Journal of Network and Computer Applications最新文献

{"title":"CL-AP2: A composite learning approach to attack prediction via attack portraying","authors":"Yingze Liu,&nbsp;Yuanbo Guo","doi":"10.1016/j.jnca.2024.103963","DOIUrl":"10.1016/j.jnca.2024.103963","url":null,"abstract":"<div><p>The capabilities of accurate prediction of cyberattacks have long been desired as detection methods cannot avoid the damages caused by occurrences of cyberattack. Attack prediction still remains an open issue especially to specify the upcoming steps of an attack with the quickly evolving intelligent techniques at the attackers’ side. This study proposes a composite learning approach (namely CL-AP<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>), which fulfills this task in two phases of “attack portraying” and “attack prediction”: (1) (Attack Portraying) CL-AP<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> generates a Temporal Attack Knowledge Graph (TAKG) from real-time system logs providing full knowledge that formulates time-aware entities related to attacks and the relations amongst them; Over the TAKG, a Tactic-based Cyber Kill Chain (TCKC) model highlights the attacker’s <em>portrait</em> via evaluation of behaviors in the past, <em>i.e.</em>, presenting the tactical path and attack steps taken by the attacker; (2) (Attack Prediction) The Soft Actor–Critic algorithm applies to identify the most possible attack trajectory confined in the attack portrait; The transformer model finally derives the specific attack technique to be taken next.</p><p>Experiments have been performed versus the state-of-the-art counterparts over a public dataset and results indicate that: (1) CL-AP<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> can effectively reveal the tactical path taken by the attacker and form a complete portrait of the attack; and (2) CL-AP<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> excels in predicting attack techniques to be taken by attackers and providing the defense guidance against the predicted attacks.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103963"},"PeriodicalIF":7.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638146","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":"Attenuating majority attack class bias using hybrid deep learning based IDS framework","authors":"K.G. Raghavendra Narayan ,&nbsp;Rakesh Ganesula ,&nbsp;Tamminaina Sai Somasekhar ,&nbsp;Srijanee Mookherji ,&nbsp;Vanga Odelu ,&nbsp;Rajendra Prasath ,&nbsp;Alavalapati Goutham Reddy","doi":"10.1016/j.jnca.2024.103954","DOIUrl":"10.1016/j.jnca.2024.103954","url":null,"abstract":"<div><p>In real-time application domains, like finance, healthcare and defence, delay in service or stealing information may lead to unrecoverable consequences. So, early detection of intrusion is important to prevent security breaches. In recent days, anomaly-based intrusion detection using Hybrid Deep Learning approaches are becoming more popular. The most used benchmark datasets in the literature are NSL-KDD and UNSW-NB15, and these datasets are imbalanced. The models built on imbalanced datasets may lead to biased results towards majority classes by neglecting the minority class, even though they are equally important. In many cases, high accuracy is achieved for majority classes in the imbalanced datasets. But, the class-level performances are poor with respect to the minority class. The class balancing will also play an important role in attenuating the bias in prediction for imbalanced datasets. In this paper, a Hybrid Deep Learning Based Intrusion Detection (HDLBID) framework is proposed with CNN-BiLSTM combination. The four techniques, namely, Random Oversampling (ROS), ADASYN, SMOTE, and SMOTE-Tomek, are used for class balancing in the proposed HDLBID framework. The proposed HDLBID with SMOTE-Tomek achieves an overall accuracy of 99.6% with NSL-KDD and 89.02% for UNSW-NB15. It results in an improvement of 13.67% for NSL-KDD and 10.62% for UNSW-NB15 over the existing recent related models. In the proposed HDLBID, in addition to overall accuracy, the class-level <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score is also calculated. A comparative study is presented to show the effectiveness of balancing dataset compared to imbalanced dataset, and observed that the SMOTE-Tomek class balancing comparatively performed well. An improvement of 37.43% is observed in the U2R class of the NSL-KDD dataset and 61.65% improvement is seen in the Worms class of the UNSW-NB15 dataset, both with SMOTE-Tomek class balancing. Therefore, the proposed HDLBID with SMOTE-Tomek class balancing reports the best results in terms of overall accuracy compared to existing recent related approaches. Also, in terms of class-level analysis, HDLBID reports best results with SMOTE-Tomek over imbalanced version of datasets.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103954"},"PeriodicalIF":7.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623263","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":"Uncovering phishing attacks using principles of persuasion analysis","authors":"Lázaro Bustio-Martínez ,&nbsp;Vitali Herrera-Semenets ,&nbsp;Juan Luis García-Mendoza ,&nbsp;Miguel Ángel Álvarez-Carmona ,&nbsp;Jorge Ángel González-Ordiano ,&nbsp;Luis Zúñiga-Morales ,&nbsp;J. Emilio Quiróz-Ibarra ,&nbsp;Pedro Antonio Santander-Molina ,&nbsp;Jan van den Berg","doi":"10.1016/j.jnca.2024.103964","DOIUrl":"10.1016/j.jnca.2024.103964","url":null,"abstract":"<div><p>With the rising of Internet in early ’90s, many fraudulent activities have migrated from physical to digital: one of them is phishing. Phishing is a deceptive practice focused on exploiting the human factor, which is the most vulnerable aspect of any security process. In this scam, social engineering techniques are extensively utilized, specifically focusing on the principles of persuasion, to deceive individuals into disclosing sensitive information or engaging in malicious actions. This research explores the use of message subjectivity for detecting phishing attacks. It does so by assessing the impact of various data representations and classifiers on automatically identifying principles of persuasion. Furthermore, it investigates how these detected principles of persuasion can be leveraged for identifying phishing attacks. The experiments conducted revealed that there is no universal solution for data representation and classifier selection to effectively detect all principles of persuasion. Instead, a tailored combination of data representation and classifiers is required for detecting each principle. The Machine Learning models created automatically detect principles of persuasion with confidence levels ranging from 0.7306 to 0.8191 for AUC-ROC. Next, principles of persuasion detected are used for phishing detection. This study also emphasizes the need for user-friendly and comprehensible models. To validate the proposal presented, several families of classifiers were tested, but among all of them, tree-based models (and Random Forest in particular) stand out as preferred option. These models achieve similar level of effectiveness as alternative methods while offering improved clarity and user-friendliness, with an AUC-ROC of 0.859842.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103964"},"PeriodicalIF":7.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566472","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":"On designing a profitable system model to harmonize the tripartite dissension in content delivery applications","authors":"Libin Yang ,&nbsp;Wei Lou","doi":"10.1016/j.jnca.2024.103965","DOIUrl":"10.1016/j.jnca.2024.103965","url":null,"abstract":"<div><p>The popularity of commercial content delivery applications has led to dissension among three embroiled parties: Content Service Providers (CSPs), Internet Service Providers (ISPs), and End Users (EUs). This dissension is not only a technical problem but an economic problem. To harmonize this dissension, this paper takes live streaming as a typical content delivery application. It proposes a profitable system model that enables all three parties to enlarge their benefits with the help of a prevalent content delivery architecture integrated with edge caching and traffic engineering technologies. Specifically, the interactions among CSPs, ISPs, and EUs are modeled as a tripartite game where more and more ISPs and CSPs are involved in the market. A pricing scheme is introduced to capture the application features. The tripartite game is studied in different market scenarios and a dynamic three-stage Stackelberg game is proposed that is combined with the Cournot game that characterizes the interdependent, interactive, and competitive relationship among the three parties. Moreover, how the market competition motivates ISPs to upgrade the cache service infrastructure is further investigated. The theoretical analysis and empirical study show that the model can result in a win-win-win outcome.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103965"},"PeriodicalIF":7.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638145","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":"Satellite synergy: Navigating resource allocation and energy efficiency in IoT networks","authors":"Muhammad Abdullah ,&nbsp;Humayun Zubair Khan ,&nbsp;Umair Fakhar ,&nbsp;Ahmad Naeem Akhtar ,&nbsp;Shuja Ansari","doi":"10.1016/j.jnca.2024.103966","DOIUrl":"10.1016/j.jnca.2024.103966","url":null,"abstract":"<div><p>Satellite-assisted internet of things (IoT) networks have emerged as a beacon of promise, offering global coverage and uninterrupted connectivity. However, the challenges of resource allocation and task offloading in such networks are intricate due to the unique characteristics of satellite communication systems. This research’s findings enrich the landscape of energy-efficient and dependable satellite-assisted IoT networks. The paper navigates the delicate balance between energy efficiency, network throughput, and fairness in distributing resources among IoT devices. The proposed techniques, notably the Outer Approximation Algorithm (OAA), usher in seamless connectivity and resource optimization. The central challenge at hand, a concave fractional programming problem, transforms through the Charnes–Cooper transformation, presenting as a concave optimization enigma. Herein, the proposed outer approximation algorithm takes flight, navigating the intricate paths of concave optimization. The performance of the epsilon-optimal solution faces scrutiny under diverse system parameters—the constellation of IoT devices, their affiliations, fairness considerations, and the equitable distribution of resource blocks. This contribution not only enriches research but also opens doors to the boundless possibilities of satellite-assisted IoT networks.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103966"},"PeriodicalIF":7.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1084804524001437/pdfft?md5=6e5bd16c957bd71a21dca1e3c0884dd5&pid=1-s2.0-S1084804524001437-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive survey","authors":"Jong Wook Kim ,&nbsp;Beakcheol Jang","doi":"10.1016/j.jnca.2024.103951","DOIUrl":"https://doi.org/10.1016/j.jnca.2024.103951","url":null,"abstract":"<div><p>The generation of trajectory data has increased dramatically with the advent and widespread use of GPS-enabled devices. This rich source of data provides invaluable insights for various applications such as traffic optimization, urban planning, crowd management, and public safety. However, the increasing demand for the publication and sharing of trajectory data for big data analytics raises significant privacy concerns due to the sensitive nature of the location information embedded in the trajectory data. Privacy-preserving trajectory publishing (PPTP) has been an active research area to address these concerns, and synthetic trajectory generation has emerged as a promising direction within PPTP. This survey paper provides a comprehensive overview of PPTP with a focus on synthetic trajectory generation methods, which have been insufficiently covered in previous surveys. Our contributions include a comparison of existing PPTP techniques based on their applicability and effectiveness for data analysis tasks. We then review and discuss the existing work on synthetic trajectory generation in the context of PPTP. Specifically, we classify the existing studies into two main categories, algorithm-based and deep learning-based approaches, and within each category, we perform a comparative analysis of the studied methods, focusing on their different characteristics. Finally, in order to encourage further research in this area, we identify and highlight a number of promising directions for future investigation that deserve to be explored in greater depth.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103951"},"PeriodicalIF":7.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604755","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":"A Contextual Multi-Armed Bandit approach for NDN forwarding","authors":"Yakoub Mordjana,&nbsp;Badis Djamaa,&nbsp;Mustapha Reda Senouci,&nbsp;Aymen Herzallah","doi":"10.1016/j.jnca.2024.103952","DOIUrl":"10.1016/j.jnca.2024.103952","url":null,"abstract":"<div><p>Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily growing, offering the potential to deliver intelligent, adaptable, and reliable NDN forwarding algorithms. In this context, this paper proposes efficient NDN forwarding strategies based on Contextual Multi-Armed Bandit (CMAB). Initially, we employ CMAB to address the challenge of forwarding Interest packets and introduce a new CMAB model tailored for NDN, dubbed CMAB4NDN. Subsequently, we construct the CMAB context using information derived from the content name and the network congestion state, which are then fed into the CMAB4NDN learning algorithm to decide on the best forwarding action. Further, we develop three CMAB strategies, namely Lin-<span><math><mi>ɛ</mi></math></span>-Greedy, Linear Upper Confidence Bound, and Contextual Thompson Sampling, and deploy them within our proposal. CMAB4NDN was implemented in ndnSIM, thoroughly evaluated, and compared with multiple state-of-the-art NDN forwarding algorithms across various scenarios. The obtained results confirm the relevance and superiority of our approach in terms of delay, throughput, and packet loss.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103952"},"PeriodicalIF":7.7,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566467","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":"JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks","authors":"Ala Altaweel,&nbsp;Sidra Aslam,&nbsp;Ibrahim Kamel","doi":"10.1016/j.jnca.2024.103953","DOIUrl":"https://doi.org/10.1016/j.jnca.2024.103953","url":null,"abstract":"<div><p>This paper first shows that Prophet, Spray and Wait, Epidemic, and First Contact routing protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the Jamhole attack. In Jamhole attack, an attacker, Eve, compromises two different locations in OMNs by (i) jamming the GPS signal of victim nodes in these locations and (ii) by establishing a pair-wise hidden wormhole tunnel among these locations to route packets and to achieve high packet delivery ratio. The Jamhole attack enables Eve to disrupt the routing, obtain more packets of victim nodes, and possibly launch more severe attacks like packet modification, packet dropping, and packet injection attacks.</p><p>In this paper, the impact of Jamhole attack on OMNs routing protocols is investigated using different attack parameters (i.e., area of compromised locations, attack frequency, and attack duration). To identify the Jamhole attack, this paper proposes JamholeHunter, a detection protocol that employs nodes’ wireless ranges, velocities, and last available GPS locations. The paper measured the impact of Jamhole attack and evaluated the JamholeHunter technique through extensive simulation experiments using synthetic and real-world mobility traces. The results demonstrate that (i) the Jamhole attack can cause a serious impact on the OMNs routing protocols, (ii) the effectiveness of JamholeHunter in identifying Jamhole attack with Detection Rate (75% to 100%) depending on various attack parameters with <span><math><mo>∼</mo></math></span>95% Accuracy, and low False Positive Rate (<span><math><mo>≤</mo></math></span> 3.7%), and (iii) the reliability of JamholeHunter in real-world scenarios of OMNs under different attack parameters, mobility models, and nodes velocity.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103953"},"PeriodicalIF":7.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543346","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":"Multi-UAV aided energy-aware transmissions in mmWave communication network: Action-branching QMIX network","authors":"Quang Tuan Do ,&nbsp;Thien Duc Hua ,&nbsp;Anh-Tien Tran ,&nbsp;Dongwook Won ,&nbsp;Geeranuch Woraphonbenjakul ,&nbsp;Wonjong Noh ,&nbsp;Sungrae Cho","doi":"10.1016/j.jnca.2024.103948","DOIUrl":"https://doi.org/10.1016/j.jnca.2024.103948","url":null,"abstract":"<div><p>Advancements in drone technology and high-frequency millimeter-wave communications are transforming unmanned-aerial-vehicles (UAV)-aided networks, expanding their potential across diverse applications. Despite the advantages of broad frequency bandwidth and enhanced line of sight connectivity in the UAV-aided millimeter-wave networks, it is challenging to provide high network performance because of the inherent limitations of limited UAV energy and millimeter-wave’s large path loss. This challenge becomes more important in dynamically changing multi-UAV environments. To address this challenge in multi-UAV networks, we propose a novel approach based on multi-agent deep reinforcement learning called action-branching QMIX. Our method determines nearly optimal codebook-based discrete beamforming vectors and UAV trajectories while maintaining a balance between communication efficiency and energy consumption. The proposed approach employs a new Long Short-Term Memory module to control long sequences effectively and enables it to adapt to changing environmental variables in real-time. We thoroughly evaluate the proposed control with a real-world measurement-based channel model. The evaluation confirms that the proposed control converges stably and consistently, and provides enhanced performance in terms of downlink data rate, success rate of reaching the destination, and service duration when compared to traditional benchmark multi-agent reinforcement learning schemes. These results emphasize the enhanced energy sustainability, robustness, and stability of the proposed approach in dynamically changing multi-UAV environments when compared to the existing benchmark algorithms.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103948"},"PeriodicalIF":7.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604880","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":"Leveraging application permissions and network traffic attributes for Android ransomware detection","authors":"Sekione Reward Jeremiah ,&nbsp;Haotian Chen ,&nbsp;Stefanos Gritzalis ,&nbsp;Jong Hyuk Park","doi":"10.1016/j.jnca.2024.103950","DOIUrl":"https://doi.org/10.1016/j.jnca.2024.103950","url":null,"abstract":"<div><p>The increase in ransomware threats targeting Android devices necessitates the development of advanced techniques to strengthen the effectiveness of detection and prevention methods. Existing studies use Machine Learning (ML) techniques to detect and classify ransomware attacks, however, the ransomware landscape's rapid evolution hinders the effectiveness of these approaches. Moreover, the potential of Deep Reinforcement Learning (DRL) for this purpose remains unexplored. This study investigates the application of various DRL models for Android ransomware detection, leveraging permissions and network traffic attributes-labeled datasets. The paper provides a detailed explanation of implementing supervised learning within a DRL context. Secondly, the challenge of devising a reward function in Android ransomware detection is addressed, given the lack of an automated method for Android ransomware identification. The conventional DRL framework, which relies on the agent's interaction with a real-time environment, is conceptually modified in a new approach. We exhaustively tested the efficiency and accuracy of DRL-based models against other ML techniques, and results show that the A2C model has a better comparable detection performance than other DRL and ML models. Moreover, when DRL models are implemented with minor parameter modifications, they expedite and improve Android ransomware detection's speed, efficiency, and accuracy relative to existing ML strategies.</p></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"230 ","pages":"Article 103950"},"PeriodicalIF":7.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543348","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}