A multi-item signal detection theory model for eyewitness identification by Yueran Yang, Janice L. Burke, Justice Healy

“…This paper proposes a multi-item signal detection theory (mSDT) model to help understand the witness decision-making process. …”
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A Classification Data Packets Using the Threshold Method for Detection of DDoS by Sukma Aji, Davito Rasendriya Rizqullah Putra, Imam Riadi, Abdul Fadlil, Muhammad Nur Faiz, Arif Wirawan Muhammad, Santi Purwaningrum, Laura Sari

“…Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.…”
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Dual-modal edible oil impurity dataset for weak feature detection by Huiyu Wang, Qianghua Chen, Jianding Zhao, Liwen Xu, Ming Li, Ying Zhao, Qinpei Zhao, Qin Lu

“…Abstract Edible oil may be mixed with tiny solid impurities like raw material fragments, hair, metal fragments and etc. during the production and manufacturing process. For food safety reasons, these tiny impurities need to be detected in the quality control process. …”
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Detect Windows Code Injection by Cross-validating Stack and VAD Information by ZHAI Jiqiang, HAN Xu, WANG Jiaqian, SUN Haixu, YANG Hailu

“…Then the data is combined with the process VAD structure to detect the function return address and match the file name to locate the injected code. …”
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Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection by Jinfu Gong, Zhen Huang, Zhengye Yang, Xuezhuan Ding, Fanming Li

“…This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. …”
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Identification and evaluation of the effective criteria for detection of congestion in a smart city by Anita Mohanty, Subrat Kumar Mohanty, Bhagyalaxmi Jena, Ambarish G. Mohapatra, Ahmed N. Rashid, Ashish Khanna, Deepak Gupta

“…This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. …”
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Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization by Giulia Veronesi, Nico Curti, Aldo Gardini, Giulia Querzoli, Gastone Castellani, Emi Dika

“…The images were first processed using a multi-resolution image processing pipeline with the aim of segmenting nuclei, evaluating their geometrical and morphological features, as well as their spatial organization. …”
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Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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Rumor detection model with weighted GraphSAGE focusing on node location by Manfu Ma, Cong Zhang, Yong Li, Jiahao Chen, Xuegang Wang

“…Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. …”
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Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data by Bastian Engelmann, Anna-Maria Schmitt, Moritz Heusinger, Vladyslav Borysenko, Niklas Niedner, Jan Schmitt

“…The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. …”
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A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

“…Diagnosing brain tumors process is a time-consuming process requiring the expertise of radiologists. …”
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Leak detection and localization in water distribution systems via multilayer networks by Daniel Barros, Ariele Zanfei, Andrea Menapace, Gustavo Meirelles, Manuel Herrera, Bruno Brentan

“…The detection process involves correlating monitored data to create a temporal graph and classify vertices. …”
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Use of Explainable Artificial Intelligence for Analyzing and Explaining Intrusion Detection Systems by Pamela Hermosilla, Mauricio Díaz, Sebastián Berríos, Héctor Allende-Cid

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Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques by Bibat Thokar, Binod Sapkota, Babu R. Dawadi, Shashidhar R. Joshi

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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

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Universal anomaly detection at the LHC: transforming optimal classifiers and the DDD method by Sascha Caron, José Enrique García Navarro, María Moreno Llácer, Polina Moskvitina, Mats Rovers, Adrián Rubio Jímenez, Roberto Ruiz de Austri, Zhongyi Zhang

“…We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well-established and well-performing method for anomaly detection. …”
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Detection of SSL/TLS protocol attacks based on flow spectrum theory by Shize GUO, Fan ZHANG, Zhuoxue SONG, Ziming ZHAO, Xinjie ZHAO, Xiaojuan WANG, Xiangyang LUO

“…Network attack detection plays a vital role in network security.Existing detection approaches focus on typical attack behaviors, such as Botnets and SQL injection.The widespread use of the SSL/TLS encryption protocol arises some emerging attack strategies against the SSL/TLS protocol.With the network traffic collection environment that built upon the implements of popular SSL/TLS attacks, a network traffic dataset including four SSL/TLS attacks, as well as benign flows was controlled.Considering the problems that limited observability of existing detection and limited separation of the original-flow spatiotemporal domains, a flow spectrum theory was proposed to map the threat behavior in the cyberspace from the original spatiotemporal domain to the transformed domain through the process of “potential change” and obtain the “potential variation spectrum”.The flow spectrum theory is based on a set of separable and observable feature representations to achieve efficient analysis of network flows.The key to the application of flow spectrum theory in actual cyberspace threat behavior detection is to find the potential basis matrix for a specific threat network flow under the condition of a given transformation operator.Since the SSL/TLS protocol has a strong timing relationship and state transition process in the handshake phase, and there are similarities between some SSL/TLS attacks, the detection of SSL/TLS attacks not only needs to consider timing context information, but also needs to consider the high-separation representation of TLS network flows.Based on the flow spectrum theory, the threat template idea was used to extract the potential basis matrix, and the potential basis mapping based on the long-short-term memory unit was used to map the SSL/TLS attack network flow to the flow spectrum domain space.On the self-built SSL/TLS attack network flow data set, the validity of the flow spectrum theory is verified by means of classification performance comparison, potential variation spectrum dimensionality reduction visualization, threat behavior feature weight evaluation, threat behavior spectrum division assessment, and potential variation base matrix heatmap visualization.…”
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Detection algorithm of LSB hidden messages based local image stability by ZHANG Qiu-yu, LIU Hong-guo, YUAN Zhan-ting

“…Aimed at the characteristics of LSB steganogtaphy, an algorithm based on local image stability was proposed.Combined with the idea of pollution data analysis, the secret information was regarded as noise in the process of informa-tion transmission.Then using the noise analysis technique, and selecting appropriate critical point value to achieve the detection purpose of the secret information.The theoretic analysis and experimental results show that detection algorithm advances than traditional algorithm in low embedding rate.…”
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Deep learning-assisted terahertz intelligent detection and identification of cancer tissue by Xingyu Wang, Yafei Xu, Rong Wang, Nuoman Tian, Zhengpeng Zhu, Shuting Fan, Liuyang Zhang, Ruqiang Yan, Xuefeng Chen

“…The cancer identification and diagnosis process are transformed into one end-to-end classification process of THz signals reflected from cancer tissue. …”
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Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features by Hawkar K. Hama, Hamsa D. Majeed, Goran Saman Nariman

“…The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. …”
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