Computers & Security最新文献

{"title":"ASIDS: Acoustic side-channel based intrusion detection system for industrial robotic arms","authors":"Kai Yang ,&nbsp;Yingjun Zhang ,&nbsp;Ting Li ,&nbsp;Limin Sun","doi":"10.1016/j.cose.2025.104586","DOIUrl":"10.1016/j.cose.2025.104586","url":null,"abstract":"<div><div>Industrial robotic arms play a vital role in manufacturing systems. However, they are susceptible to attackers executing malicious mechanical movements, thereby presenting significant threats to both industrial manufacturing and human safety. Existing techniques attempt to detect the abnormal signals within a manufacturing network to mitigate these attacks. However, these signals are unreliable since they might be deliberately tampered with by network attackers, including trajectory signals, and thus bypass anomaly detection. In this work, we propose ASIDS, a novel acoustic side-channel intrusion detection system to protect industrial robotic arms against data tampering attacks. We take advantage of an important insight that the acoustic side-channel signal emitted by an industrial robotic arm during a mechanical movement is unique, which could be used to reconstruct industrial robotic arms’ trajectory and detect abnormal movements. In particular, we extract the time-domain and frequency-domain features of the sounds emitted by the industrial robotic arm during a movement and reconstruct its trajectory by using a neural network. The data tampering attack can be detected by identifying the discrepancy between the reconstructed trajectory and the fake trajectory tampered with by the attackers through network traffic. To validate the performance of ASIDS, we have conducted real-world experiments on three industrial robotic arms, testing across more than 25,000 operational cycles. The experimental results indicate that ASIDS can accurately reconstruct trajectories and detect the attacks, achieving an average reconstruction error of 2.36% and an average detection rate of 95.9%.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104586"},"PeriodicalIF":4.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To insure or not to insure: How attackers exploit cyber-insurance via game theory","authors":"Zhen Li ,&nbsp;Qi Liao","doi":"10.1016/j.cose.2025.104585","DOIUrl":"10.1016/j.cose.2025.104585","url":null,"abstract":"<div><div>Cyber-insurance provides organizations with financial protection against losses from cyber incidents. As its adoption grows, organizations face the challenge of balancing investments in cybersecurity defense measures with the acquisition of cyber-insurance. This convergence presents opportunities but also introduces risks. The effects of cyber-insurance on the interplay between cybersecurity investment and attacker strategies remains poorly understood. In this paper, we systematically analyze an organization’s decision-making process regarding optimal cybersecurity investment and cyber-insurance, with a particular focus on the strategic behavior of attackers. Using economic and game-theoretic models, supported by simulation studies, our findings reveal that while cyber-insurance can mitigate financial losses, it may inadvertently weaken overall cybersecurity defenses. Furthermore, we demonstrate that cyber-attacks are not random events but calculated actions influenced by the attacker’s understanding of the organization’s insurance and defense posture. Attackers can exploit cyber-insurance by strategically launching targeted attacks to manipulate an organization’s reliance on insurance and disrupt its investment equilibrium. This manipulation can persist up to a critical threshold, beyond which escalating threats prompt organizations to strengthen their defenses. In this way, attackers effectively “play God,” strategically shaping an organization’s insurance and cybersecurity portfolio. To counter these risks, we propose actionable recommendations to prevent attackers from exploiting the cyber-insurance market, ensuring a more resilient and secure cybersecurity ecosystem.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104585"},"PeriodicalIF":4.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature attention assisted convolutional stacked sparse auto-encoder model for intrusion detection in network function virtualization environment","authors":"Gajanan Nanaji Tikhe,&nbsp;Pushpinder Singh Patheja","doi":"10.1016/j.cose.2025.104595","DOIUrl":"10.1016/j.cose.2025.104595","url":null,"abstract":"<div><div>Network function virtualization (NFV) in 5 G networks has recently received much attention. However, it generates numerous challenges while providing security in emerging technologies such as information, education, biotechnology, etc. NFV exploration has concentrated on intrusion detection because detecting an intrusion is necessary due to the wastage of resources and security threats. Therefore, an intrusion detection system called Feature Attention assisted Convolutional Stacked Sparse Auto-encoder (FA_CS²ANet) Model for Intrusion Detection in the NFV Environment has been proposed. To detect intrusions in the NFV network, the input data is first collected from a publicly available dataset, and then pre-processing is performed to remove the unwanted data using min-max normalization, standardization and missing value replacement. Next, feature selection is done to reduce the dimensionality issues using Chaotic Osprey Optimization (COO). After selecting the necessary features, the intrusions in NFVs are identified by using the deep learning-based FA_CS²ANet model, which is a combination of the Convolutional Neural Network (CNN) and Stacked Sparse Auto-encoder (SSAE) model. The simulation is completed using Python programming, and the results demonstrate that the suggested method outperforms existing methods with an accuracy of 93.12%. The intrusions are discovered, and the suggested method’s performance metrics for accuracy, precision, recall, and F-score are assessed.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104595"},"PeriodicalIF":4.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting DBMS bugs with context-sensitive instantiation and multi-plan execution","authors":"Jiaqi Li ,&nbsp;Ke Wang ,&nbsp;Yaoguang Chen ,&nbsp;Yajin Zhou ,&nbsp;Lei Wu ,&nbsp;Jiashui Wang","doi":"10.1016/j.cose.2025.104564","DOIUrl":"10.1016/j.cose.2025.104564","url":null,"abstract":"<div><div>DBMS (Database Management System) bugs can cause serious consequences, posing severe security and privacy concerns. This paper works towards the detection of crash-related bugs and logic bugs in DBMSs, and aims at solving the two innate challenges, including how to generate semantically correct SQL queries in a test case, and how to propose effective oracles to capture logic bugs. To this end, our system proposes two key techniques. The first key technique is called context-sensitive instantiation, which can obtain all static semantic requirements to guide query generation. The second key technique is called multi-plan execution, which can effectively capture logic bugs. Given a test case, multi-plan execution makes the DBMS execute all query plans instead of the default optimal one, and compares the results. A logic bug is detected if a difference is found among the execution results of the executed query plans. We have implemented a prototype system called Kangaroo and applied it to three widely used and well-tested DBMSs, including SQLite, PostgreSQL, and MySQL. Our system successfully detected 54 previously unknown bugs, including 41 crash-related bugs and 13 logic bugs. The comparison between our system with the state-of-the-art systems shows that our system outperforms them in terms of the number of generated semantically valid SQL queries, the explored code paths during testing, and the detected bugs.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104564"},"PeriodicalIF":4.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical-layer identity-authentication mechanism for network time synchronisation using network and precision time protocols","authors":"Ting He","doi":"10.1016/j.cose.2025.104590","DOIUrl":"10.1016/j.cose.2025.104590","url":null,"abstract":"<div><div>Time-spoofing attacks, especially those using time-source spoofing, pose a serious threat to network time synchronisation. Such attacks can be suppressed by authenticating received time-synchronisation messages at the receiving terminal. Current identity-authentication mechanisms under the network time protocol (NTP) and precision time protocol (PTP) are based on cryptography and network-security technologies and have inherent limitations. This study proposes a novel physical-layer identity-authentication mechanism based on a general physical-layer security-architecture for network time synchronisation and a special system-infrastructure model. In this approach, legitimate messages and transmission paths are endowed with unique characteristics, thus the legitimate time source is uniquely identified. The receiving terminal can determine whether the received signal characteristics and transmission path are consistent with the preset conditions, and thus whether the signal comes from a legitimate time source. Simulation results show that under zero-false-alarm conditions, the proposed physical-layer identity-authentication mechanism successfully suppresses all illegitimate messages in channels containing additive white Gaussian noise and in Rayleigh fading channels. Moreover, this mechanism covers all operational modes of NTP/PTP, achieving a reasonable trade-off between security performance and computational complexity. It can thus significantly improve NTP/PTP resistance to time-source spoofing.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104590"},"PeriodicalIF":4.8,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening edge defense: A differential game-based edge intelligence strategy against APT attacks","authors":"Man Zhou ,&nbsp;Lansheng Han ,&nbsp;Xin Che","doi":"10.1016/j.cose.2025.104580","DOIUrl":"10.1016/j.cose.2025.104580","url":null,"abstract":"<div><div>In modern industrial settings, the Industrial Internet of Things (IIoT) serves as a backbone, connecting devices, sensors, and systems to enhance production efficiency and facilitate real-time data processing and decision-making. As the adoption of IIoT expands, edge nodes have emerged as critical components, functioning as hubs for data collection, transmission, and real-time response. However, their physical accessibility and limited computational resources render them susceptible to Advanced Persistent Threat (APT) attacks. This study proposes a defense mechanism specifically designed for edge nodes to effectively mitigate APT attacks, leveraging a combination of optimal control theory and intelligent edge game theory. First, we develop a system evolution model based on covert adversarial dynamics to accurately capture the complex interactions between attacks and defenses in real-world edge networks, thereby improving detection and response capabilities against emerging threats. Additionally, we propose an attack-defense model that integrates optimal control techniques and differential games, allowing the detection system to dynamically adapt its defense strategies while optimizing the trade-off between attack detection effectiveness and resource utilization efficiency. Finally, we implement a Nash strategy reinforcement learning mechanism based on multi-agent deep Q-networks to optimize edge game strategies and enhance attack detection performance. Experimental evaluations conducted on an ethanol distillation system testbed demonstrate the effectiveness, robustness, and computational efficiency of our defense approach compared to SG-LMM and DDQN-PV methodologies.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104580"},"PeriodicalIF":4.8,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FiPiBox:Development of firewall for IoT networks using P4Pi","authors":"Suvrima Datta ,&nbsp;Venkanna U. ,&nbsp;Aditya Kotha","doi":"10.1016/j.cose.2025.104560","DOIUrl":"10.1016/j.cose.2025.104560","url":null,"abstract":"<div><div>The IoT has experienced remarkable expansion, connecting an extensive array of devices to the internet. With this proliferation, the security of IoT networks has become a paramount concern. Unfortunately, existing security mechanisms failed due to the static security policies, deficiency in understanding device behavioral patterns, limited visibility of IoT traffic flows, and vendor dependency on IoT devices. To overcome the security problems in IoT networks, FiPiBox: a firewall, has been developed by leveraging P4Pi to filter the IoT traffic flows precisely by analyzing the flow behavior. Initially, the incoming IoT traffic flows have been parsed in the FiPiBox data plane to obtain several header field information. Subsequently, the header information is sent to the controller through a message digest. This information helps the FiPiBox controller build the behavioral profile of IoT devices. Further, the FiPibox controller monitors the behavior of incoming IoT traffic flows based on the behavioral profile’s flow statistics. If the controller finds that the incoming traffic behavior is normal, forward the traffic to the desired destination. Otherwise, if the IoT traffic behavior deviates from its normal behavior, quarantine the device for a specified time to understand its behavior. Further, a user interface has been developed to monitor the device’s behavior to take appropriate action. The evaluation result of FiPiBox shows that packet processing time in FiPiBox is 0.01998 ms for 1000 devices and has a nominal false alarm rate (0.034 for 1000 devices), which ensures the reliability of FiPiBox to filter IoT traffic flows. Additionally, FiPiBox updates the firewall rules dynamically based on the IoT traffic behavior. Specifically, FiPiBox takes 0.124 ms to install the firewall rules. Finally, the proposed firewall, FiPibox, emerges as a robust solution to enhance IoT security by accurately filtering IoT traffic flows.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104560"},"PeriodicalIF":4.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PathWatcher: A path-based behavior detection method for attack detection and investigation","authors":"Zehui Wang,&nbsp;Hao Li,&nbsp;Yinhao Qi,&nbsp;Wei Qiao,&nbsp;Song Liu,&nbsp;Chen Zhang,&nbsp;Bo Jiang,&nbsp;Zhigang Lu","doi":"10.1016/j.cose.2025.104563","DOIUrl":"10.1016/j.cose.2025.104563","url":null,"abstract":"<div><div>Advanced Persistent Threats (APTs) comprise complex and stealthy attack techniques. Due to the characteristics of system audit logs in capturing system-level process calls and providing granular log data, using audit logs for causal analysis of advanced threat behaviors has become a popular solution. However, existing solutions still suffer from several deficiencies: (1) semantic gaps between raw data in low-level views and high-level system behaviors, (2) fatigue alert, and (3) poor interpretability and inferability.</div><div>In this paper, we propose PathWatcher, a path-based behavior detection method, which enables attack investigation based on detection results. PathWatcher enhances low-level semantics by combining operation sequences, extracting paths as behavioral entities from the provenance graph, and learning path features. This approach reduces the semantic gap between low-level data and high-level system behaviors. PathWatcher first performs graph construction and path extraction in the graph construction module, followed by feature learning of nodes and paths in the behavioral sequence extraction module, the data generated during the process exists in the path record with a certain rule, and finally the data from the path record is used for feature extraction and path tracing in the behavior identification and attack clues module, the data from the path record is used for feature extraction and path tracing. This model exhibits strong inferability and interpretability by matching paths to operational behaviors in logs. This allows security researchers to combine path records and investigate attacks directly using high-level semantics, thereby alleviating alert fatigue. Our experimental results demonstrate that PathWatcher effectively improves the detection accuracy of malicious behaviors while enhancing semantic interpretability. The detection results are inferable, achieving accuracies of 99.76% and 99.07% on two datasets, and we provide an analysis of attack investigations.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104563"},"PeriodicalIF":4.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LiDAR point cloud transmission: Adversarial perspectives of spoofing attacks in autonomous driving","authors":"Tariq Hussain ,&nbsp;Muhammad Nawaz Khan ,&nbsp;Bailin Yang ,&nbsp;Razaz Waheeb Attar ,&nbsp;Ahmed Alhomoud","doi":"10.1016/j.cose.2025.104544","DOIUrl":"10.1016/j.cose.2025.104544","url":null,"abstract":"<div><div>LiDAR technology uses laser light to illuminate the surrounding area and detect 3D objects. Calculates different features such as distance, shape, height, and direction of objects, ultimately generating comprehensive 3D maps by collecting cloud points. They are frequently used in autonomous vehicles, robotics, forestry, archaeology, and environmental monitoring. LiDAR is important in autonomous vehicles for recognizing objects, pedestrians, and other vehicles, allowing them to make judgments to prevent collisions and ensure human safety. The LiDAR systems are generally robust; they are not immune to certain types of security attacks that could compromise the integrity of the signals and may affect the accuracy of the data. If the signal is compromised, the system could incorrectly interpret the environment, resulting in erroneous object recognition, incorrect obstacle avoidance decisions, or inaccurate environment mapping. As a result, it can lead to serious consequences, such as property damage, accidents, or dangerous driving conditions. To address these security challenges and establish better security mechanisms for LiDAR systems, we have proposed a novel technique for detecting and avoiding all possible spoofing attacks on LiDAR signals. Initially, the system identifies potential spoofing attacks, and as a preventive measure, it employs an optimized path strategy. This strategy ensures safe crossings and autonomous navigation while avoiding obstacles along the vehicle’s route. The main aim is to identify the spoofed objects, suitably map the 3D presentation of the objects, and properly navigate autonomous vehicles with an optimized path selection in the automatic driving system. The proposed system is validated in different scenarios, and the experimental results demonstrate a success rate of 94.57% in true positive and false positive rates, indicating the effectiveness of the system. The average precision rate of 0.95 further supports its performance. The strength of the system was confirmed by testing it with different intersection over union (IoU) rates in different situations and closely looking at the attacker’s success rate.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104544"},"PeriodicalIF":4.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studying the robustness of data imputation methodologies against adversarial attacks","authors":"Arthur Dantas Mangussi ,&nbsp;Ricardo Cardoso Pereira ,&nbsp;Ana Carolina Lorena ,&nbsp;Miriam Seoane Santos ,&nbsp;Pedro Henriques Abreu","doi":"10.1016/j.cose.2025.104574","DOIUrl":"10.1016/j.cose.2025.104574","url":null,"abstract":"<div><div>Cybersecurity attacks, such as poisoning and evasion, can intentionally introduce false or misleading information in different forms into data, potentially leading to catastrophic consequences for critical infrastructures, like water supply or energy power plants. While numerous studies have investigated the impact of these attacks on model-based prediction approaches, they often overlook the impurities present in the data used to train these models. One of those forms is missing data, the absence of values in one or more features. This issue is typically addressed by imputing missing values with plausible estimates, which directly impacts the performance of the classifier.</div><div>The goal of this work is to promote a Data-centric AI approach by investigating how different types of cybersecurity attacks impact the imputation process. To this end, we conducted experiments using four popular evasion and poisoning attacks strategies across 29 real-world datasets, including the NSL-KDD and Edge-IIoT datasets, which were used as case study. For the adversarial attack strategies, we employed the Fast Gradient Sign Method, Carlini &amp; Wagner, Project Gradient Descent, and Poison Attack against Support Vector Machine algorithm. Also, four state-of-the-art imputation strategies were tested under Missing Not At Random, Missing Completely at Random, and Missing At Random mechanisms using three missing rates (5%, 20%, 40%). We assessed imputation quality using MAE, while data distribution shifts were analyzed with the Kolmogorov–Smirnov and Chi-square tests. Furthermore, we measured classification performance by training an XGBoost classifier on the imputed datasets, using F1-score, Accuracy, and AUC. To deepen our analysis, we also incorporated six complexity metrics to characterize how adversarial attacks and imputation strategies impact dataset complexity. Our findings demonstrate that adversarial attacks significantly impact the imputation process. In terms of imputation assessment in what concerns to quality error, the scenario that enrolees imputation with Project Gradient Descent attack proved to be more robust in comparison to other adversarial methods. Regarding data distribution error, results from the Kolmogorov–Smirnov test indicate that in the context of numerical features, all imputation strategies differ from the baseline (without missing data) however for the categorical context Chi-Squared test proved no difference between imputation and the baseline.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"157 ","pages":"Article 104574"},"PeriodicalIF":4.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}