Long short-term memory-based Malware classification method for information security

2019 ◽  
Vol 77 ◽  
pp. 366-375 ◽  
Author(s):  
Jungho Kang ◽  
Sejun Jang ◽  
Shuyu Li ◽  
Young-Sik Jeong ◽  
Yunsick Sung
Keyword(s):  
Information Security ◽  
Short Term Memory ◽  
Classification Method ◽  
Short Term ◽  
Term Memory ◽  
Malware Classification ◽  
Long Short Term Memory

scholarly journals Massive IoT Malware Classification Method Using Binary Lifting

2022 ◽  
Vol 32 (1) ◽  
pp. 467-481
Author(s):  
Hae-Seon Jeong ◽  
Jin Kwak
Keyword(s):  
Classification Method ◽  
Malware Classification

A Malware Classification Method Based on Generic Malware Information

2015 ◽  
pp. 329-336 ◽  
Author(s):  
Jiyeon Choi ◽  
HeeSeok Kim ◽  
Jangwon Choi ◽  
Jungsuk Song
Keyword(s):  
Classification Method ◽  
Malware Classification

A malware classification method based on memory dump grayscale image

2018 ◽  
Vol 27 ◽  
pp. 30-37 ◽  
Author(s):  
Yusheng Dai ◽  
Hui Li ◽  
Yekui Qian ◽  
Xidong Lu
Keyword(s):  
Classification Method ◽  
Grayscale Image ◽  
Malware Classification ◽  
Memory Dump

A fuzzy weighted c-means classification method for traffic flow state division

2021 ◽  
pp. 2150341
Author(s):  
Liangliang Zhang ◽  
Yuanhua Jia ◽  
Dongye Sun ◽  
Yang Yang
Keyword(s):  
Traffic Flow ◽  
Classification Accuracy ◽  
Euclidean Distance ◽  
Classification Method ◽  
Flow State ◽  
Weight Optimization ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
State Classification ◽  
Fuzzy C Means Clustering

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

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