Pub Date : 2024-01-18DOI: 10.1142/s0218843024500096
Hao Liu
{"title":"ACSICS: Joint Distribution Mode Integrating Agricultural Industry Chain Logistics Under the Background of Artificial Intelligence","authors":"Hao Liu","doi":"10.1142/s0218843024500096","DOIUrl":"https://doi.org/10.1142/s0218843024500096","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1142/s0218843024500035
Amani K. Samha, Ghalib H. Alshammri, Sasidhar Attuluri, Preetam Suman, Arvind Yadav
{"title":"IMRCDS: AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks","authors":"Amani K. Samha, Ghalib H. Alshammri, Sasidhar Attuluri, Preetam Suman, Arvind Yadav","doi":"10.1142/s0218843024500035","DOIUrl":"https://doi.org/10.1142/s0218843024500035","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-23DOI: 10.1142/s0218843023990010
{"title":"Author Index Volume 32 (2023)","authors":"","doi":"10.1142/s0218843023990010","DOIUrl":"https://doi.org/10.1142/s0218843023990010","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135459865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growth in services supplied over the internet, network infrastructure has become more exposed to cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, which can easily cause the disruption of services. The key factor for fighting against these attacks is the earlier separation and detection of the traffic in networks. In this paper, a novel approach, named Half Autoencoder-Stacked DNNs (HAE-SDNN) model, is proposed. We suggest using a Stacked Deep Neural Networks (SDNN) model. as a deep learning model, in order to detect DDoS attacks. Our approach allows feature selection from a preprocessed dataset using a Half AutoEncoder (HAE), resulting in a final set of important features. These features are subsequently used to train the DNNs that are stacked together by applying Softmax layer to combine their outputs. Experiments were performed on a benchmark cybersecurity dataset, named CICDDoS2017, containing various DDoS attack types. The experimental results demonstrate that the introduced model attained an overall accuracy rate of 99.95%. Moreover, the HAE-SDNN model outperformed existing models, highlighting its superiority in accurately classifying attacks.
{"title":"DDOS Attacks Detection with Half Autoencoder-Stacked Deep Neural Network","authors":"Emna Benmohamed, Adel Thaljaoui, Salim El Khediri, Suliman Aladhadh, Mansor Alohali","doi":"10.1142/s0218843023500259","DOIUrl":"https://doi.org/10.1142/s0218843023500259","url":null,"abstract":"With the growth in services supplied over the internet, network infrastructure has become more exposed to cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, which can easily cause the disruption of services. The key factor for fighting against these attacks is the earlier separation and detection of the traffic in networks. In this paper, a novel approach, named Half Autoencoder-Stacked DNNs (HAE-SDNN) model, is proposed. We suggest using a Stacked Deep Neural Networks (SDNN) model. as a deep learning model, in order to detect DDoS attacks. Our approach allows feature selection from a preprocessed dataset using a Half AutoEncoder (HAE), resulting in a final set of important features. These features are subsequently used to train the DNNs that are stacked together by applying Softmax layer to combine their outputs. Experiments were performed on a benchmark cybersecurity dataset, named CICDDoS2017, containing various DDoS attack types. The experimental results demonstrate that the introduced model attained an overall accuracy rate of 99.95%. Moreover, the HAE-SDNN model outperformed existing models, highlighting its superiority in accurately classifying attacks.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1142/s0218843023500247
Kamal Upreti, Prashant Vats, Aravindan Srinivasan, K. V. Daya Sagar, R. Mahaveerakannan, G. Charles Babu
When income, assets, sales, and profits are inflated while expenditures, debts, and losses are artificially lowered, the outcome is a set of fraudulent financial statements (FFS). Manual auditing and inspections are time-consuming, inefficient, and expensive options for spotting these false statements. Auditors will find great assistance from the use of intelligent methods in the analysis of several financial declarations. Now more than ever, victims of financial fraud are at risk since more and more individuals are using the Internet to conduct their financial transactions. And the frauds are getting more complex, evading the protections that banks have put in place. In this paper, we offer a new-fangled method for detecting fraud using NLP models: an ensemble model comprising Feedforward neural networks (FNNs) and Long Short-Term Memories (LSTMs). The Spotted Hyena Optimizer is a unique metaheuristic optimization technique used to choose weights and biases for LSTM (SHO). The proposed method takes inspiration from the law of gravity and is meant to mimic the group dynamics of spotted hyenas. Mathematical models and discussions of the three fundamental phases of SHO — searching for prey, encircling prey, and at-tacking prey — are presented. We build a model of the user’s spending habits and look for suspicious outliers to identify fraud. We do this by using the ensemble mechanism, which helps us predict and make the most of previous trades. Based on our analysis of real-world data, we can confidently say that our model provides superior performance compared to state-of-the-art approaches in a variety of settings, with respect to both precision and.
{"title":"Detection of Banking Financial Frauds Using Hyper-Parameter Tuning of DL in Cloud Computing Environment","authors":"Kamal Upreti, Prashant Vats, Aravindan Srinivasan, K. V. Daya Sagar, R. Mahaveerakannan, G. Charles Babu","doi":"10.1142/s0218843023500247","DOIUrl":"https://doi.org/10.1142/s0218843023500247","url":null,"abstract":"When income, assets, sales, and profits are inflated while expenditures, debts, and losses are artificially lowered, the outcome is a set of fraudulent financial statements (FFS). Manual auditing and inspections are time-consuming, inefficient, and expensive options for spotting these false statements. Auditors will find great assistance from the use of intelligent methods in the analysis of several financial declarations. Now more than ever, victims of financial fraud are at risk since more and more individuals are using the Internet to conduct their financial transactions. And the frauds are getting more complex, evading the protections that banks have put in place. In this paper, we offer a new-fangled method for detecting fraud using NLP models: an ensemble model comprising Feedforward neural networks (FNNs) and Long Short-Term Memories (LSTMs). The Spotted Hyena Optimizer is a unique metaheuristic optimization technique used to choose weights and biases for LSTM (SHO). The proposed method takes inspiration from the law of gravity and is meant to mimic the group dynamics of spotted hyenas. Mathematical models and discussions of the three fundamental phases of SHO — searching for prey, encircling prey, and at-tacking prey — are presented. We build a model of the user’s spending habits and look for suspicious outliers to identify fraud. We do this by using the ensemble mechanism, which helps us predict and make the most of previous trades. Based on our analysis of real-world data, we can confidently say that our model provides superior performance compared to state-of-the-art approaches in a variety of settings, with respect to both precision and.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-29DOI: 10.1142/s0218843023500235
Zeqing Xiao, Hui Ou
The amount of voltage fault data collection is limited to signal acquisition instruments and simulation software. Generative adversarial networks (GAN) have been successfully applied to the data generation tasks. However, there is no theoretical basis for the selection of the network structure and parameters of generators and discriminators in these GANs. It is difficult to achieve the optimal selection basically by experience or repeated attempts, resulting in high cost and time-consuming deployment of GAN computing in practical applications. The existing methods of neural network optimization are mainly used to compress and accelerate the deep neural network in classification tasks. Due to different goals and training processes, they cannot be directly applied to the data generation task of GAN. In the three-generation scenario, the hidden layer filter nodes of the initial GAN generator and discriminator are growing firstly, then the GAN parameters after the structure adjustment are optimized by particle swarm optimization (PSO), and then the node sensitivity is analyzed. The nodes with small contribution to the output are pruned, and then the GAN parameters after the structure adjustment are optimized using PSO algorithm to obtain the GAN with optimal structure and parameters (GP-PSO-GAN). The results show that GP-PSO-GAN has good performance. For example, the simulation results of generating unidirectional fault data show that the generated error of GP-PSO-GAN is reduced by 70.4% and 15.2% compared with parameters optimization only based on PSO (PSO-GAN) and pruning- PSO-GAN (P-PSO-GAN), respectively. The convergence curve shows that GP-PSO-GAN has good convergence.
电压故障数据的采集量受到信号采集仪器和仿真软件的限制。生成对抗网络(GAN)已成功地应用于数据生成任务。然而，这些gan的网络结构和产生器和鉴别器参数的选择尚无理论依据。GAN计算在实际应用中的部署成本高、耗时长，难以通过经验或反复尝试实现最优选择。现有的神经网络优化方法主要用于深度神经网络在分类任务中的压缩和加速。由于目标和训练过程不同，它们不能直接应用于GAN的数据生成任务。在三代方案中，首先对初始GAN发生器和鉴别器的隐层滤波节点进行生长，然后利用粒子群算法对结构调整后的GAN参数进行优化，最后对节点的灵敏度进行分析。对输出贡献较小的节点进行剪枝，然后利用粒子群算法对结构调整后的GAN参数进行优化，得到结构和参数最优的GAN (GP-PSO-GAN)。结果表明，GP-PSO-GAN具有良好的性能。例如，生成单向故障数据的仿真结果表明，与仅基于PSO (PSO- gan)和基于剪枝-PSO- gan (P-PSO-GAN)参数优化相比，GP-PSO-GAN的生成误差分别降低了70.4%和15.2%。收敛曲线表明GP-PSO-GAN具有较好的收敛性。
{"title":"Research on Data Generation Based on the Combination of Growing-Pruning GAN and Intelligent Parameter Optimization","authors":"Zeqing Xiao, Hui Ou","doi":"10.1142/s0218843023500235","DOIUrl":"https://doi.org/10.1142/s0218843023500235","url":null,"abstract":"The amount of voltage fault data collection is limited to signal acquisition instruments and simulation software. Generative adversarial networks (GAN) have been successfully applied to the data generation tasks. However, there is no theoretical basis for the selection of the network structure and parameters of generators and discriminators in these GANs. It is difficult to achieve the optimal selection basically by experience or repeated attempts, resulting in high cost and time-consuming deployment of GAN computing in practical applications. The existing methods of neural network optimization are mainly used to compress and accelerate the deep neural network in classification tasks. Due to different goals and training processes, they cannot be directly applied to the data generation task of GAN. In the three-generation scenario, the hidden layer filter nodes of the initial GAN generator and discriminator are growing firstly, then the GAN parameters after the structure adjustment are optimized by particle swarm optimization (PSO), and then the node sensitivity is analyzed. The nodes with small contribution to the output are pruned, and then the GAN parameters after the structure adjustment are optimized using PSO algorithm to obtain the GAN with optimal structure and parameters (GP-PSO-GAN). The results show that GP-PSO-GAN has good performance. For example, the simulation results of generating unidirectional fault data show that the generated error of GP-PSO-GAN is reduced by 70.4% and 15.2% compared with parameters optimization only based on PSO (PSO-GAN) and pruning- PSO-GAN (P-PSO-GAN), respectively. The convergence curve shows that GP-PSO-GAN has good convergence.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1142/s0218843023500223
Tao Wang, Tianbang Song
At present, the financial situation of China’s supply chain finance is still relatively unstable, and there are still some problems between supply chain enterprises and banks such as asymmetric information, insufficient model innovation and high operational risks. Based on this, this paper proposes and constructs a risk control model of financial big data analysis based on collaborative filtering algorithm. The purpose of this study is to realize the resource integration of supply chain enterprises and optimize the logistics chain, financial chain and information chain through the analysis of financial big data based on collaborative filtering algorithm, provide quality services for supply chain enterprises and good support for solving the financing problems of small and medium-sized enterprises. In order to verify the feasibility of the model, an experimental analysis is carried out. The experimental results show that this model has good scalability and operability, and the algorithm itself also has good scalability. The results of empirical analysis further verify that the design method in this paper has a good recommendation effect in terms of matching degree and user satisfaction. Compared with other risk control models, it is more practical and feasible. This research has certain practical significance for the financial management of supply chain enterprises.
{"title":"Application of Financial Big Data Analysis Method Based on Collaborative Filtering Algorithm in Supply Chain Enterprises","authors":"Tao Wang, Tianbang Song","doi":"10.1142/s0218843023500223","DOIUrl":"https://doi.org/10.1142/s0218843023500223","url":null,"abstract":"At present, the financial situation of China’s supply chain finance is still relatively unstable, and there are still some problems between supply chain enterprises and banks such as asymmetric information, insufficient model innovation and high operational risks. Based on this, this paper proposes and constructs a risk control model of financial big data analysis based on collaborative filtering algorithm. The purpose of this study is to realize the resource integration of supply chain enterprises and optimize the logistics chain, financial chain and information chain through the analysis of financial big data based on collaborative filtering algorithm, provide quality services for supply chain enterprises and good support for solving the financing problems of small and medium-sized enterprises. In order to verify the feasibility of the model, an experimental analysis is carried out. The experimental results show that this model has good scalability and operability, and the algorithm itself also has good scalability. The results of empirical analysis further verify that the design method in this paper has a good recommendation effect in terms of matching degree and user satisfaction. Compared with other risk control models, it is more practical and feasible. This research has certain practical significance for the financial management of supply chain enterprises.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135477161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1142/s0218843023500181
Minquan Wang, Siyang Lu, Sizhe Xiao, Dong Dong Wang, Xiang Wei, Ningning Han, Liqiang Wang
We consider the problem of real-time log anomaly detection for distributed system with deep neural networks by unsupervised learning. There are two challenges in this problem, including detection accuracy and analysis efficacy. To tackle these two challenges, we propose GLAD, a simple yet effective approach mining for anomalies in distributed systems. To ensure detection accuracy, we exploit the gradient features in a well-calibrated deep neural network and analyze anomalous pattern within log files. To improve the analysis efficacy, we further integrate one-class support vector machine (SVM) into anomalous analysis, which significantly reduces the cost of anomaly decision boundary delineation. This effective integration successfully solves both accuracy and efficacy in real-time log anomaly detection. Also, since anomalous analysis is based upon unsupervised learning, it significantly reduces the extra data labeling cost. We conduct a series of experiments to justify that GLAD has the best comprehensive performance balanced between accuracy and efficiency, which implies the advantage in tackling practical problems. The results also reveal that GLAD enables effective anomaly mining and consistently outperforms state-of-the-art methods on both recall and F1 scores.
研究了基于无监督学习的深度神经网络分布式系统日志实时异常检测问题。该问题面临着检测精度和分析效率两方面的挑战。为了解决这两个挑战，我们提出了GLAD，这是一种简单而有效的分布式系统异常挖掘方法。为了保证检测的准确性，我们在一个校准良好的深度神经网络中利用梯度特征，并分析日志文件中的异常模式。为了提高分析效率，我们进一步将一类支持向量机(one-class support vector machine, SVM)集成到异常分析中，显著降低了异常决策边界划分的成本。这种有效的集成成功地解决了实时日志异常检测的准确性和有效性。此外，由于异常分析是基于无监督学习的，它大大减少了额外的数据标记成本。我们通过一系列实验证明，GLAD在准确性和效率之间具有最佳的综合性能，这意味着在解决实际问题方面具有优势。结果还表明，GLAD能够有效地挖掘异常，并且在召回率和F1分数上始终优于最先进的方法。
{"title":"An Unsupervised Gradient-Based Approach for Real-Time Log Analysis From Distributed Systems","authors":"Minquan Wang, Siyang Lu, Sizhe Xiao, Dong Dong Wang, Xiang Wei, Ningning Han, Liqiang Wang","doi":"10.1142/s0218843023500181","DOIUrl":"https://doi.org/10.1142/s0218843023500181","url":null,"abstract":"We consider the problem of real-time log anomaly detection for distributed system with deep neural networks by unsupervised learning. There are two challenges in this problem, including detection accuracy and analysis efficacy. To tackle these two challenges, we propose GLAD, a simple yet effective approach mining for anomalies in distributed systems. To ensure detection accuracy, we exploit the gradient features in a well-calibrated deep neural network and analyze anomalous pattern within log files. To improve the analysis efficacy, we further integrate one-class support vector machine (SVM) into anomalous analysis, which significantly reduces the cost of anomaly decision boundary delineation. This effective integration successfully solves both accuracy and efficacy in real-time log anomaly detection. Also, since anomalous analysis is based upon unsupervised learning, it significantly reduces the extra data labeling cost. We conduct a series of experiments to justify that GLAD has the best comprehensive performance balanced between accuracy and efficiency, which implies the advantage in tackling practical problems. The results also reveal that GLAD enables effective anomaly mining and consistently outperforms state-of-the-art methods on both recall and F1 scores.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1142/s0218843023500168
S. Deepa, A. Umamageswari, S. Neelakandan, Hanumanthu Bhukya, I. V. Sai Lakshmi Haritha, Manjula Shanbhog
Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot.
{"title":"Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications","authors":"S. Deepa, A. Umamageswari, S. Neelakandan, Hanumanthu Bhukya, I. V. Sai Lakshmi Haritha, Manjula Shanbhog","doi":"10.1142/s0218843023500168","DOIUrl":"https://doi.org/10.1142/s0218843023500168","url":null,"abstract":"Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.1142/s0218843023500260
Mustafa Sabah Mustafa, Mustafa Qahtan Alsudani, Mustafa Musa Jaber, Mohammed Hasan Ali, Sura Khalil Abd, Mustafa Mohammed Jassim
{"title":"Human Emotion Detection from Big Data Using Deep Learning Approach","authors":"Mustafa Sabah Mustafa, Mustafa Qahtan Alsudani, Mustafa Musa Jaber, Mohammed Hasan Ali, Sura Khalil Abd, Mustafa Mohammed Jassim","doi":"10.1142/s0218843023500260","DOIUrl":"https://doi.org/10.1142/s0218843023500260","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136241211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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