Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques by Mohamed Ali Kazi

“…These feature selection algorithms are also compared with a manual feature selection approach to determine whether a manual, automated, or hybrid feature selection approach would be more suitable for this type of problem.…”
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Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions by A. N. Borodulina, E. V. Mikhalkova

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DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  …”
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Improving Image Spam Detection Using a New Image Texture Features Selection by Azam Shekari Shahrak, Seyed javad Mirabedini, Nasser Mikaeilvand, Seyed Hamid Haj Seyed Javadi

“…The purpose of the research is to use image texture features to detect image spam. So far, 22 features of image texture have not been used in one place to detect image spam. …”
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Robust tampering detection and localization of composite image by Xiao WANG, Rong ZHANG, Lijun GUO

“…Aiming at the problem of tamper detection of composite image of natural images and highly simulated computer-generated images,a method of extracting image block color and texture feature based on differential histogram and local binary texture descriptor in YCbCr color space was proposed.By training posterior probability support vector machine,the image block to be measured was identified.In the case of non-overlapping block,the approximate tampering area was general judged,then the block was discriminated by pixel in the region,ultimately the accurate location of tampering area was achieved.The experimental results show that the recognition rate of 128 dpi×128 dpi image blocks is 94.75%,which is higher than other methods.The tapering region of the synthesized image can be precisely positioned,and the rotation and scaling operation show good coercivity.…”
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VMC-Net: multi-scale feature aggregation and distribution with contextual attention guided fusion for aerial object detection by Haodong Li, Haicheng Qu

“…However, this task faces many challenges such as small object size and complex background, which increase the difficulty of detection. Existing methods usually use multi-scale feature fusion or attention mechanism to improve performance, but they often ignore the role of object feature perception in the image and have problems such as insufficient use of context information. …”
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Shot boundary detection algorithm based on ORB by Jiang-qi TANG, Lin-jiang XIE, Qing-sheng YUAN, Dong-ming ZHANG, Xiu-guo BAO, Wei Guo

“…Experi-ment results show that the proposed algorithm is effective to solve false and miss detection caused by the above problems, with a sharp rise in procession speed.…”
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Grape cluster detection based on spatial-to-depth convolution and attention mechanism by Shuai Rong, Xinghai Kong, Ruibo Gao, Zhiwei Hu, Hua Yang

“…Secondly, based on the problem of information loss in grape cluster detection, a plug-and-play module of spatial-to-depth convolution (STD-Conv) is added to enrich grape cluster feature information. …”
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A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

“…To address the above problems, a lightweight personnel detection method YOLOv5-CWG is proposed for underground coal mine based on YOLOv5.Firstly, the coordinate attention mechanism (Coordinate Attention) embedded in the backbone network adaptively adjusts the weights of each channel in the feature map to enhance the expression ability of the features. …”
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Employing combined spatial and frequency domain image features for machine learning-based malware detection by Abul Bashar

“…However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. …”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Research on traffic representation in network anomaly detection by SUN Jianwen, ZHANG Bin, CHANG Heyu

“…Aiming to address the problem of information loss in traffic representation for network anomaly detection, the impact of feature information dimension of different traffic representation on anomaly detection performance was analyzed from the perspective of data collection granularity. …”
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A novel similarity-constrained feature selection method for epilepsy detection via EEG signals by Chunlei Shi, Jun Gao, Jian Yu, Lingzhi Zhao, Faxian Jia

“…Then, an optimization problem for feature selection is formulated by enhancing intra-class similarity and reducing inter-class similarity. …”
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Application of Permutation Entropy in Feature Extraction for Near-Infrared Spectroscopy Noninvasive Blood Glucose Detection by Xiaoli Li, Chengwei Li

“…Finally, the support vector regression and partial least square regression are used to establish the mathematical model between the characteristic spectral data and glucose concentration, and the results are compared with conventional feature extraction methods. Results show that the proposed new method can extract useful information from near-infrared spectra, effectively solve the problem of characteristic wave band extraction, and improve the analytical accuracy of spectral and model stability.…”
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An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network by Qingfeng Li, Bo Li, Linzhi Wen

“…The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. …”
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Anchor-Free Object Detection Algorithm Based on Dual-Angle Multi-Scale Feature Fusion by WANG Xiaoyu, WEI Yuxin, LU Jianyu, YU Yue

“… In response to the problem of insufficient feature extraction and insufficient detection accuracy in the anchor-free frame object detection algorithm CenterNet. …”
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Research on the performance of the SegFormer model with fusion of edge feature extraction for metal corrosion detection by Bingnan Yan, Conghui Wang, Xiaolong Hao

“…In this paper, a SegFormer metal corrosion detection method based on parallel extraction of edge features is proposed. …”
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Edge-Guided Feature Pyramid Networks: An Edge-Guided Model for Enhanced Small Target Detection by Zimeng Liang, Hua Shen

“…The goal is to resolve the problem of missing target information that occurs when Feature Pyramid Networks (FPNs) perform continuous down-sampling to obtain deeper semantic features. …”
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