Cross-Architecture Vulnerability Detection Combining Semantic and Attribute Feature by LI Kun, LI Bin, ZHU Wenjing, ZHOU Qinglei

“…A cross-architecture vulnerability detection method combining semantic and attribute feature is proposed. …”
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Webshell malicious traffic detection method based on multi-feature fusion by Yuan LI, Yunpeng WANG, Tao LI, Baoqiang MA

“…Webshell is the most common malicious backdoor program for persistent control of Web application systems, which poses a huge threat to the safe operation of Web servers.For most Webshell detection method based on the request packet data for training, the method for web-based Webshell recognition effect is poorer, and the model of training efficiency is low.In response to the above problems, a Webshell malicious traffic detection method based on multi-feature fusion was proposed.The method was characterized by the three dimensions of Webshell packet meta information, packet payload content and traffic access behavior.Combining domain knowledge, feature extraction of request and response packets in the data stream.Transformed into feature extraction information for information fusion, forming a discriminant model that could detect different types of attacks.Compared with the previous research method, the accuracy rate of the method here in the two classification of normal and malicious traffic has been improved to 99.25%.The training efficiency and detection efficiency have also been significantly improved, and the training time and detection time have been reduced by 95.73% and 86.14%.…”
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Bacterial Disease Detection of Cherry Plant Using Deep Features by Hatice Kayhan, Emrah Dönmez, Yavuz Ünal

“…These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. …”
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Impact of Lexical Features on Answer Detection Model in Discussion Forums by Atif Khan, Muhammad Adnan Gul, Abdullah Alharbi, M. Irfan Uddin, Shaukat Ali, Bader Alouffi

“…However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. …”
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Robust Resilience Blocks Detection Problem in Dynamic Social Networks by Yuxin Gao, Jianming Zhu, Peikun Ni, Guoqing Wang

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Object detection model design for tiny road surface damage by Chenguang Wu, Min Ye, Hongwei Li, Jiale Zhang

“…Firstly, a backbone applied to road surface damage feature extraction is designed to solve the problems of feature loss and insufficient extraction of tiny damage during feature extraction. …”
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Exploiting Full-Scale Feature for Remote Sensing Object Detection Based on Refined Feature Mining and Adaptive Fusion by Honghui Xu, Xinqing Wang, Ting Rui, Baoguo Duan, Dong Wang

“…Object detection for remote sensing images remains a challenging problem. …”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection by Dhani Ariatmanto, Anggi Muhammad Rifai

“…This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. …”
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Surface Defect Detection Based on Adaptive Multi-Scale Feature Fusion by Guochen Wen, Li Cheng, Haiwen Yuan, Xuan Li

“…However, the diversity of defects and the presence of complex backgrounds bring significant challenges to salient object detection. To this end, this study proposes a new adaptive multi-scale feature fusion network (AMSFF-Net) to solve the SOD problem of object surface defects. …”
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Infrared dim tiny-sized target detection based on feature fusion by Peng Zhang, Yaman Jing, Guodong Liu, Ziyang Chen, Xiaoyan Wu, Osami Sasaki, Jixiong Pu

“…These two main modules are employed as the core to form a feature fusion network to realize the detection of infrared dim tiny-sized targets. …”
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Foreground and Background Interaction Fusion Network for Camouflaged Object Detection by WEI Mingjun, LIU Ming, LIU Yazhi, LI Hui

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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

“…To deal with these issues, this paper proposes a novel Multi-scale Feature Fusion (MFF) model for bridge appearance disease detection. …”
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Investigating feature extraction by SIFT methods for prostate cancer early detection by Shadan Mohammed Jihad, Ali A. Alsaud, Firas H. Almukhtar, Shahab Kareem, Raghad Zuhair Yousif

“…This study, therefore, highlights the potential for use in the early detection of prostate cancer with advanced feature extraction methods, such as SIFT, and points toward a very promising direction of further research on applying computer vision techniques to problems in medical diagnostic applications. …”
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Method of detecting IRC Botnet based on the multi-features of traffic flow by Jian-en YAN, Chun-yang YUAN, Hai-yan XU, Zhao-xin ZHANG

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Deep blur detection network with boundary-aware multi-scale features by Xiaoli Sun, Qiwei Wang, Xiujun Zhang, Chen Xu, Weiqiang Zhang

“…To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. …”
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BotCatcher:botnet detection system based on deep learning by Di WU, Binxing FANG, Xiang CUI, Qixu LIU

“…Machine learning technology has wide application in botnet detection.However,with the changes of the forms and command and control mechanisms of botnets,selecting features manually becomes increasingly difficult.To solve this problem,a botnet detection system called BotCatcher based on deep learning was proposed.It automatically extracted features from time and space dimension,and established classifier through multiple neural network constructions.BotCatcher does not depend on any prior knowledge which about the protocol and the topology,and works without manually selecting features.The experimental results show that the proposed model has good performance in botnet detection and has ability to accurately identify botnet traffic .…”
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Straight Line Features Detection and Mosaic of Wind Power Blades Image by MA Baoyan, TANG Lei, ZHAO Jing, HE Yongjun

“…A splicing method based on line features is proposed to solve this problem. This method first detects straight lines in the images of the wind power blade, then selects the deduplication as the feature for image registration, and finally splices the blade image according to translation rotation matrix.Experimental results show that the proposed method shows strong robustness and stability in complex situations such as single structure, small coincidence degree and diverse backgroundstability.…”
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The Problem of Detecting Incitement in Extremist Content (Using Examples from the Internet) by T. V. Berdnikova

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