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25 December 2023, Volume 9 Issue 6
Comprehensive Review
Review of cryptographic application security evaluation techniques for new critical infrastructures
Gaolei LI, Jianhua LI, Zhihong ZHOU, Hao ZHANG
2023, 9(6):  1-19.  doi:10.11959/j.issn.2096-109x.2023079
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The construction of new critical infrastructure, represented by high-speed full-time signal coverage, intelligent and fine-grained urban management, and deep space and deep sea scientific innovation experimental fields, has entered a new stage with the deep integration and development of new technologies such as 5G/6G, artificial intelligence, and blockchain in various fields.The security evaluation of cryptography applications, as a key technological resource for ensuring the security of national information, integration, and innovation infrastructure, has risen to the level of international law and national development strategy.It is urgent to construct a comprehensive, fine-grained, and self-evolving cryptography security evaluation system throughout the data lifecycle.The typical APT attacks and ransomware attacks faced by new critical infrastructure in industries such as energy, medicine, and transportation in recent years were considered.And then the growing demand for security evaluation of cryptography applications was analyzed in the face of new business requirements such as preventing endogenous data security risks, achieving differentiated privacy protection, and supporting authenticated attack traceability.The new challenges were also examined, which were brought by new information infrastructure (including big data, 5G communication, fundamental software, etc.), integration infrastructure (including intelligent connected vehicles, intelligent connected industrial control systems, etc.), and innovation infrastructure (including big data, artificial intelligence, blockchain, etc.) to the security evaluation of cryptography applications.Furthermore, the new requirements were revealed about domestically produced cryptography algorithms and protocols deployed on high-performance computing chips, ultra-high-speed communication modules, and large-capacity storage media for cryptography application security evaluation technology.Finally, the development of automated and intelligent cryptography application security evaluation technology was explored.

Papers
Efficient and safe software defined network topology discovery protocol
Dong LI, Junqing YU, Yongpu GU, Pengcheng ZHAO
2023, 9(6):  20-33.  doi:10.11959/j.issn.2096-109x.2023080
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The network topology discovery in OpenFlow-based software-defined networks is mainly achieved by utilizing the OpenFlow discovery protocol (OFDP).However, it has been observed in existing research that OFDP exhibits low updating efficiency and is susceptible to network topology pollution attacks.To address the efficiency and safety concerns of the network topology discovery protocol, an in-depth investigation was conducted on the mechanism and safety of OFDP network topology discovery.The characteristics of network topology establishment and updating in OFDP were analyzed, and an improved protocol named Im-OFDP (improved OpenFlow discovery protocol) based on the minimum vertex covering problem in graph theory was proposed.In Im-OFDP, the switch port table and network link table were initially established using prior information obtained from OFDP network topology discovery.Subsequently, a graph model of the network topology was constructed, and the minimum vertex covering algorithm in graph theory was employed to select specific switches for the reception and forwarding of topology discovery link layer discovery protocol (LLDP) packets.Multi-level flow tables were designed based on the network topology structure, and these flow entries were installed on the selected switches by the controller to process LLDP packets.To address security issues, dynamic check code verification in LLDP packets was employed to ensure the safety of network links.Additionally, a network equipment information maintenance mechanism was established based on known network topologies to ensure the safety of the network equipment.Experimental results demonstrate a significant reduction in the number of network topology discovery messages, bandwidth overhead, and CPU overhead through the deployment of Im-OFDP.Moreover, the response time for node failures and link recovery time after mode failure is substantially reduced.Im-OFDP also effectively mitigates various network topology pollution attacks, such as link fabrication and switch forgery attacks.Overall, Im-OFDP has the capability to enhance the efficiency and safety of SDN topology discovery.

Reversible data hiding algorithm in encrypted images based on prediction error and bitplane coding
Haiyong WANG, Mengning JI
2023, 9(6):  34-45.  doi:10.11959/j.issn.2096-109x.2023081
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With the increasing use of cloud backup methods for storing important files, the demand for privacy protection has also grown.Reversible data hiding in encrypted images (RDHEI) is an important technology in the field of information security that allows embedding secret information in encrypted images while ensuring error-free extraction of the secret information and lossless recovery of the original plaintext image.This technology not only enhances image security but also enables efficient transmission of sensitive information over networks.Its application in cloud environments for user privacy protection has attracted significant attention from researchers.A reversible data hiding method in encrypted images based on prediction error and bitplane coding was proposed to improve the embedding rate of existing RDHEI algorithms.Different encoding methods were employed by the algorithm depending on the distribution of the bitplanes, resulting in the creation of additional space in the image.The image was rearranged to allocate the freed-up space to the lower-order planes.Following this, a random matrix was generated using a key to encrypt the image, ensuring image security.Finally, the information was embedded into the reserved space.The information can be extracted and the image recovered by the receiver using different keys.The proposed algorithm achieves a higher embedding rate compared to five state-of-the-art RDHEI algorithms.The average embedding rates on BOWS-2, BOSSBase, and UCID datasets are 3.769 bit/pixel, 3.874 bit/pixel, and 3.148 bit/pixel respectively, which represent an improvement of 12.5%, 6.9% and 8.6% compared to the best-performing algorithms in the same category.Experimental results demonstrate that the proposed algorithm effectively utilizes the redundancy of images and significantly improves the embedding rate.

High speed national secret SM4 optical fiber communication system scheme based on FPGA
Peiyu HUANG, Jiabo SONG, Yangfan JIA
2023, 9(6):  46-55.  doi:10.11959/j.issn.2096-109x.2023082
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With the increasing use of optical fiber communication technology in the Industrial Internet of Things, cryptographic algorithms play a crucial role in ensuring the security of data transmission in embedded device environments.The SM4 packet cipher algorithm, developed independently in our country, is widely applied to wireless LAN and Internet of Things data encryption.However, the software-based encryption and decryption processes are relatively slow, which hampers their application in scenarios requiring high real-time performance, especially for embedded devices.To address this issue, a high-performance and secure optical fiber communication system was designed based on the FPGA platform and the SM4 algorithm.The FPGA was used to implement the MAC layer interface for SM4 algorithm encryption and decryption, as well as data transmission.Besides, an optimization scheme for the hardware implementation architecture of the SM4 algorithm was proposed.The critical path was shortened by employing a pipeline method, thereby improving the system clock frequency.Additionally, parallel processing of S-box transformation was accelerated to enable efficient data replacement.To reduce data reading delays, a dual-cache processing method was combined to facilitate easier processing of cache data and significantly reduce packet loss rates.This scheme greatly enhanced system data throughput.Experimental results demonstrate that compared to similar designs, the throughput of the SM4 algorithm encryption and decryption module in this scheme reaches up to 25.6 Gbit/s, with minimal differences in resource consumption.Due to limitations imposed by the 10-gigabit SFP+ optical module, the throughput of the entire optical fiber communication system reaches 9.4 Gbit/s.For 128-bit data, the average encryption speed is 0.47 μs/bit and the average decryption speed is 0.28 μs/bit, which can be applied to a variety of secure communication scenarios in the internet of things.

Hybrid-chain-based supervision scheme for privacypreserving trading system
Xuedan JIA, Longxia HUANG, Pujie JING, Liangmin WANG, Xiangmei SONG
2023, 9(6):  56-70.  doi:10.11959/j.issn.2096-109x.2023083
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Due to its characteristics of decentralization, transparency, and security, blockchain technology is widely used in finance, the Internet of Things, and supply chain.However, along with the opportunities it brings, the application of blockchain technology also presents new challenges.On one hand, traditional centralized regulatory methods can undermine the decentralized and autonomous nature of blockchain, creating regulatory dilemmas.On the other hand, blockchain-based systems require privacy protection as transactions often contain sensitive information beyond currency amounts.Failure to protect privacy can result in information leakage and make it difficult for authorities to monitor transactions.To address these challenges and enable regulation in privacy-preserving blockchain-based trading systems, a hybrid chain framework was proposed.This framework incorporated a multiple committee-based approach for general cross-chain regulation.Separate committees were established for the supervision chain and service chain.The service chain committee handled service chain data, generating and sending proofs to the supervision chain to achieve private supervision.In addition to single-chain regulation, cross-chain communication committees and supervision committees collaborated to achieve cross-chain transaction supervision among different service chains.Through a security analysis, it is proven that the proposed scheme is secure when each module meets its security requirements.Experimental evaluations have been conducted to demonstrate the feasibility of the proposed scheme, showing that it achieves cross-chain supervision at a low cost and is scalable.

Adaptive IP geolocation framework for target network scenarios
Shuodi ZU, Shichang DING, Fuxiang YUAN, Xiangyang LUO
2023, 9(6):  71-85.  doi:10.11959/j.issn.2096-109x.2023084
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The target location can be determined through IP geolocation, serving as a vital foundation for location-based services.Researchers have proposed various IP geolocation algorithms with different implementation principles to cater to different network scenarios.However, maintaining an ideal geolocation effect proves challenging for these algorithms in diverse network scenarios.Three typical implementation principles for IP geolocation based on network measurement were introduced.The advantages and disadvantages of these methods in various network scenarios were analyzed, and an adaptive IP geolocation framework specifically tailored to target network scenarios was proposed.The geolocation framework was functioned as follows: initially, a preliminary city-level location estimation was obtained by comparing the target’s location with the landmark database.Then, detection sources were deployed in a distributed manner, and information such as delay, topology and same subnet landmarks for the target city was gathered to determine the network scenario.Finally, an appropriate geolocation method was employed to accurately estimate the target’s location according to the identified network scenario.Through simulation geolocation experiments in 11 cities of China, the multi-level performance of various IP geolocation methods supported by landmark data of different quantities and distributions was evaluated.The results indicate that the proposed framework achieves a city-level geolocation success rate of 96.16% and a median error of 4.13 km for street-level geolocation.Moreover, the framework demonstrates stable geolocation performance across different conditions, including varying same subnet landmark numbers and target accessibility.These experimental findings validate the effectiveness of the proposed framework and offer novel insights for IP geolocation research.

Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs
Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN
2023, 9(6):  86-101.  doi:10.11959/j.issn.2096-109x.2023085
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In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.

Evolutionary game model of information management mechanism for public opinion governance
Shuting LIU, Yinghua MA, Xiuzhen CHEN
2023, 9(6):  102-115.  doi:10.11959/j.issn.2096-109x.2023086
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Conflicting information in online social networks, often stemming from opposing views, can lead to rumors and the fragmentation of society.A reputation-based dynamic information management mechanism was proposed, adopting a dynamic reward and punishment strategy to decrease unnecessary information conflict so as to activate self-purification of network.By utilizing evolutionary game theory, a cumulative-prospect-based evolutionary game model of social network information conflict was constructed to validate the efficacy of the proposed mechanism.Numerical simulations were conducted to analyze the evolutionary trends of 18 different theoretical application scenarios under 9 constraints.The results demonstrate that, under constraint 5, both sides of the game tend to exhibit relatively rational behavior towards conflicts.Furthermore, dynamic evolution analysis and sensitivity analysis of the model parameters were performed while satisfying constraint 5, providing practical strategic suggestions for the dynamic information management mechanism.This model represents a novel attempt to capture the interaction of multiple factors, such as interest-driven behavior and conformity psychology, in the evolution of public opinion.Moreover, the model successfully simulates well-known patterns of rumor dissemination, confirming its validity.The proposed model offers valuable insights for the development and design of malicious information control systems in social networks and provides new ideas for the research and implementation of public opinion governance.

Data recommendation algorithm of network security event based on knowledge graph
Xianwei ZHU, Wei LIU, Zihao LIU, Zeyu GU
2023, 9(6):  116-126.  doi:10.11959/j.issn.2096-109x.2023087
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To address the difficulty faced by network security operation and maintenance personnel in timely and accurate identification of required data during network security event analysis, a recommendation algorithm based on a knowledge graph for network security events was proposed.The algorithm utilized the network threat framework ATT&CK to construct an ontology model and establish a network threat knowledge graph based on this model.It extracted relevant security data such as attack techniques, vulnerabilities, and defense measures into interconnected security knowledge within the knowledge graph.Entity data was extracted based on the knowledge graph, and entity vectors were obtained using the TransH algorithm.These entity vectors were then used to calculate data similarity between entities in network threat data.Disposal behaviors were extracted from literature on network security event handling and treated as network security data entities.A disposal behavior matrix was constructed, and the behavior matrix enabled the vector representation of network threat data.The similarity of network threat data entities was calculated based on disposal behaviors.Finally, the similarity between network threat data and threat data under network security event handling behavior was fused to generate a data recommendation list for network security events, which established correlations between network threat domains based on user behavior.Experimental results demonstrate that the algorithm performs optimally when the fusion weight α=7 and the recommended data volume K=5, achieving a recall rate of 62.37% and an accuracy rate of 68.23%.By incorporating disposition behavior similarity in addition to data similarity, the algorithm better represents factual disposition behavior.Compared to other algorithms, this algorithm exhibits significant advantages in recall rate and accuracy, particularly when the recommended data volume is less than 10.

GDPR-oriented intelligent checking method of privacy policies compliance
Xin LI, Peng TANG, Xiheng ZHANG, Weidong QIU, Hong HUI
2023, 9(6):  127-139.  doi:10.11959/j.issn.2096-109x.2023088
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The implementation of the EU’s General Data Protection Regulation (GDPR) has resulted in the imposition of over 300 fines since its inception in 2018.These fines include significant penalties for prominent companies like Google, which were penalized for their failure to provide transparent and comprehensible privacy policies.The GDPR, known as the strictest data protection laws in history, has made companies worldwide more cautious when offering cross-border services, particularly to the European Union.The regulation's territorial scope stipulates that it applies to any company providing services to EU citizens, irrespective of their location.This implies that companies worldwide, including domestic enterprises, are required to ensure compliance with GDPR in their privacy policies, especially those involved in international operations.To meet this requirement, an intelligent detection method was introduced.Machine learning and automation technologies were utilized to automatically extract privacy policies from online service companies.The policies were converted into a standardized format with a hierarchical structure.Through natural language processing, the privacy policies were classified, allowing for the identification of relevant GDPR concepts.In addition, a constructed GDPR taxonomy was used in the detection mechanism to identify any missing concepts as required by GDPR.This approach facilitated intelligent detection of GDPR-oriented privacy policy compliance, providing support to domestic enterprises while they provided cross-border services to EU users.Analysis of the corpus samples reveals the current situation that mainstream online service companies generally fail to meet GDPR compliance requirements.

Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example
Hu LI, Yunfang CHEN, Wei ZHANG
2023, 9(6):  140-153.  doi:10.11959/j.issn.2096-109x.2023089
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Designed to enhance the privacy of user transactions, mixed coin technology has created disruptions to the address clustering rules typically used for cryptocurrency regulation.Consequently, hackers have begun utilizing mixed coin technology as a tool for money laundering and fund evasion, which has raised concerns among financial regulators regarding the detection of mixed coin transactions.Currently, most detection methods for mixed coin transactions rely on data analysis and statistics, lacking a comprehensive understanding of the internal workings of these transactions.This lack of knowledge may undermine the credibility and effectiveness of detection methods due to the absence of reliable verification data.CoinJoin, a decentralized mixed coin concept, represents one approach, and commercial implementations like Wasabi have gained popularity.Drawing from the characteristics of CoinJoin and its restriction on the size of anonymous transaction sets and mixed coin amounts, a general detection method for CoinJoin mixed coin transactions was devised.Such transactions typically involved multiple inputs and outputs, with more output items than UTXOs in the input, and a high occurrence of duplicate values among the output amounts.A basic detection method for Wasabi was developed by combining the generic detection method for CoinJoin with specific features of Wasabi, as identified in related studies, to complete the detection process.A trusted validation dataset was acquired from the Wasabi platform service interface, and this dataset was analyzed to achieve two objectives.First, the alignment of rule parameters in the Wasabi base detection method was accomplished.Second, a new metric was proposed, measuring the ratio of the highest frequency of duplicate values in the output amount of transactions to the number of UTXOs in the input.This metric assessed the level of user participation in mixed coin transactions, providing a measure of user freedom.Using these two approaches, significant progress is made in the detection of mixed coin transactions.The experiments show that the recall rate of Wasabi’s basic detection method is 94.2% and the accuracy rate is 67.2%.After the analytical feedback from the credible validation dataset, the recall rate of the improved detection method reaches 100% and the accuracy rate is above 99%.The total market size of the entire CoinJoin type of mixed coin transactions was evaluated and predicted based on a common test methodology.It is concluded that the number of CoinJoin mixed coin transactions in today’s mixed coin market represents 1.9 per 1 000 of all Bitcoin transactions and 3.7 per 1 000 of the transaction value at most.

Visual explanation method for reversible neural networks
Xinying MU, Bingbing SONG, Fanxiao LI, Yisen ZHENG, Wei ZHOU, Yunyun DONG
2023, 9(6):  154-165.  doi:10.11959/j.issn.2096-109x.2023090
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The issue of model explainability has gained significant attention in understanding the vulnerabilities and anonymous decision-making processes inherent in deep neural networks (DNN).While there has been considerable research on explainability for traditional DNN, there is a lack of exploration on the operation mechanism and explainability of reversible neural networks (RevNN).Additionally, the existing explanation methods for traditional DNN are not suitable for RevNN and suffer from issues such as excessive noise and gradient saturation.To address these limitations, a visual explanation method called visual explanation method for reversible neural network (VERN) was proposed for RevNN.VERN leverages the reversible property of RevNN and is based on the class-activation mapping mechanism.The correspondence between the feature map and the input image was explored by VERN, allowing for the mapping of classification weights of regional feature maps to the corresponding regions of the input image.The importance of each region for model decision-making was revealed through this process, which generates a basis for model decision-making.Experimental comparisons with other explanation methods on generalized datasets demonstrate that VERN achieves a more focused visual effect, surpassing suboptimal methods with up to 7.80% improvement in average drop (AD) metrics and up to 6.05% improvement in average increase (AI) metrics in recognition tasks.VERN also exhibits an 82.00% level of localization for the maximum point of the heat value.Furthermore, VERN can be applied to explain traditional DNN and exhibits good scalability, improving the performance of other methods in explaining RevNN.Furthermore, through adversarial attack analysis experiments, it is observed that adversarial attacks alter the decision basis of the model.This is reflected in the misalignment of the model’s attention regions, thereby aiding in the exploration of the operation mechanism of adversarial attacks.

Autonomous security analysis and penetration testing model based on attack graph and deep Q-learning network
Cheng FAN, Guoqing HU, Taojie DING, Zhanhua ZHANG
2023, 9(6):  166-175.  doi:10.11959/j.issn.2096-109x.2023091
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With the continuous development and widespread application of network technology, network security issues have become increasingly prominent.Penetration testing has emerged as an important method for assessing and enhancing network security.However, traditional manual penetration testing methods suffer from inefficiency,human error, and tester skills, leading to high uncertainty and poor evaluation results.To address these challenges, an autonomous security analysis and penetration testing framework called ASAPT was proposed, based on attack graphs and deep Q-learning networks (DQN).The ASAPT framework was consisted of two main components:training data construction and model training.In the training data construction phase, attack graphs were utilized to model the threats in the target network by representing vulnerabilities and possible attacker attack paths as nodes and edges.By integrating the common vulnerability scoring system (CVSS) vulnerability database, a “state-action”transition matrix was constructed, which depicted the attacker’s behavior and transition probabilities in different states.This matrix comprehensively captured the attacker’s capabilities and network security status.To reduce computational complexity, a depth-first search (DFS) algorithm was innovatively applied to simplify the transition matrix, identifying and preserving all attack paths that lead to the final goal for subsequent model training.In the model training phase, a deep reinforcement learning algorithm based on DQN was employed to determine the optimal attack path during penetration testing.The algorithm interacted continuously with the environment, updating the Q-value function to progressively optimize the selection of attack paths.Simulation results demonstrate that ASAPT achieves an accuracy of 84% in identifying the optimal path and exhibits fast convergence speed.Compared to traditional Q-learning, ASAPT demonstrates superior adaptability in dealing with large-scale network environments, which could provide guidance for practical penetration testing.

Chinese Journal of Network and Information Security. 2017 Vol. 3 (3): 71-77 doi: 10.11959/j.issn.2096-109x.2017.00157
Abstract6070)   HTML88)    PDF (169KB)(62276)    Knowledge map   
Survey of block chain
Xin SHEN,Qing-qi PEI,Xue-feng LIU
Chinese Journal of Network and Information Security. 2016 Vol. 2 (11): 11-20 doi: 10.11959/j.issn.2096-109x.2016.00107
Abstract20659)   HTML2549)    PDF (461KB)(28092)    Knowledge map   
Study on BGP route leak
Jia JIA,Zhi-wei YAN,Guang-gang GENG,Jian JIN
Chinese Journal of Network and Information Security. 2016 Vol. 2 (8): 54-61 doi: 10.11959/j.issn.2096-109x.2016.00074
Abstract3242)   HTML129)    PDF (472KB)(20853)    Knowledge map   
Research on host malcode detection using machine learning
Dong ZHANG,Yao ZHANG,Gang LIU,Gui-xiang SONG
Chinese Journal of Network and Information Security. 2017 Vol. 3 (7): 25-32 doi: 10.11959/j.issn.2096-109x.2017.00179
Abstract2808)   HTML132)    PDF (648KB)(12247)    Knowledge map   
Supply chain dynamic multi-center coordination authentication model based on block chain
Jian-ming ZHU,Yong-gui FU
Chinese Journal of Network and Information Security. 2016 Vol. 2 (1): 27-33 doi: 10.11959/j.issn.2096-109x.2016.00019
Abstract6225)   HTML152)    PDF (1052KB)(11182)    Knowledge map   
Define cyberspace security
Binxing FANG
Chinese Journal of Network and Information Security. 2018 Vol. 4 (1): 1-5 doi: 10.11959/j.issn.2096-109x.2018002
Abstract4963)   HTML324)    PDF (456KB)(10675)    Knowledge map   
Research progress and trend of text summarization
Tuosiyu MING, Hongchang CHEN
Chinese Journal of Network and Information Security. 2018 Vol. 4 (6): 1-10 doi: 10.11959/j.issn.2096-109x.2018048
Abstract5685)   HTML92)    PDF (568KB)(10119)    Knowledge map   
Review of key technology and its application of blockchain
Feng ZHANG, Boxuan SHI, Wenbao JIANG
Chinese Journal of Network and Information Security. 2018 Vol. 4 (4): 22-29 doi: 10.11959/j.issn.2096-109x.2018028
Abstract5523)   HTML561)    PDF (690KB)(10036)    Knowledge map   
Machine learning security and privacy:a survey
Lei SONG, Chunguang MA, Guanghan DUAN
Chinese Journal of Network and Information Security. 2018 Vol. 4 (8): 1-11 doi: 10.11959/j.issn.2096-109x.2018067
Abstract6056)   HTML271)    PDF (701KB)(9939)    Knowledge map   
Analysis and enlightenment on the cybersecurity strategy of various countries in the world
Yu-xiao LI,Yong-jiang XIE
Chinese Journal of Network and Information Security. 2016 Vol. 2 (1): 1-5 doi: 10.11959/j.issn.2096-109x.2016.00017
Abstract2506)   HTML199)    PDF (374KB)(9295)    Knowledge map   
Research of phishing detection technology
Xi ZHANG,Zhi-wei YAN,Hong-tao LI,Guang-gang GENG
Chinese Journal of Network and Information Security. 2017 Vol. 3 (7): 7-24 doi: 10.11959/j.issn.2096-109x.2017.00180
Abstract2453)   HTML103)    PDF (870KB)(8793)    Knowledge map   
Symbolic execution based control flow graph extraction method for Android native codes
Hui-ying YAN,Zhen-ji ZHOU,Li-fa WU,Zheng HONG,He SUN
Chinese Journal of Network and Information Security. 2017 Vol. 3 (7): 33-46 doi: 10.11959/j.issn.2096-109x.2017.00178
Abstract1686)   HTML13)    PDF (619KB)(8213)    Knowledge map   
Survey of DDoS defense:challenges and directions
Fei CHEN,Xiao-hong BI,Jing-jing WANG,Yuan LIU
Chinese Journal of Network and Information Security. 2017 Vol. 3 (10): 16-24 doi: 10.11959/j.issn.2096-109x.2017.00202
Abstract2612)   HTML83)    PDF (555KB)(8194)    Knowledge map   
Data security and protection techniques in big data:a survey
Kai-min WEI,Jian WENG,Kui REN
Chinese Journal of Network and Information Security. 2016 Vol. 2 (4): 1-11 doi: 10.11959/j.issn.2096-109x.2016.00046
Abstract4029)   HTML128)    PDF (1446KB)(8096)    Knowledge map   
Machine learning algorithm for intelligent detection of WebShell
Hua DAI,Jing LI,Xin-dai LU,Xin SUN
Chinese Journal of Network and Information Security. 2017 Vol. 3 (4): 51-57 doi: 10.11959/j.issn.2096-109x.2017.00126
Abstract2801)   HTML54)    PDF (671KB)(8027)    Knowledge map   
Suggestions on cyber security talents cultivation
Hui LI,Ning ZHANG
Chinese Journal of Network and Information Security. 2015 Vol. 1 (1): 18-23 doi: 10.11959/j.issn.2096-109x.2015.00003
Abstract2213)   HTML62)    PDF (357KB)(7696)    Knowledge map   
Malware classification method based on static multiple-feature fusion
Bo-wen SUN,Yan-yi HUANG,Qiao-kun WEN,Bin TIAN,Peng WU,Qi LI
Chinese Journal of Network and Information Security. 2017 Vol. 3 (11): 68-76 doi: 10.11959/j.issn.2096-109x.2017.00217
Abstract1705)   HTML82)    PDF (529KB)(7545)    Knowledge map   
Analysis of cyberspace security based on game theory
Jian-ming ZHU,Qin WANG
Chinese Journal of Network and Information Security. 2015 Vol. 1 (1): 43-49 doi: 10.11959/j.issn.2096-109x.2015.00006
Abstract2176)   HTML70)    PDF (764KB)(7094)    Knowledge map   
Overview of searchable encryption research
Ying LI, Chunguang MA
Chinese Journal of Network and Information Security. 2018 Vol. 4 (7): 13-21 doi: 10.11959/j.issn.2096-109x.2018062
Abstract2089)   HTML119)    PDF (592KB)(6944)    Knowledge map   
Progress of research on privacy protection for data publication and data mining
Jiao WANG,Ke-feng FAN,Yong WANG
Chinese Journal of Network and Information Security. 2016 Vol. 2 (1): 18-26 doi: 10.11959/j.issn.2096-109x.2016.00021
Abstract1517)   HTML18)    PDF (965KB)(6779)    Knowledge map   
Survey of block chain
Xin SHEN,Qing-qi PEI,Xue-feng LIU
Chinese Journal of Network and Information Security. 2016 Vol. 2 (11): 11-20
doi: 10.11959/j.issn.2096-109x.2016.00107
Abstract( 20659 )   HTML PDF (461KB) (28092 Knowledge map   
Blockchain-based digital copyright trading system
Li LI,Siqin ZHOU,Qin LIU,Debiao HE
Chinese Journal of Network and Information Security. 2018 Vol. 4 (7): 22-29
doi: 10.11959/j.issn.2096-109x.2018060
Abstract( 8825 )   HTML PDF (771KB) (4051 Knowledge map   
Relation extraction based on CNN and Bi-LSTM
Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG
Chinese Journal of Network and Information Security. 2018 Vol. 4 (9): 44-51
doi: 10.11959/j.issn.2096-109x.2018074
Abstract( 8755 )   HTML PDF (618KB) (4223 Knowledge map   
Supply chain dynamic multi-center coordination authentication model based on block chain
Jian-ming ZHU,Yong-gui FU
Chinese Journal of Network and Information Security. 2016 Vol. 2 (1): 27-33
doi: 10.11959/j.issn.2096-109x.2016.00019
Abstract( 6225 )   HTML PDF (1052KB) (11182 Knowledge map   
Chinese Journal of Network and Information Security. 2017 Vol. 3 (3): 71-77
doi: 10.11959/j.issn.2096-109x.2017.00157
Abstract( 6070 )   HTML PDF (169KB) (62276 Knowledge map   
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Bimonthly, started in 2015
Authorized by:Ministry of Industry and Information Technology of the People's Republic of China
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Co-sponsored by:Xidian University, Beihang University, Huazhong University of Science and Technology, Zhejiang University
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ISSN 2096-109X
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