Network User Behavior Research Articles

Explainable Machine Learning Models for Real-Time Threat Detection in Cybersecurity

In the rapidly evolving landscape of cybersecurity, traditional machine learning models often operate as "black boxes," providing high accuracy but lacking transparency in decision-making. This lack of explainability poses challenges for trust and accountability, especially in critical areas like threat detection and incident response. Explainable machine learning models aim to address this by making the model's predictions more understandable and interpretable to users. This research integrates explainable machine learning models for real-time threat detection in cybersecurity. Data from multiple sources, including network traffic, system logs, and user behavior, undergo preprocessing such as cleaning, feature extraction, and normalization. The processed data is passed through various machine learning models, including traditional approaches like SVM and decision trees, as well as deep learning models like CNN and RNN. Explainability techniques such as LIME, SHAP, and attention mechanisms provide transparency, ensuring interpretable predictions. The explanations are delivered through a user interface that generates alerts, visualizations, and reports, facilitating effective threat assessment and incident response in decision support systems. This framework enhances model performance, trust, and reliability in complex cybersecurity scenarios.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Proactive cyber threat mitigation: Integrating data-driven insights with user-centric security protocols

In the rapidly evolving cybersecurity landscape, proactive threat mitigation has become essential for protecting sensitive data and systems from increasingly sophisticated attacks. The paper explores a holistic approach to enhancing cybersecurity by merging data-driven insights with user-centric security practices. This study highlights the limitations of traditional reactive security measures and advocates for a shift towards a proactive strategy that anticipates and addresses potential threats before they materialize. The research emphasizes the importance of leveraging data analytics to gain actionable insights into potential vulnerabilities and emerging threats. By analyzing large datasets from network traffic, user behavior, and historical incidents, organizations can identify patterns and anomalies that signal potential risks. These data-driven insights enable the development of more effective threat detection systems and predictive models that enhance an organization’s ability to respond swiftly to evolving cyber threats. In addition to data analytics, the study underscores the significance of integrating user-centric security protocols. Traditional security measures often overlook the human element, which can be a critical factor in both the efficacy of security measures and the overall risk profile. User-centric protocols focus on enhancing user awareness, implementing tailored security training, and promoting best practices in cybersecurity. This approach ensures that security measures are not only technologically sound but also aligned with user behavior and needs, reducing the risk of human error and improving overall security posture. The research also discusses practical implementation strategies for combining data-driven insights with user-centric protocols, including the use of advanced analytics tools, continuous user education, and adaptive security frameworks. Case studies demonstrate the benefits of this integrated approach, such as improved threat detection rates, reduced incident response times, and enhanced overall security resilience. In conclusion, this study presents a comprehensive framework for proactive cyber threat mitigation, emphasizing the synergy between data-driven analytics and user-centric security practices as a robust strategy for safeguarding against modern cyber threats. Keywords: Cyber Threat, Proactive, Data-Driven, User-Centric, Security Protocols

Multi-dimensional feature fusion-based expert recommendation in community question answering

PurposeCommunity question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.Design/methodology/approachTo address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.FindingsThe proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.Practical implicationsThis work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.Originality/valueThis work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

HARNESSING ARTIFICIAL INTELLIGENCE FOR ADVANCED THREAT DETECTION IN NETWORK INFRASTRUCTURES

As cyber threats grow increasingly sophisticated, traditional security measures are falling behind in the face of these evolving attacks. This article highlights the role of Smart Sentinel, a cutting-edge threat detection system that utilizes Artificial Intelligence (AI) to strengthen network defenses. Unlike conventional security solutions, Smart Sentinel employs machine learning algorithms to continuously learn and adapt, enabling it to detect anomalies and potential threats in real-time. By analyzing a variety of data sources, including network traffic and user behavior, the system establishes a baseline of normal activity and continually enhances its threat detection capabilities. Key features such as anomaly detection, behavioral analysis, real-time response, and adaptive learning contribute to an improved security posture, reduced false positives, enhanced operational efficiency, and cost-effectiveness. Smart Sentinel represents a significant breakthrough in cybersecurity, providing organizations with a proactive and resilient defense against the ever-evolving cyber threat landscape. Key features such as anomaly detection, behavioral analysis, real-time response, and adaptive learning contribute to Smart Sentinel’s effectiveness in protecting organizations against cyber threats. Anomaly detection: Smart Sentinel identifies unusual patterns and activities that deviate from established norms, alerting security teams to potential threats. Behavioral analysis: Smart Sentinel goes beyond simply detecting anomalies by analyzing user and system behavior to identify suspicious patterns that may indicate malicious intent. Real-time response: Smart Sentinel’s real-time response capabilities enable it to take immediate action upon detecting a threat, such as isolating compromised systems, blocking malicious IP addresses, or generating alerts for human intervention. Adaptive learning: Through continuous monitoring and analysis, Smart Sentinel continuously learns and improves its ability to discern between normal and malicious activities, reducing false positives and improving overall security posture. Smart Sentinel’s impact on organizations is multifaceted: Enhanced security posture: Smart Sentinel proactively identifies and mitigates emerging threats, strengthening an organization’s overall security posture. Reduced false positives: Smart Sentinel’s adaptive learning capabilities minimize the number of false positives, preventing alert fatigue and allowing security teams to focus on genuine threats. Operational efficiency: Smart Sentinel automates routine tasks, freeing up security teams to focus on strategic initiatives and improve operational efficiency. Cost-effectiveness: Smart Sentinel optimizes cybersecurity investments by automating threat detection and response, reducing the need for costly manual intervention and minimizing the potential for financial losses from successful cyberattacks.

Evaluating the efficacy of decision tree-based machine learning in classifying intrusive behaviour of network users

Evaluating the efficacy of decision tree-based machine learning in classifying intrusive behaviour of network users

Workload prediction based on improved error correlation logistic regression algorithm and Cross‐TRCN of spatiotemporal neural network

AbstractIn view of the randomness of user network usage behavior in data centers, which leads to a large randomness in power load, and considering that a single randomness processing method is usually difficult to fully characterize the uncertain characteristics of the system, this paper proposes a dual fusion prediction analysis model based on an improved error correlation logic regression algorithm and a novel spatiotemporal neural network structure called Cross‐TRCN. Two weight coefficients λ1 and λ2 are introduced to fuse the prediction results with different long‐term sequence prediction performance, thereby further eliminating the influence of random errors. The results show that it is feasible to predict the workload of data centers based on the improved error correlation logic regression algorithm and the innovative spatiotemporal neural network structure Cross‐TRCN.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

Optimizing throughput value through re-examining of 4G LTE infrastructure link budgets: A Case Study in Lubuk Kilangan

Broadband wireless access technology has taken the lead in communication technology development, enabling users to communicate with fast data speeds and extensive coverage.. Problems that frequently arise in mobile communication systems are the wide range of services and the level of network service quality. Thus, optimization is carried out to improve network quality in the Lubuk Kilangan area by using the drive test method to determine the quality of the network and bad spot areas and analyzed using terms discovery software. The reason behind the findings of this study is that the optimal link budget value for 4G LTE infrastructure in Lubuk Kilangan is higher than the current industry standard due to the unique characteristics of the area. According to the analysis and calculation of the link budget, power additions, and antenna tilting alterations were carried out for optimization. The power quality of the RSRP parameter increased from the fair (orange) area of 2.58 to 0.29. For the SINR parameter, For the SINR parameter, there was also an increase in quality at poor category locations in the KPI parameters (Range RSRP) locations from 0.08% to 0.05%, resulting in SINR bad spots decreasing. The proportion for Throughput after optimization remains the same, at 100%. Indicating that there had been a good increase in the coverage area, making the coverage in the Lubuk Kilangan area good. It is necessary to conduct a deeper study by comparing several types of antenna orientation, including tilt, depending on various factors such as the deployment scenario, coverage requirements, interference considerations, and the specific characteristics of picocells, femtocells, and repeaters. Future research could explore the impact of other factors, such as network topology and user behavior, on link budget optimization of 4G LTE infrastructure in different contexts

An improved model of the diffusion of Bass innovations for predicting changes in public opinion during the implementation of a narrative in social networks

During the writing of the article, the complex dynamics of forecasting public opinion based on the implementation of narratives in social networks were analyzed using Bass's innovation diffusion model. Recognizing the important role of narratives in the formation of collective views provides a basis for using Bass's innovation diffusion model to predict and understand changes in public attitudes during the introduction and implementation of narratives in social networks. On the basis of the above, the research task was set: improvement of the Bass innovation diffusion model for predicting changes in public opinion in social networks, which will directly contribute to the development of quality content by determined forces and means to create favorable conditions during the use of troops (forces) and in peacetime due to entering the coefficient of coverage of the target audience. During the writing of the article, theoretical methods were applied, namely, the analysis of research and publications on the topic of development in relation to the use of diffusion of innovations, which describes the process of how innovations are accepted by the population, the analysis of statistical data of public opinion, comparisons to identify development trends in the consumption of information in social networks, as well as synthesis to achieve the goal of the study. The article presents a mathematical apparatus that incorporates the Bass diffusion model into network behavior for modeling the spread of narratives and predicting changes in public opinion. For the first time, an approach to simulation modeling of the introduction and implementation of a narrative in a social network based on the Bass diffusion model was substantiated, which made it possible to investigate the consumer behavior of social network users under the influence of information and interpersonal communication in a social network. Integrating concepts from Bass's model, such as innovation adoption and imitative behavior, the proposed model aims to predict the dynamics of public attitudes in response to the spread of narratives.

Research on Algorithm-Driven International Chinese Online Language and Culture Dissemination Mechanisms

Abstract With the development of digitalization and Internet technology, algorithm-driven international Chinese online language and culture dissemination mechanisms are becoming increasingly important and a key area of cultural exchange in globalization. This study explores the international Chinese online language and culture communication mechanism under algorithm-driven. By analyzing social network data and adopting quantitative methods such as the SEIR model, the study reveals the effects of network structure, user behavior and cultural content characteristics on communication. The study results show that in the algorithm-optimized network environment, the propagation speed of information is increased by about 50%, and the propagation efficiency of its central node is increased by nearly 70%. Simulations using the SEIR model show that the dissemination coverage of Chinese culture can be increased by more than 40% in a short period with the intervention of the algorithm. In addition, the survey data shows that about 60% of the respondents consider social media the main channel to learn about Chinese culture. The study also found that content with a high degree of interactivity is more likely to be disseminated, with an average interaction rate 30% higher than that of moderate content. Algorithm-driven Chinese cultural communication has higher speed and breadth, but it also needs to pay attention to the depth and diversity of cultural content.

A data mining method based on label mapping for long-term and short-term browsing behaviour of network users

A data mining method based on label mapping for long-term and short-term browsing behaviour of network users

A social network user behaviour data recommendation system based on fuzzy partition clustering

A social network user behaviour data recommendation system based on fuzzy partition clustering

A social network user behaviour data recommendation system based on fuzzy partition clustering

A social network user behaviour data recommendation system based on fuzzy partition clustering

GCN-LSTM: multi-label educational emotion prediction based on graph Convolutional network and long and short term memory network fusion label correlation in online social networks

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects.

Social networks and aggressive attitudes: who is who. Scoping review of the scientific production on their relationships.

In the current world, an increasing number of people use social networks as a scenario for socialization, which have come to stay as a part of human development. During this socialization process, violent situations occur all too often, despite their virtuality, and seriously compromises the emotional well-being of the other participants. Based on the work conducted on this subject, the following systematic review aims to establish the state of the art regarding the relationship between moral disengagement, disruptive behavior and emotional intelligence of social network users. A scoping review is carried out, according to the PRISMA-ScR criteria, consulting the WoS, Scopus, Education database, PsycINFO, PsycARTICLES, PLOS one and ScienceDirect databases, from 2021 up to the present day. A total of 999 articles related to the research topic were collected, although the result of research responding to the specific search criteria was reduced to 10. The research identified shows that there is a relationship between the level of moral development of social network users and their participation in aggressive online behavior. However, more research is needed, as it has not been demonstrated whether it is the networks that develop or favor the emergence of these attitudes, or simply act as facilitators for their amplified expression.

LEXICAL MEANS OF EXPRESSING VERBAL AGGRESSION IN SOCIAL NETWORKS (BASED ON THE MATERIALS OF COMMENTS ON FACEBOOK AND TWITTER ON THE PAGES OF P. POROSHENKO AND V. ZELENSKY)

The article characterizes the lexical means of realizing the conflictual communicative behavior of social network users. The network comments presented on the official pages of Ukrainian politicians - the current President of Ukraine Volodymyr Zelenskyi and the previous President of Ukraine Petro Poroshenko - in the Facebook and Twitter social networks were considered. The concept of verbal aggression is defined as a means of implementing conflictual communicative behavior. The genre specificity of comments in social networks is outlined and the peculiarities of their functioning are determined. Lexical units, which are verbal markers of aggressive behavior of commentators in social networks, were analyzed. The purpose of the study is to identify the main lexical means of implementing verbal aggression in the political discourse of social networks. The object of analysis is the conflictual communicative behavior of social network users; the subject of the study is the verbal aggression of commentators on the official pages of Ukrainian politicians. The material for analysis was comments on posts on the official pages of Ukrainian politicians Volodymyr Zelenskyi and Petro Poroshenko on Facebook and Twitter for the year 2023. In total, more than 2,000 comments were analyzed. The lexical means of expressing verbal aggression were analyzed based on the material of those comments that characterize politicians, rather than direct communicators – supporters of one of the Ukrainian presidents. It has been proven that the main means of representing verbal aggression in comments to the posts of Ukrainian politicians on Facebook and Twitter are anthroponyms, stylistically reduced vocabulary, invectives, jargonisms and reminiscences and quotes of politicians’ speeches.

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

Social Complex Networks in communication networks are pivotal for comprehending the impact of human-like interactions on information flow and communication efficiency. These networks replicate social behavior patterns in the digital realm by modeling device interactions, considering friendship, influence, and information-sharing frequency. A key challenge in communication networks is their dynamic topologies, driven by dynamic user behaviors, fluctuating traffic patterns, and scalability needs. Analyzing these changes is essential for optimizing routing and enhancing the user experience. This paper introduces a network model tailored for Opportunistic Networks, characterized by intermittent device connections and disconnections, resulting in sporadic connectivity. The model analyzes node behavior, extracts vital properties, and ranks nodes by influence. Furthermore, it explores the evolution of node connections over time, gaining insights into changing roles and their impact on data exchange. Real-world datasets validate the model’s effectiveness. Applying it enables the development of refined routing protocols based on dynamic influence rankings. This approach fosters more efficient, adaptive communication systems that dynamically respond to evolving network conditions and user behaviors.

PERILAKU KOMUNIKASI PENGGUNAAN MEDIA SOSIAL TIKTOK DI KALANGAN MAHASISWA KPI FAKULTAS DAKWAH DAN KOMUNIKASI UNIVERSITAS ISLAM NEGERI SUMATERA UTARA STAMBUK 2019

This study discusses the communication behavior of TikTok social Network users among the students of the Islamic Broadcasting and Media Studies (KPI) program at the Faculty of Dakwah, Islamic State University of North Sumatra, course 2019. This study aims to find out how the student KPI's communication behavior of class 2019 via social Network TikTok. The research method used is qualitative with a descriptive approach. The researcher used observational tools, interview guide and himself as the main tools. Research data was collected through in-depth interviews with KPI students and analysis was performed holistically for a comprehensive understanding of the use of TikTok in the context of student-to-student communication. Research results show that KPI students, as users of the social Network TikTok, use the platform as a means of entertainment, a source of knowledge, learning about Islam, and a motivation to share. their own positive content. TikTok's social media also makes it easy for them to stay up to date with relevant trending information. In addition, students' communication behavior when using TikTok tends to be positive and has no negative impact. However, we are reminded to use this platform wisely, avoid abuse, and always categorize trending information and content seriously and intelligently. This study provides valuable insights into TikTok's social media usage among KPI students and its relevance in digital communication in the context of higher education.