Journal of ICT Standardization最新文献

Application of Machine Learning Algorithms in User Behavior Analysis and a Personalized Recommendation System in the Media Industry 机器学习算法在用户行为分析和媒体行业个性化推荐系统中的应用

Journal of ICT Standardization Pub Date : 2025-03-01 DOI: 10.13052/jicts2245-800X.1313

Jialing Wang;Jun Zheng

{"title":"Application of Machine Learning Algorithms in User Behavior Analysis and a Personalized Recommendation System in the Media Industry","authors":"Jialing Wang;Jun Zheng","doi":"10.13052/jicts2245-800X.1313","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1313","url":null,"abstract":"Aimed at the multidimensional and nonlinear characteristics of user behavior in the media industry, this paper proposes an intelligent user modeling and recommendation framework (MUMA) based on hybrid machine learning. The system constructs a spatial-temporal dual-driven user characterization system by fusing heterogeneous data from multiple sources (clickstream, viewing duration, social graph, and eye-movement hotspot). The core technological breakthroughs include: (1) designing a dynamic interest-aware network (DIN) and adopting a hybrid LSTM-Transformer architecture with a time decay factor to capture short-term/long-term behavioral patterns; (2) developing a cross-domain migratory learning module based on a heterogeneous information network (HIN) to realize collaborative recommendation of news/video/advertising business; (3) innovatively combining reinforcement learning and causal inference to construct a bandit-propensity hybrid recommendation strategy, balancing the contradiction between exploration and development. At the system realization level, build a Flink+Redis realtime feature engineering pipeline to support millisecond update of thousands of dimensional features; deploy an XGBoost-LightGBM dual-engine ranking model to realize an interpretable recommendation by SHAP value. Experiments show that in the 800 million behavioral logs test of the head video platform, compared with traditional collaborative filtering methods, this scheme improves CTR by 29.7%, viewing completion by 18.3%, and coldstart user recommendation satisfaction by 82.5% (A/B test <tex>$P < 0.005$</tex>). This study provides new ideas for user behavior modeling in the media industry, as well as theoretical and practical references for the design and implementation of personalized recommendation systems.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 1","pages":"41-66"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11042905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural Language Processing: Classification of Web Texts Combined with Deep Learning 自然语言处理：结合深度学习的网络文本分类

Journal of ICT Standardization Pub Date : 2025-03-01 DOI: 10.13052/jicts2245-800X.1312

Chenwen Zhang

引用次数: 0

Validating Reliability and Security Requirements in Public Sector Infrastructure Built by Small Companies 验证小公司建设的公共部门基础设施的可靠性和安全性要求

Journal of ICT Standardization Pub Date : 2025-03-01 DOI: 10.13052/jicts2245-800X.1311

Roar E. Georgsen;Geir M. Køien

引用次数: 0

Deep Reinforcement Learning-Based Asymmetric Convolutional Autoencoder for Intrusion Detection 基于深度强化学习的非对称卷积自编码器入侵检测

Journal of ICT Standardization Pub Date : 2025-03-01 DOI: 10.13052/jicts2245-800X.1314

Yuqin Dai;Xinjie Qian;Chunmei Yang

{"title":"Deep Reinforcement Learning-Based Asymmetric Convolutional Autoencoder for Intrusion Detection","authors":"Yuqin Dai;Xinjie Qian;Chunmei Yang","doi":"10.13052/jicts2245-800X.1314","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1314","url":null,"abstract":"In recent years, intrusion detection systems (IDSs) have become a critical component of network security, due to the growing number and complexity of cyber-attacks. Traditional IDS methods, including signature-based and anomaly-based detection, often struggle with the high-dimensional and imbalanced nature of network traffic, leading to suboptimal performance. Moreover, many existing models fail to efficiently handle the diverse and complex attack types. In response to these challenges, we propose a novel deep learning-based IDS framework that leverages a deep asymmetric convolutional autoencoder (DACA) architecture. Our model combines advanced techniques for feature extraction, dimensionality reduction, and anomaly detection into a single cohesive framework. The DACA model is designed to effectively capture complex patterns and subtle anomalies in network traffic while significantly reducing computational complexity. By employing this architecture, we achieve superior detection accuracy across various types of attacks even in imbalanced datasets. Experimental results demonstrate that our approach surpasses several state-of-the-art methods, including HCM-SVM, D1-IDDS, and GNN -IDS, achieving high accuracy, precision, recall, and F1-score on benchmark datasets such as NSL-KDD and UNSW-NB15. The results emphasize how effectively our model identifies complex and varied attack patterns. In conclusion, the proposed IDS model offers a promising solution to the limitations of current detection systems, with significant improvements in performance and efficiency. This approach contributes to advancing the development of robust and scalable network security solutions.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"13 1","pages":"67-92"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11042904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Different Machine Learning Algorithms in the Mental Health Assessment of College Students 不同机器学习算法在大学生心理健康评估中的比较

Journal of ICT Standardization Pub Date : 2024-12-01 DOI: 10.13052/jicts2245-800X.1243

Yongsen Cai;Danling Lin;Qing Lu

{"title":"Comparison of Different Machine Learning Algorithms in the Mental Health Assessment of College Students","authors":"Yongsen Cai;Danling Lin;Qing Lu","doi":"10.13052/jicts2245-800X.1243","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1243","url":null,"abstract":"This paper assesses college students' mental health based on the symptom checklist 90 (SCL-90). In view of the assessment data processing and analysis, the performance of different machine learning algorithms, including random forest (RF), LightGBM3, extreme gradient boosting (XGBoost), in the classification of college students' mental health samples was compared. Moreover, the effect of different hyperparameter optimization methods (grid search, Bayesian optimization, and particle swarm optimization) was compared. The experiment on the SCL-90 assessment dataset found that the optimization effect of grid search was poor, and the highest F1 value and area under the curve (AUC) of the RF algorithm were 0.8914 and 0.9384, respectively, the highest F1 and AUC values of the XGBoost algorithm were 0.9166 and 0.9551, respectively. The LightGBM algorithm optimized by particle swarm optimization showed the best performance in the classification of mental health samples, with an F1 value of 0.9790 and an AUC of 0.9945. It also achieved optimal results when compared to machine learning algorithms such as naive Bayes and the support vector machines. The results prove the reliability and accuracy of the particle swarm optimization-improved Light-GBM algorithm in the analysis of college students' mental health assessment data. The algorithm can be applied in practice to provide an effective tool for the analysis of the mental health assessment data of college students.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"409-427"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Meta-Learning Approach for Few-Shot Network Intrusion Detection Using Depthwise Separable Convolution 基于深度可分离卷积的小样本网络入侵检测元学习方法

Journal of ICT Standardization Pub Date : 2024-12-01 DOI: 10.13052/jicts2245-800X.1245

Guo Li;MingHua Wang

{"title":"A Meta-Learning Approach for Few-Shot Network Intrusion Detection Using Depthwise Separable Convolution","authors":"Guo Li;MingHua Wang","doi":"10.13052/jicts2245-800X.1245","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1245","url":null,"abstract":"As cyberattacks become more frequent and sophisticated, network intrusion detection systems (IDS) play a critical role in safeguarding networks. However, traditional IDS models face challenges in detecting new, unseen attacks and typically require large volumes of labeled data for effective training. To address these issues, we propose a novel intrusion detection model based on meta-learning, integrating depthwise separable convolution (DSC). This model leverages few-shot learning to detect rare and emerging attack types with minimal labeled data. By using meta-learning, our model can rapidly adapt to new tasks, offering greater flexibility and scalability in various network scenarios. Experimental results on the CIC-DDoS2019 and CIC-IDS2017 datasets demonstrate that our model achieves competitive accuracy compared to state-of-the-art methods, even with fewer training samples. It also shows superior performance in terms of both detection accuracy and training efficiency, while being more resource-efficient, making it suitable for deployment in resource-constrained environments. In conclusion, our model offers a promising solution for network intrusion detection, enhancing the ability to detect new and emerging threats while ensuring computational efficiency for real-world applications.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"443-470"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study on the Translation of Spoken English from Speech to Text 英语口语从语篇到语篇的翻译研究

Journal of ICT Standardization Pub Date : 2024-12-01 DOI: 10.13052/jicts2245-800X.1244

Ying Zhang

{"title":"A Study on the Translation of Spoken English from Speech to Text","authors":"Ying Zhang","doi":"10.13052/jicts2245-800X.1244","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1244","url":null,"abstract":"Rapid translation of spoken English is conducive to international communication. This paper briefly introduces a convolutional neural network (CNN) algorithm for converting English speech to text and a long short-term memory (LSTM) algorithm for machine translation of English text. The two algorithms were combined for spoken English translation. Then, simulation experiments were performed by comparing the speech recognition performance among the CNN algorithm, the hidden Markov model, and the back-propagation neural network algorithm and comparing the machine translation performance with the LSTM algorithm and the recurrent neural network algorithm. Moreover, the performance of the spoken English translation algorithms combining different recognition algorithms was compared. The results showed that the CNN speech recognition algorithm, the LSTM machine translation algorithm and the combined spoken English translation algorithm had the best performance and sufficient anti-noise ability. In conclusion, utilizing a CNN for converting English speech to texts and LSTM for machine translation of the converted English text can effectively enhance the performance of translating spoken English.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"429-441"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Lenstra-Lenstra-Lovasz on Elliptic Curve Cryptosystem Using IOT Sensor Nodes Lenstra-Lenstra-Lovasz在物联网传感器节点椭圆曲线密码系统中的应用

Journal of ICT Standardization Pub Date : 2024-12-01 DOI: 10.13052/jicts2245-800X.1242

Md Sameeruddin Khan;Thomas M. Chen;Mithileysh Sathiyanarayanan;Mohammed Mujeerulla;S. Pravinth Raja

{"title":"Application of Lenstra-Lenstra-Lovasz on Elliptic Curve Cryptosystem Using IOT Sensor Nodes","authors":"Md Sameeruddin Khan;Thomas M. Chen;Mithileysh Sathiyanarayanan;Mohammed Mujeerulla;S. Pravinth Raja","doi":"10.13052/jicts2245-800X.1242","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1242","url":null,"abstract":"The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"381-407"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security Monitoring and Early Warning of Negative Public Opinion on Social Networks Under Deep Learning 基于深度学习的社交网络负面舆情安全监测与预警

Journal of ICT Standardization Pub Date : 2024-12-01 DOI: 10.13052/jicts2245-800X.1241

Haixiang He;Shiqi Ma

{"title":"Security Monitoring and Early Warning of Negative Public Opinion on Social Networks Under Deep Learning","authors":"Haixiang He;Shiqi Ma","doi":"10.13052/jicts2245-800X.1241","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1241","url":null,"abstract":"With the continuous development of social networks, negative social network public opinion appears frequently, which is particularly important for its safety monitoring and early warning. Taking Sina microblog as an example, this paper crawled texts from the platform, used BERT to generate word vectors, combined the bidirectional gated recurrent unit (BiGRU) and attention mechanism to design an emotion tendency classification method, and realized the classification of positive and negative emotion texts. Then, TCN was used to predict the negative emotion text to realize public opinion safety monitoring and early warning. It was found that BERT had the best performance. Compared with other deep learning methods, BERT-BiGRUA had a P value of 0.9431, an R value of 0.9012, and an F1 value of 0.9217 in the classification of emotion tendency, which were all the best. In the prediction of negative emotion text, TCN obtained a smaller mean square error and a higher <tex>$R^{2}$</tex> than long short-term memory and other methods, showing a better prediction effect. The results verify the usability of the approach designed in this paper for practical safety monitoring and early warning of public opinion.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"365-380"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint 结合 5G 融合信道指纹的跨层认证机制模型

Journal of ICT Standardization Pub Date : 2024-09-01 DOI: 10.13052/jicts2245-800X.1234

Wei Ao;Jian Wei;Yindong Li;Xiaolong Zhang;Bin Yu;Kaiwen Hou

{"title":"Cross-Layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint","authors":"Wei Ao;Jian Wei;Yindong Li;Xiaolong Zhang;Bin Yu;Kaiwen Hou","doi":"10.13052/jicts2245-800X.1234","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1234","url":null,"abstract":"In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"311-336"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0