based on Geohash code, called HR-tree. The internal nodes of HR-tree store Geohash codes to achieve rapid spatial pruning. The leaf nodes store the Geohash code of the spatial objects and the Term Frequency-Inverse Document Frequency (TF-IDF) vectors, facilitating filtering based on the spatial range and keywords. Users can submit queries with weight vectors to reflect their keyword preferences. The cloud computes cosine similarity between the query weight vector and the TF-IDF vectors of candidate objects to identify the top-k most relevant results. To ensure privacy, both index and query vectors are encrypted using an Enhanced Asymmetric Scalar Product Encryption (EASPE) algorithm. Security analysis confirms that the proposed scheme is IND-CPA secure. Extensive experiments validate the practicality and efficiency of our scheme, demonstrating superior performance compared to existing methods.

Graph matching, as an important query technology, has been widely applied in various fields. With the increasing of graph data, users choose to encrypt a large number of graphs and store them in the cloud. Existing solutions to graph matching query over encrypted graphs require the user to execute a lot of time-consuming subgraph isomorphism (NP-complete problem) operations to extract the matched graphs, which inevitably brings heavy computational burden to the user. Therefore, how to reduce the number of subgraph isomorphisms is crucial for releasing the user from the heavy workload in a graph matching query scheme over encrypted graphs. In this paper, we propose a secure and efficient scheme for graph matching query over encrypted graphs. The main idea is to classify the query graph into frequent subgraph and infrequent subgraph, and adopt different strategies to perform the matching query. We design the novel secure index based on the frequent subgraphs and the edge labels to reduce the number of subgraph isomorphisms. When the query graph is a frequent subgraph, the proposed scheme can directly produce the exact result owing to this secure index. The user does not need to perform any subgraph isomorphism in this case. When the query graph is an infrequent subgraph, the proposed scheme can return a set of data graphs very close to the exact result. As a result, the proposed scheme reduces the number of subgraph isomorphisms substantially. Formal security proof is provided. Extensive experiments on real-world data sets show that the proposed scheme reduces nearly 90% subgraph isomorphism.

With the rapid development of IoT technology, a vast quantity of spatial data with text information is generated, because of the explosive growth of the spatial data, users usually encrypt these data and outsource them to the cloud for enjoying the storage and computing capability. Privacy-preserving Boolean k Nearest Neighbor (kNN) query is a typical query technique over the spatial data. It finds k objects that exactly match the query keyword and are nearest to the query point upon encrypted spatial data. We propose a scheme which supports the privacy-preserving Boolean kNN query over the cloud-based spatial data in this article. In order to efficiently obtain the spatial objects containing the query keywords, we ask the cloud to pick the objects containing the query keyword with the lowest frequency. Then, the cloud filters out the objects that do not contain other query keywords. Since the number of objects containing the query keyword with the lowest frequency is minimal, the number of objects to filter is also minimal. In this way, the query efficiency is improved. In order to realize convenient and safe distance comparison over the encrypted spatial data, we convert the coordinates to the vectors. The distance between the two points can be expressed as the inner product of the two vectors. Furthermore, we use the enhanced asymmetric scalar-product-preserving encryption algorithm to protect the data privacy. We prove that the proposed scheme satisfies the CQA2-security. Meanwhile, we conduct experiments using the real data sets to show the performance of the proposed scheme.

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, graph user tends to outsource the encrypted graphs to the cloud. The complex graph matching is performed by the cloud. Several schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism (NP-complete problem) operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. Two cloud servers jointly get a matched matrix to represent the matching relationship between vertices in data graph and query graph. Graph user can directly and quickly extract the subgraph isomorphic to query graph from data graph based on the matched matrix. No subgraph isomorphism operation is involved for graph user. The time complexity of subgraph extraction is$O(m^{2})$in our scheme, where$m$is the number of vertices in query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.

K-hop Reachability Query (KRQ) is one of fundamental graph queries, which can answer whether a node u can reach a node v within K hops. With the scale of graph data increasing, data owner desires to outsource the local graphs to cloud server. To protect the graph privacy, data owner encrypts graphs before outsourcing them to the cloud server. It imposes a great challenge to KRQ over encrypted graphs. How to realize Privacy-Preserving K-hop Reachability Query (PPKRQ) over encrypted graphs is still an unexplored problem. In this paper, we explore this problem and propose a practical scheme. In order to efficiently answer KRQ over encrypted graphs, we construct the encrypted Breadth-First Spanning Tree table and adjacent list D (BFST-D). Based on encrypted BFST table, we can directly judge whether two query nodes are reachable within K hops when they are in one spanning tree. The encrypted adjacent list D can help answer that two query nodes in different spanning trees. To protect the privacy, we utilize the Paillier cryptographic and Order-Revealing Encryption (ORE) to support the comparison and computation over ciphertexts. As a result, our scheme achieves the sensitive information privacy without losing the ability of querying over encrypted graphs. The security analysis shows that our proposed scheme is secure based on semi-honest cloud server. The extensive experiments show the efficiency of our scheme.

Linguistic steganalysis is the technology to distinguish whether looking-innocent texts hide covert (possibly hazardous) messages. Traditional methods, dominantly focusing on internal linguistic difference in texts, are seriously challenged by the recent linguistic steganography technology that can reduce the difference to near zero. However, even via the most advanced linguistic steganography methods, due to the random and uncontrollable message bits, steganographic texts may express content against common sense knowledge. To fully employ this defect of linguistic steganography, we propose LINK, a novel Linguistic steganalysis framework with the help of external Knowledge. We link texts to the external knowledge database, and employ Graph Neural Networks (GNNs) to translate linked knowledge into knowledge features, while linguistic features will be captured by the same modules from existing methods. Knowledge features and linguistic features will be combined to make final decisions. Extensive experimental results show that owing to additional external knowledge, the proposed framework can effectively compensate for the shortcomings of existing methods.1

With the integration of the healthcare system, Internet of Things, and cloud storage service, more and more medical institutions upload their electronic medical records (EMRs) to the cloud to reduce the local storage burden and realize data sharing among external researchers. To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that encrypt the entire EMRs can hide the sensitive information, but this results in the shared EMRs being unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis and even endanger the patient’s life. In order to solve the aforementioned problems, in this article, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted, while other contents in EMR can be shared among users in this scheme. Doctors and researchers can quickly perform search operations based on keywords to extract the EMRs they require. This time complexity is$O(n)$, where$n$is the number of attribute values in the record. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud. This time complexity is max$\lbrace O(n^{\prime }),O(N^{\prime })\rbrace$, where$n^{\prime }$is the number of query keywords and$N^{\prime }$is the number of the retrieved records. We expound the security and carry out experiments to estimate the efficiency of the proposed scheme.

In recent years, Industrial Internet of Things (IIoT) has gradually attracted the attention of the industry owing to its accurate time synchronization, communication accuracy, and high adaptability. As an important data structure, graphs are widely used in IIoT applications, where entities and their relationships can be expressed in the form of graphs. With the widespread adoption of IIoT and cloud computing, an increasing number of individuals or organizations are outsourcing their IIoT graph data to cloud servers to enjoy the unlimited storage space and fast computing service. To protect the privacy of graph data, graphs are usually encrypted before being outsourced. In this article, we propose a search pattern privacy system for approximate shortest distance query of encrypted graphs in IIoT. To realize search pattern privacy, we adopt two noncolluded cloud servers to accomplish different tasks. We leverage the first server to store the encrypted data and perform query operations, and use the second one to rerandomize the contents and shuffle the locations of the queried records. Before queries, we generate the trapdoors by using different random numbers. After queries, we ask the second server to rerandomize the contents of the records that the first server touched. In addition, we shuffle the physical locations of original records by inserting some fake records. In this way, all contents and physical locations of the touched records change, so that the first server cannot distinguish whether two queries are the same or not. To enhance the efficiency on the user side, we further improve this system by moving some heavy workloads from the user to the cloud. The security analysis and the performance evaluation show that our work is secure and efficient.

Phrase search encryption, as an important technique in cloud-based IoT system, allows users to retrieve encrypted IoT data that contains a set of consecutive keywords. It plays an important role in cloud-based e-healthcare diagnosis system, machine learning applications for cloud-based IoT system, etc. However, to the best of our knowledge, the existing phrase search encryption schemes cannot achieve the complete verification for search results. They either cannot verify whether the returned files correctly containing the query phrase or cannot verify whether all files containing this query phrase are returned. Result verification is very important for some cloud-based IoT applications. If the search result is incorrect in the cloud-based e-healthcare diagnosis system, it will lead to misdiagnosis even endanger the patient's life. In order to deal with this problem, this article explores how to achieve verifiable phrase search over encrypted cloud-based IoT data. Specifically, we design novel look-up tables which can be utilized to determine and verify the position relationship among keywords. Meanwhile, we adopt a two-phase query strategy. In the first query phase, the data user can know the identifiers of files containing the keywords in the query phrase, and generate the search trapdoor based on these identifiers for the next phase. In the second query phase, the data user can obtain the verification information to check whether all files containing the query phrase are correctly returned. We present the security analysis of our scheme and conduct extensive experiments. The results prove the high security and efficiency of our proposed scheme.

Verifiable Searchable Symmetric Encryption, as an important cloud security technique, allows users to retrieve the encrypted data from the cloud through keywords and verify the validity of the returned results. Dynamic update for cloud data is one of the most common and fundamental requirements for data owners in such schemes. To the best of our knowledge, the existing verifiable SSE schemes supporting data dynamic update are all based on asymmetric-key cryptography verification, which involves time-consuming operations. The overhead of verification may become a significant burden due to the sheer amount of cloud data. Therefore, how to achieve keyword search over dynamic encrypted cloud data with efficient verification is a critical unsolved problem. To address this problem, we explore achieving keyword search over dynamic encrypted cloud data with symmetric-key based verification and propose a practical scheme in this paper. In order to support the efficient verification of dynamic data, we design a novel Accumulative Authentication Tag (AAT) based on the symmetric-key cryptography to generate an authentication tag for each keyword. Benefiting from the accumulation property of our designed AAT, the authentication tag can be conveniently updated when dynamic operations on cloud data occur. In order to achieve efficient data update, we design a new secure index composed by a search table ST based on the orthogonal list and a verification list VL containing AATs. Owing to the connectivity and the flexibility of ST, the update efficiency can be significantly improved. The security analysis and the performance evaluation results show that the proposed scheme is secure and efficient.