Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection by Jian Yang, Zixin Tang, Zhenkai Guan, Wenjia Hua, Mingyu Wei, Chunjie Wang, Chenglong Gu

“…Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. …”
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Airport Clearance Detection Based on Vision Transformer and Multi-Scale Feature Fusion by Yutong Chen, Yufen Liu, Zhixiong Guo, Qiang Gao

“…To overcome the defects in detection, this paper proposes an airport clearance detection algorithm based on Vision Transformer and multi-scale feature fusion to address the problems of poor real-time performance, low accuracy, and large parameter quantity in existing airport clearance detection systems. …”
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Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature by Lifang Fu, Shuai Liu

“…In terms of the propagation chain, the research tends to emphasize only the single chain from the previous communication node, ignoring the intricate communication chain and the mutual influence relationship among users. To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). …”
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Partial feature reparameterization and shallow-level interaction for remote sensing object detection by Minh Tai Pham Nguyen, Quoc Duy Nam Nguyen, Hoang Viet Anh Le, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran

“…To address these problems, this study introduces an efficient one-stage object detector that is designed mainly for detecting objects on remote sensing images, which consists of several innovations. …”
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GFDet: Multi-Level Feature Fusion Network for Caries Detection Using Dental Endoscope Images by Nan Gao, Yukai Li, Peng Chen, Jijun Tang, Tianshuang Liu

“…However, automatically identifying dental caries remains challenging due to the uncertainty in size, contrast, low saliency, and high interclass similarity of dental caries. To address these problems, we propose the Global Feature Detector (GFDet) that integrates the proposed Feature Selection Pyramid Network (FSPN) and Adaptive Assignment-Balanced Mechanism (AABM). …”
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Infrared Small Target Detection Based on Density Peak Search and Local Features by Leihong Zhang, Hui Yang, Qinghe Zheng, Yiqiang Zhang, Dawei Zhang

“…In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. …”
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Multi-scale cross-layer fusion and center position network for pedestrian detection by Qian Liu, Youwei Qi, Cunbao Wang

“…A new backbone unit is designed to introduce channel-wise attention into the improved aggregated residual transformations for effective feature extraction. We select suitable anchor setting for pedestrian detection datasets to tackle the problem of dataset difference. …”
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A novel dynamic weighted prediction framework with stability-enhanced dynamic thresholding feature selection for neurodegenerative disease detection using gait features by Diksha Giri, Ranjit Panigrahi, Samrat Singh Bhandari, Moumita Pramanik, Akash Kumar Bhoi, Victor Hugo C. de Albuquerque

“…The DWPF ensemble and SEDT feature selection approach are merged to detect neurodegenerative diseases in gait data. …”
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Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

“…Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. …”
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Unsupervised detection method of RoQ covert attacks based on multilayer features by Jing ZHAO, Jun LI, Chun LONG, Wei WAN, Jinxia WEI, Kai CHEN

“…To solve the problems that RoQ covert attacks are hidden in overwhelming background traffic and difficult to identify, besides the existing samples are scarce and cannot provide large-scale learning data, an unsupervised detection method of RoQ covert attacks based on multilayer features was proposed under the condition of very little prior knowledge.First, considering that most normal flow might interfere with subsequent results, a classification method based on semi-supervised spectral clustering was studied by flow characteristics, so that the proportion of normal samples in the filtered traffic was close to 100%.Secondly, in order to distinguish the nuance between the hidden attack features and normal flow without relying on the attack samples, an unsupervised detection model based on the n-Shapelet subsequence was constructed by packet characteristics, and the subsequences with obvious difference were used, which enabled detection of RoQ convert attacks.Experimental results demonstrate that with only a small number of learning samples, the proposed method has higher precision and recall rate than existing methods, and is robust to evading attacks.…”
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Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features by Hua Yang, Yanjie Lyu, Yunpeng Jiang, Feng Jiang, Taiyong Deng, Lihao Yu, Yuanhao Qiu, Hao Xue, Junying Guo, Zhaoqi Meng

“…To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). …”
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A low illumination target detection method based on a dynamic gradient gain allocation strategy by Zhiqiang Li, Jian Xiang, Jiawen Duan

“…Abstract Current target detection methods perform well under normal lighting conditions; however, they encounter challenges in effectively extracting features, leading to false detections and missed detections in low illumination environments. …”
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Nighttime Vehicle Detection Algorithm Based on Improved YOLOv7 by Fan Zhang

“…Aiming at the problems of low visibility, fuzzy target features and high leakage rate of small targets in nighttime vehicle detection, this paper proposes a nighttime vehicle detection algorithm E-YOLOv7 based on the improved YOLOv7. …”
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WSCDNet: A Window Structural Similarity Guided Deep Feature Recalibration Method for Remote Sensing Image Change Detection by Siming Fu, Sijun Dong, Xiaoliang Meng

“…This enables the model to better capture temporal information during feature extraction. Furthermore, we use a dense ladder-shaped fusion method, making full use of the multilevel feature maps in the feature pyramid. …”
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OW-YOLO: An Improved YOLOv8s Lightweight Detection Method for Obstructed Walnuts by Haoyu Wang, Lijun Yun, Chenggui Yang, Mingjie Wu, Yansong Wang, Zaiqing Chen

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UAV Small Target Detection Model Based on Dual Branches and Adaptive Feature Fusion by Guogang Wang, Mingxing Gao, Yunpeng Liu

“…In order to solve the problem of small and dense targets in drone aerial images, a small target detection model based on dual branches and adaptive feature fusion is proposed. …”
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Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance. by Hootan Kamran, Dionne Aleman, Chris McIntosh, Tom Purdie

“…Unlike its predecessors, our method enhances anomaly detection for RT plan QA without compromising on interpretability-a critical feature in healthcare applications, where understanding and trust in automated decisions are paramount. …”
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Enhancing Facial Feature Detection: Hybrid Active Shape and Active Appearance Model (HASAAM) by Musab Iqtait, Jafar Ababneh, Mohammad Rasmi, Amer Abu-Jassar, Suhaila Abuowaida

“…The aim of the HASAAM integrated fitting model is to find new solutions for the feature identification issue by combining the strengths of the Active Shape Model (ASM) and Active Appearance Model (AAM) to provide unique findings on the feature detection problem. …”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…To meet these demands, this paper proposes DynaOOD-Net, a novel detection framework designed to enhance model generalization performance through dynamic feature integration and energy-balanced regularization strategies. …”
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