Structural Analysis of User Association Patterns in University Campus Wireless LANs

Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.

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.

Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

This paper presents a continuous authentication system based on user behavior analysis that makes use of environmental context information, users' behavior analysis and Neuro-Fuzzy Logic. This system must be able to acquire information in context, making them into a computational environment. This information is the basis of user behavior. The System, based on the evidences of the behavior, establishes if it can trust the user or not. According to the user behavior, levels of trust are released, to access the application software. Weights are attributed in the fuzzyfication process, according to the rules that were previously established for the parameters which help to establish the evidences of behavioral trust, in its different degrees. The neuro-fuzzy logic allows that the user behavioral database be continuously updated, interacting with the fuzzyfication mechanism, so as to keep trust levels updated according to the user behavior, in a more accurate and faithful way.

This paper discusses the construction and analysis method of user behavioral portrait by the data provided by the electric power platform in the big data environment. Firstly, it introduces the construction and analysis of user profiles based on big data platforms, which covers the construction of user basic attribute profiles, user behavioral characteristics profiles, user product characteristics profiles and user interaction characteristics profiles from different dimensions. Secondly, for the electric power sector, the article discusses the analysis of big data provided by electric power platforms to better understand user behavior and trends in energy consumption. The article proposes a method for constructing a behavioral portrait of power users based on big data analysis, including the construction and management of a user label library and the process of constructing a behavioral portrait of power users based on the improved K-mean algorithm. Finally, the effectiveness and accuracy of the method of this paper are verified by experimental analysis. Overall, this paper provides some guidance and reference for the analysis of user behavior in the field of electric power by exploring the method of user behavior portrait construction with the data provided by the electric power platform in the big data environment.

Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with userpsilas requested services. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper, we propose a novel data mining method based on temporal mobile access patterns that can efficiently discover mobile user's behavior patterns. An advantage of our method compactly stores user's behavior patterns according to location log dataset and requested services in memory. Thus, even the information datasets require large shared memory when they store, our approach still provides faster access and consume less memory than existing techniques.

This study aims to determine factors that influence the use of information technology in higher education, especially at Makassar State University. The type of information technology studied is academic information systems. This study combines two theories, namely the Technology Acceptance Model (TAM) and The Theory of Planned Behavior (TPB) to explain the user behavior for the use of academic information systems at Makassar State University. The results of this study indicate that perceived usefulness and ease of use significantly affect user behavioral intention to use academic information systems. Behavioral intention significantly affected actual use of the academic information system. An interesting finding is that the facilities available for the use of the system in the campus environment have no significant effect on the use of the academic information system at Makassar State University.

Users for online shopping are increasing day by day because of easy to get and time-saving property of online shopping. Having a proper understanding of users interest for certain type of product or different products for online shopping becomes important to create personalized service for a target market. An important property of successful e-commerce website is the ability to provide useful content at the right time to users. And because of all this, personalization techniques are introduced to create adaptive shopping application in which user interfaces change according to users interest. User’s behavior information is stored in web log files, and to get the information data mining techniques are used in which they use statistical characters to model users behavior and not considering the sequence of action performed by uses. It becomes helpful if we follow user’s session to understand complex user behavior. Therefore to eliminates all these issues this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce weblogs. If we consider a common way of mapping log records according to e-commerce structure, weblogs can be easily converted into event logs by which behavior of the user is captured. After getting users behavior by performing different predefine queries to identify different behavioral patterns that consider the different actions performed by a user during a session.

Churning of mobile users from one service provider to another is expected to become a common feature when the mobile users have freedom to choose the best wireless service. This churning behavior impacts both the technical and the economical aspects of wireless network design. In this paper, we model the churning behavior of wireless service users by using the theory of evolutionary game. We consider a system model consisting of WLAN hotspots where a wireless user can choose among different WLAN access points based on the performances and/or price. A continuous-time Markov chain model is established to capture the connection arrival and departure processes, as well as the rational and irrational churning behaviors of wireless service users. The evolutionary equilibrium, which is used to compute the average number of users choosing each wireless service, is considered as the solution. Based on this evolutionary game framework, we investigate two different possible pricing schemes, namely, non-cooperative and cooperative pricing schemes, for the wireless service providers. These schemes maximize individual revenue and total revenue, respectively, of the service providers. Performance analysis results are presented for the proposed modeling framework.

The behavior of users concerning mobile Internet varies significantly throughout the day. Results from existing studies—which generally simply segment one day into morning, afternoon, and evening—often provide inaccurate predictions of the behavior of users. To improve prediction accuracy, we propose a segment-based multi-state Markov (SBMSM) model for the dynamic time interval segmentation of the sequential behavior of users. The specific procedure of this proposed model can be described as follows: first, we divide each user’s behaviors into minimum unit according to time dimension; then, we merge adjacent time intervals or ensure they are constant according to the similarities in behavior; and finally, a multi-state Markov (MSM) model is trained using the newly constructed data individually. The experimental results illustrate that for 95.78% of users, an SBMSM model performs much better than a naive MSM model and hidden Markov model.

7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017, San Antonio, United States, 23-26 October 2017

User behavior visualization as a powerful tool can help analysts better understand user behavior data. Previous research such as Information Architecture believes that a user journey map is the regular way to visualize user behavior patterns. The technique is a diagram that shows the steps, touch-points, and emotions that users go through when using their products or services. Current studies mainly focus on the procedure and framework of applying user behavior data into visualization tools and its benefit to business development. Studies also found that visualization in user behavior analysis has difficulty in dealing with complex data. However, few studies have discussed the possible solution for the current limitation of visualization in user behavior analysis. With the popularization of the Internet and mobile devices, the scale and scale of user behavior data and the complexity will continue to increase, and user behavior visualization will be an important tool for analyzing this data, helping to tap potential business opportunities and improve the efficiency of data analysis. Therefore, we would make the review on visualization in user behavior analysis. We will first provide the background of the user behavior and its visualization and then introduce the definition of user behavior. Afterward, we will discuss the current process of visualization in user behavior analysis and its current limitations. Next, we will propose the possible solution of introducing machine learning, deep learning, predicting the data with a sequence model, and using cluster analysis with usability analysis tools to contribute to the future development of visualization in user behavior analysis. We believe that with data amount and scale keep increasing, the visualization tools need to develop themselves in diversity and data processing speed.

In a Systems Engineering perspective, insider threats are not new but emerging. For modern systems that require a seamless integration between human and machine, human interactions with systems become a large part of operations. This creates a system security challenge while protecting systems from insider threats. As technology evolves, system vulnerabilities and network penetrations from external entities have been the center of attention. However, when considering system security, insider threats are as damaging as external threats. Unforutnately, insider threats have been overlooked for decades and there is no clear guidance or standards to protect systems from insider attacks. In this paper, we focus on identifying potential insider threats based on human behaviors, such as system logins. By analyzing user behavioral patterns, we attempt to identify suspicious behaviors based on common characterestics that appear to be unusual and estimate the prediction interval to determine if such behaviors should be considered as a threat. Having a baseline of normal user behaviors and comparing against unusual behaviors allow us to predict and identify potential insider threats.

Background. In the context of hybrid warfare, the role of information influence on critical infrastructure is growing. Fake news can trigger waves of user behavioral activity that mimic DDoS attacks, overloading government digital services even without direct technical interference. This creates the need to model such scenarios considering both technical and socio-behavioral factors. Methods. An agent-based model of hybrid influence is proposed, including users, bots, fake news sources, and protective mechanisms. The model is implemented using mathematical expressions and scenario simulation, including a baseline scenario, delayed response scenario, technical intervention scenario, and official rebuttal scenario. The analysis considers system load parameters, behavioral activation probability, and the system's response. Results. It was found that user behavior alone can generate critical system load equivalent to a DDoS attack. The most effective scenario combined technical blocking and information rebuttal, reducing load to a stable level. Classic cybersecurity models often fail to account for delayed reactions or wave-like behavioral patterns, limiting their effectiveness in hybrid environments. Conclusions. The proposed model enables not only simulation of hybrid influence but also evaluation of response tactics. It can be applied in CERT platforms, Red Team / Blue Team exercises, and early-warning systems. Further research may focus on adapting the model to real-world environments and developing automated response mechanisms to information threats.

Social media has developed rapidly, which had already affected modern people's life style. Meanwhile, social media has produced huge amounts of data. In research, we find that there is no explicit definition of user's behavior pattern in social network. Therefore, we focused on the research of that and found people who have two types of behaviors which are inseparably intertwined. The two types that mentioned are functional behaviors and social behaviors. We have defined user's behavior, and regarded user's behavior as a vertical timeline which the object of study is individual. With adding some horizontal lines which stand for social behaviors between vertical timelines, the model linked different people. Experiment's research takes Sina Weibo as an object. For obtaining the social behavior pattern, we use the generalized sequential pattern algorithm (GSP) for data mining. And the pattern is represented by the form of [User Behavior User a#x2192; User Behavior User]*n. According to the result and algorithm accuracy confirmation, the frequent behavioral patterns of a certain person can be obtained. After that we can also predict user's behaviors in the future.