Cyberattack detection model using deep learning in a network log system with data visualization

Liu, Jung-Chun; Yang, Chao-Tung; Chan, Yu‐Wei; Kristiani, Endah; Jiang, Wei-Je

doi:10.1007/s11227-021-03715-6

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…κ is set as the stability coefficient of unstructured data visualization of social networks. When the stability coefficient takes a value in the interval κ � [2,6], the stability of unstructured data visualization of social networks is relatively high.…”

Section: Visual Stability Analysismentioning

confidence: 99%

“…It can be seen from Figure 7 that when the number of iterations is 500, the stability coefficient of the social network unstructured data visualization method in reference [8] takes a value in the interval [1.5, 6.3], the stability coefficient of the social network unstructured data visualization method in reference [9] takes a value in the interval [1.8, 6.7], and the stability coefficient of the social network unstructured data visualization of the proposed method takes a value in the interval [2.1, 5.5]. It can be seen that, compared with the method of reference [8] and the method of reference [9], the stability coefficient of unstructured social network data visualization of the proposed method takes a value in the interval κ � [2,6], which can effectively improve the visualization of social network unstructured data stability.…”

Section: Visual Stability Analysismentioning

confidence: 99%

“…e platform hopes to obtain useful information from it, but the original data are difficult to understand directly. Data visualization technology can quickly express a wide range of data and visualize information, to reduce the cognitive difficulty of data and help people understand data [5][6][7]. How to use visualization technology to quickly and effectively find useful data from huge and complex social network data and transform it into easy-to-understand image information, to serve users and platform managers, has attracted great attention in the industry.…”

Section: Introductionmentioning

confidence: 99%

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Visual Dynamic Simulation Model of Unstructured Data in Social Networks

Zhang

2022

Security and Communication Networks

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Social networks contain a large amount of unstructured data. To ensure the stability of unstructured big data, this study proposes a method for visual dynamic simulation model of unstructured data in social networks. This study uses the Hadoop platform and data visualization technology to establish a univariate linear regression model according to the time correlation between data, estimates and approximates perceptual data, and collects unstructured data of social networks. Then, the unstructured data collected from the original social network are processed, and an adaptive threshold is designed to filter out the influence of noise. The unstructured data of social network after feature analysis are processed to extract its visual features. Finally, this study carries out the Hadoop cluster design, implements data persistence by HDFS, uses MapReduce to extract data clusters for distributed computing, builds a visual dynamic simulation model of unstructured data in social network, and realizes the display of unstructured data in social network. The experimental results show that this method has a good visualization effect on unstructured data in social networks and can effectively improve the stability and efficiency of unstructured data visualization in social networks.

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Section: Visual Stability Analysismentioning

confidence: 99%

Section: Visual Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visual Dynamic Simulation Model of Unstructured Data in Social Networks

Zhang

2022

Security and Communication Networks

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“…In terms of external intrusion identification, Pan et al 27 designed a dynamic residual generator to detect a variety of attack methods through an entity behavioral analysis filtering device, which solved the problem of a few types of abnormal detection in static detection models. Some scholars have also established an anomaly detection model to detect Cyber attacks based on CNN 28,29 , RNN 30 , long short-term memory (LSTM) [31][32][33] , GAN 34,35 , and deep autoencoder (DAE) 36,37 . In terms of insider threat identification, the research focuses on the analysis of enterprise employee behavior 38 , user portrait 39,40 , complex behavior modeling 41 , and so on.…”

Section: Anomaly Behavior Detectionmentioning

confidence: 99%

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

Cui

Zhang

Chen

et al. 2022

Sci Rep

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User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

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An Open-Source Framework Unifying Stream and Batch Processing

Deshpande

Rao

2022

Inventive Computation and Information Technologies

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Cyberattack detection model using deep learning in a network log system with data visualization

Cited by 12 publications

References 32 publications

Visual Dynamic Simulation Model of Unstructured Data in Social Networks

Visual Dynamic Simulation Model of Unstructured Data in Social Networks

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

An Open-Source Framework Unifying Stream and Batch Processing

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