A survey for user behavior analysis based on machine learning techniques: current models and applications

Martín, Alfonso Jiménez; Fernández-Isabel, Alberto; Diego, Isaac Martín de; Beltrán, Marta

doi:10.1007/s10489-020-02160-x

Cited by 37 publications

(20 citation statements)

References 164 publications

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“…Data mining approaches can be applied to automatically dig information with the goal to find out some habits and tendencies. 9,10,11 In that case, the web access log file is analysed in order to divide users on the basis of a selected algorithm into several clusters. Users within the same cluster should have similar properties from the point of view of their activity in cyberspace.…”

Section: Web Users' Behaviourmentioning

confidence: 99%

Network User Behaviour Analysis by Machine Learning Methods

Michal¹

2021

ISIJ

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Cyber security is one of the prominent global challenges due to the significant increase in the number of cyberattacks over the last few decades. The amount of transferred data is growing, and a quick reaction to cyber incidents is needed. The paper is a contribution to this effort. There is a possibility to save time and resources by concentrating only on a subgroup of potential threats caused by a specific group of users. The main source of information about a selected group of users is the web access log file, where all the necessary data is stored. The contribution also presents the concept of preprocessing data from the log files to a form useful for clustering. In the next step, a densitybased spatial clustering algorithm is applied to create the clusters. Clustering algorithms have been applied to many fields (marketing, business, etc.), but not for the purposes of cyber defence. The created clusters were analysed according to our definition of risky behaviour. After analysis of the clustering results, it was possible to select a potentially dangerous group of users in the specific cluster. The presented method has potential use in different areas of cyber defence and other applications where intelligent classification is required.

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Section: Web Users' Behaviourmentioning

confidence: 99%

Network User Behaviour Analysis by Machine Learning Methods

Michal¹

2021

ISIJ

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“…The mainstream English teaching still focuses on the traditional offline face-to-face teaching, supplemented by online network teaching, so the teaching mode is relatively single [ 1 ]. Since the teaching model innovation policies are promoted in different regions at this stage, the vigorous development of a variety of data analysis technologies has also triggered the innovative application of English Classroom Teaching [ 2 ]. Manufacturing computer-integrated manufacturing system CIMS, system scheduling, communication network system, database management system, military C3I system, and other systems are typical discrete event systems [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching

Cao

2022

Computational Intelligence and Neuroscience

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The current English teaching mode focuses on the traditional offline teaching and online teaching. In order to solve the problems that some students are inefficient and cannot teach students according to their aptitude in the teaching process, this paper uses the big data analysis strategy based on a neural network algorithm. This paper studies the discrete dynamic modeling method of learner behavior analysis in English teaching. Firstly, it summarizes the current situation of English teaching and the research status of the hybrid application of discrete dynamic modeling technology. Secondly, combined with English teaching content and teaching objectives, through the analysis of various data of students’ learning behavior, this paper evaluates students’ English teaching quality from five aspects that affect the students’ English teaching quality and puts forward a personalized English teaching quality evaluation model based on discrete dynamic modeling technology and learners’ behavior analysis. Finally, through the practical teaching application in a university, the feasibility of the discrete dynamic English teaching model is verified. The results show that compared with the current innovative English teaching methods based on a dynamic iterative decision algorithm, the personalized discrete dynamic English teaching model based on learner behavior analysis significantly improves the quality of English teaching and students’ academic performance.

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“…Most often, phishing attacks exploit network bandwidth and connectivity, downgrading performance in systems they are compromising, whether during network-based attacks or host-based attacks, as shown in Figure 1. Consequently, phishing attacks are successful at halting, interrupting, and demoting the real-time performance of the system by draining all its resources [3].…”

Section: Introductionmentioning

confidence: 99%

“…Avoiding any one of these tenets deliberately or inadvertently might lead to open security breaches and unresolved vulnerabilities, consequently leading to a loss of credentials, reputations, and financial gains. Hence, the focus on web security against phishing attacks is necessary and must counter the latest exploitation techniques [3]. Among all cyberattacks, phishing attacks appear friendly but target financial transactions and highly confidential data.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Antiphishing (CAP) Model for Smart Phones

Faisal

Abed

2021

Scientific Programming

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Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

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A survey for user behavior analysis based on machine learning techniques: current models and applications

Cited by 37 publications

References 164 publications

Network User Behaviour Analysis by Machine Learning Methods

Network User Behaviour Analysis by Machine Learning Methods

Research on Discrete Dynamic Modeling of Learner Behavior Analysis in English Teaching

Cluster-Based Antiphishing (CAP) Model for Smart Phones

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