Abstract Partial neural distinguishers limit the available ciphertext bit combinations in differential neural cryptanalysis. When the training data size and the number of bits are not appropriately selected, label collisions can occur, which adversely affects key recovery efficiency. This paper conducts an analysis to investigate the correlation between the number of bits and the data size, aiming to address the aforementioned issue. It develops a strategy to control collisions and mitigate the impact of these collisions on model performance. A Collision-Aware Key Recovery (CAKR) framework is proposed tailored for high-collision data based on this strategy. This framework leverages the distribution characteristics of labels, eliminating the need for training neural distinguishers and significantly reducing both time and resource consumption. Experimental results show that the CAKR framework reduces the key recovery time by 96.8%, 95.5%, and 91.0% for the Speck32/64, Speck64/96, and Speck96/128, respectively. Additionally, a bit search algorithm is proposed that incorporates a differential evolution strategy and uses the non-uniformity of the ciphertext difference distribution among positive samples as the fitness criterion. Frequent calls to the neural distinguisher are avoided by our method, reducing the search time from 3.286 h to 7.464 s for 8-bit combinations in Speck32/64. The CAKR framework also offers a quantum version that theoretically further reduces time complexity.

Differential-neural cryptanalysis has become a frontier method for evaluating the security of block ciphers. This optimizes the security boundaries of embedded devices in the Internet of Things (IoT) more effectively. However, it is challenging to construct an effective dataset for the differential neural distinguisher. The main challenge lies in that when expanding to deep rounds, the distribution of data features becomes scattered and a computational complexity disaster arises. Therefore, this paper proposes a dual coupling optimization model called the Normality Test Search Dataset Model (NTSD). The model achieves synergistic breakthroughs in search dataset enhancement and search efficiency. First, we propose a differential-key constraint mechanism. The mechanism enhances the anti-decay capability of the dataset by constraining the number of random keys and the form of ciphertexts in the dataset. This makes the probability of collision higher and also strengthens the overall features. Additionally, the distribution features of the enhanced dataset in high rounds are significantly different from those of random datasets. Second, we propose a more efficient dual evaluation strategy. This strategy employs a dual normality distribution detection method to reduce the complexity of searching input differences. Experiments show that the execution time of the NTSD model is approximately 80% less than that of Seok’s method. Finally, the NTSD model is also used in a variety of ciphers. For instance, GIFT-COFB has advanced from round 4 to round 6. The accuracy of ASCON-PERMUTATION in four rounds has increased from 50.69% to 58.23%.

Existing misinformation detection benchmark datasets (e.g., COVMIS and LIAR2) are limited by their reliance on fact-checking labels that are prone to factual inaccuracies due to cognitive constraints of fact-checkers and outdated labels. Prior misinformation detection tasks have been hindered by the dual problems of label redundancy and cold start. To this end, we propose a novel Cyclic Evidence-based Misinformation Detection (CEMD) framework, which incorporates two core mechanisms: (i) a Retrieval Augmented Generation (RAG) pipeline that leverages the latest external knowledge to augment insufficient prior knowledge; and (ii) a cyclic evidence-bootstrapping mechanism that mitigates label redundancy and cold start. We introduce an improved dataset, COVMIS2, built upon COVMIS, and conduct comprehensive experiments to evaluate the efficacy of our framework. Our results demonstrate that the CEMDo outperforms the prior state-of-the-art (SOTA) baseline on LIAR2 by 11.95% and surpasses the human baseline on COVMIS2 by 6.31%, leveraging the Llama-3-70B-Instruct model to augment prior knowledge and the DoRA fine-tuned Llama-3-8B-Instruct model for binary classification. Furthermore, we curate new benchmark datasets, COVMIS2024 and LIAR2024, by recategorizing the redundant labels of COVMIS2 and LIAR2 through the CEMDo.

Steganography is an important technology for ensuring the security of cyberspace and the privacy of communications. In the last decade, emerging biotechnology has made it possible for DNA to be used as a promising steganographic carrier with high hidden capacity, high imperceptibility and high feasibility. However, severe statistical distortion might appear in steganographic carriers generated by existing DNA steganographies when they are compared with the natural ones. Therefore, efforts are being made to seek an advanced strategy to generate quasi-natural steganographic carriers with a strong anti-steganalysis capability. In this work, we first thoroughly analyze and model the numerous complicated statistical properties that exist in natural DNA chains, and then utilize the LSTM model to learn the serialized statistical properties. After obtaining an optimal sequence model that highly satisfies the statistical properties of natural DNA chains, we utilize the Adaptive Dynamic Grouping (ADG) algorithm to perform information hiding. In addition, we have carried out experimental analysis and verification from the perspectives of perceptual-imperceptibility, statistical-imperceptibility, and anti-steganalysis capability, all of which show that our proposed steganography method vastly outperforms previous DNA steganographic methods, taking a successful step towards achieving higher security DNA steganography.

Driven by the increasingly serious air pollution problem, nowadays different systems can be used to achieve the monitoring task of air quality index (AQI) in urban areas. In this article, we design a novel unmanned aerial vehicle-aided (UAV-aided) AQI monitoring system, called AQ360, which detects the air quality level from the 360-degree aerial panoramic images taken by the onboard camera. Specifically, we first present our own AQI recognition approach based on the physical form of the haze pictures, where the AQI is jointly decided by the images captured along six directions over the target location. Then, we study the UAV placement problem of selecting UAV's flight altitude and 2-D coordinates during the monitoring process. The objective is to save the system energy consumption while maintaining the accuracy of estimating AQI distribution. For practical considerations, we implement and evaluate the proposed system in real-world scenarios. The results show that our system can provide a lower AQI recognition error compared with existing vision-based monitoring approaches, and energy consumption is also reduced when applying for large-area tasks.

In recent years, linguistic steganography based on text auto-generation technology has been greatly developed, which is considered to be a very promising but also a very challenging research topic. Previous works mainly focus on optimizing the language model and conditional probability coding methods, aiming at generating steganographic sentences with better quality. In this paper, we first report some of our latest experimental findings, which seem to indicate that the quality of the generated steganographic text cannot fully guarantee its steganographic security, and even has a prominent perceptual-imperceptibility and statistical-imperceptibility conflict effect (Psic Effect). To further improve the imperceptibility and security of generated steganographic texts, in this paper, we propose a new linguistic steganography based on Variational Auto-Encoder (VAE), which can be called VAE-Stega. We use the encoder in VAE-Stega to learn the overall statistical distribution characteristics of a large number of normal texts, and then use the decoder in VAE-Stega to generate steganographic sentences which conform to both of the statistical language model as well as the overall statistical distribution of normal sentences, so as to guarantee both the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic texts at the same time. We design several experiments to test the proposed method. Experimental results show that the proposed model can greatly improve the imperceptibility of the generated steganographic sentences and thus achieves the state of the art performance.

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

Indoor air pollution has become a growing health risk, but it is challenging to provide low-cost air quality monitoring for the indoor environment. In this article, we present “AirScope,” a mobile sensing system that employs cooperative robots to monitor the indoor air quality. Since the wireless coverage can be incomplete in some indoor areas, AirScope allows the robots to defer uploading the data to the central server by utilizing their own data buffers. In order to guarantee the timeliness of the data in the server, AirScope aims to minimize the average data latency by properly planning the routes of the robots. Such a route planning strategy has to be implemented in a distributed way since the robots that are out of wireless coverage can only make plans on their own. In addition, the cooperation of the robots is also necessary because the aggregation of the robots in a small area increases the average data latency of the other unattended areas. To solve this distributed and cooperative routing planning problem, we propose a solution based on distributed deep Q-learning (DDQL). We evaluate the system performance by simulations and real-world experiments. The results show that AirScope is effective to reduce data latency, where the proposed DDQL is 8% better than the greedy algorithm and 24% better than the random strategy.

Due to the significant air pollution problem, monitoring and prediction for air quality have become increasingly necessary. To provide real-time fine-grained air quality monitoring and prediction in urban areas, we have established our own Internet-of-Things-based sensing system in Peking University. Due to the energy constraint of the sensors, it is preferred that the sensors wake up alternatively in an asynchronous pattern, which leads to a sparse sensing dataset. In this paper, we propose a novel approach to predict the real-time fine-grained air quality based on asynchronous sensing. The sparse dataset and the spatial-temporal-meteorological relations are modeled into the correlation graph, in which way the prediction procedures are carefully designed. The advantage of the proposed solution over existing ones is evaluated over the dataset collected by our air quality monitoring system.

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. The objective of our optimization problem is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy a limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

Given the increasingly serious air pollution problem, air quality index (AQI) monitoring in urban areas has drawn considerable attention. This paper presents ImgSensingNet, a vision guided aerial-ground sensing system, for air quality monitoring and forecasting by the fusion of haze images taken by the unmanned-aerial-vehicle (UAV) and the AQI data collected by an on-ground wireless sensor network. Specifically, ImgSensingNet first leverages the computer vision technique to tell the AQI scale in different regions from the haze images, where haze-relevant features and a deep convolutional neural network (CNN) are designed for direct learning between haze images and corresponding AQI scale. Based on the learnt AQI scale, ImgSensingNet determines whether to wake up on-ground wireless sensors for small-scale AQI monitoring and inference, which can greatly reduce the energy consumption of the system. An entropy-based model is employed for accurate real-time AQI estimation at un-measured locations and future air quality distribution forecasting. We implement and evaluate ImgSensingNet on two university campuses since Feb. 2018, and has collected 17,630 photos and 2.6 millions of AQI data samples. Experimental results confirm that ImgSensingNet can achieve high estimation accuracy while greatly reduce the battery consumption, compared to other state-of-the-art AQI monitoring approaches.

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation, and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q -learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

Unmanned Aerial Vehicle (UAV) has been recognized as a promising way to assist future wireless communications due to its high flexibility of deployment and scheduling. In this paper, we focus on temporarily deployed UAVs that provide downlink data offloading under a macro base station (MBS), where the MBS allocates some of its spectrum to the UAVs in an exclusive usage mode. Since the manager of the MBS and the operators of the UAVs could be of different interest groups, we formulate the spectrum trading problem by means of contract theory, where the manager of the MBS has to design an optimal contract to maximize its own revenue. Such contract comprises a set of bandwidth-price options, and each UAV operator only chooses the most profitable one from the whole contract. We analytically derive the optimal contract design, and then propose a dynamic programming algorithm to achieve the optimal result in polynomial time. By simulations, we compare the outcome of the MBS optimal contract with that of a socially optimal one, and find that a selfish MBS manager sells less bandwidth to the UAV operators.

Unmanned aerial vehicle (UAV) has been recognized as a promising way to assist future wireless communications due to its high flexibility of deployment and scheduling. In this paper, we focus on temporarily deployed UAVs that provide downlink data offloading in some regions under a macro base station (MBS). Since the manager of the MBS and the operators of the UAVs could be of different interest groups, we formulate the corresponding spectrum trading problem by means of contract theory, where the manager of the MBS has to design an optimal contract to maximize its own revenue. Such contract comprises a set of bandwidth options and corresponding prices, and each UAV operator only chooses the most profitable one from all the options in the whole contract. We analytically derive the optimal pricing strategy based on fixed bandwidth assignment, and then propose a dynamic programming algorithm to calculate the optimal bandwidth assignment in polynomial time. By simulations, we compare the outcome of the MBS optimal contract with that of a socially optimal one and find that a selfish MBS manager sells less bandwidth to the UAV operators.

Mutation Based Fault Localization(MBFL) is a fault localization technique based on mutation analysis, which precisely identifies the location of fault but incurs a high execution cost, since it needs to execute the test suite on a large amount of mutants. Reduction strategies proposed are usually regarding selecting mutation operators or sampling mutants directly, meanwhile at the cost of losing precision of fault localization. This paper proposes a Statement-Oriented Mutant Reduction strategy (SOME), which selects a proportion of mutants at the statement level, specifically, the statements covered by failed tests. SOME keeps the advantage of using whole types of mutation operators, and further considers the increase of mutants' diversity to avoid the precision loss of fault localization. Empirical studies are conducted on 112 faulty versions from 7 benchmark programs, and the results indicate that SOME can reduce 73.51%-79.98% mutation execution cost while keeping almost the same fault location precision as the original MBFL without reduction..

Recent improvements in vehicular ad hoc networks are accelerating the realization of intelligent transportation system (ITS), which not only provides road safety and driving efficiency, but also enables infotainment services. Since data dissemination plays an important part in ITS, recent studies have found caching as a promising way to promote the efficiency of data dissemination against rapid variation of network topology. In this paper, we focus on the scenario of roadside unit (RSU) caching, where multiple content providers (CPs) aim to improve the data dissemination of their own contents by utilizing the storages of RSUs. To deal with the competition among multiple CPs for limited caching facilities, we propose a multi-object auction-based solution, which is sub-optimal and efficient to be carried out. A caching-specific handoff decision mechanism is also adopted to take advantages of the overlap of RSUs. Simulation results show that our solution leads to a satisfactory outcome.

Wireless network virtualization has been well recognized as a way to improve the flexibility of wireless networks by decoupling the functionality of the system and implementing infrastructure and spectrum as services. Recent studies have shown that caching provides a better performance to serve the content requests from mobile users. In this paper, we propose that caching can be applied as a service in mobile networks, i.e., different service providers (SPs) cache their contents in the storage of wireless facilities that are owned by mobile network operators. Specifically, we focus on the scenario of small-cell networks, where cache-enabled small-cell base stations are the facilities to cache contents. To deal with the competition for storage among multiple SPs, we design a mechanism based on multi-object auctions, where the time-dependent feature of system parameters and the frequency of content replacement are both taken into account. Simulation results show that our solution leads to a satisfactory outcome.

Roadside units (RSUs) with caching abilities are becoming an important part for the future transportation system, enabling both Internet accesses and local caching services for vehicular users. In this paper, we address the caching problem which involves the coexistence of multiple service providers who intend to cache their own contents into the RSUs by competitions to improve the data disseminations. And we propose a mechanism based on multi-object auctions, which can achieve a sub-optimal outcome. Simulation results also show the effectiveness of our solution.

As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively.

Recently, research on web services composition has attracted a lot of attentions from both academic and industry. Related research projects and prototype platforms are also been developed. We have implemented the semi-automatic services composition with workflow technology which combines Business Process Execution Language for Web Services with process ontology. We propose a method for semi-automatic composition of web services using process ontology. This method describes unfixed part of a process by process ontology, The fixed part is described by BPEL. In this paper, we propose a flexible framework for web services composition which consists of services composition model and services composition execution. The former includes mainly web services discovery, composition and verification, the latter includes web services composition composer and execution. A framework based on this approach has been successfully implemented in the DartFlow project.