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Journal articles on the topic 'Network behavior detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

BOBROVNIKOVA, K., and D. DENYSIUK. "METHOD FOR MALWARE DETECTION BASED ON THE NETWORK TRAFFIC ANALYSIS AND SOFTWARE BEHAVIOR IN COMPUTER SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 287, no. 4 (2020): 7–11. https://doi.org/10.31891/2307-5732-2020-287-4-7-11.

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The paper presents a method for malware detection by analyzing network traffic and software behavior in computer systems. The method is based on the classification of API call sets extracted from the constructed control flow graphs for software applications, and based on the analysis of DNS traffic of the computer network. As a classifier a combination of deep neural network and recurrent neural network is used. The proposed method consists of two stages: the deep neural network and the recurrent neural network learning stage and the malware detecting stage. The steps of the malware detecting

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Zeng, Huiqun, and Huiqian Chen. "Network Intrusion Detection based on LSTM." Frontiers in Science and Engineering 4, no. 9 (2024): 131–37. http://dx.doi.org/10.54691/p4w71z56.

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Network intrusion detection, as an important means of ensuring daily network security, its accuracy and response speed are crucial for defending against network attacks. This article explores and implements deep learning based network intrusion detection techniques, particularly the application of Long Short Term Memory (LSTM) networks in detecting network intrusion behavior. The aim is to solve the problems of gradient vanishing and exploding in traditional RNNs, improve the emergency response capability of network systems, and enhance the reliability and security of networks. The study used

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Rahman, Atta-ur, Maqsood Mahmud, Tahir Iqbal, et al. "Network Anomaly Detection in 5G Networks." Mathematical Modelling of Engineering Problems 9, no. 2 (2022): 397–404. http://dx.doi.org/10.18280/mmep.090213.

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On the telecommunications front, 5G is the fifth-generation technology standard for broadband cellular networks, which is a replacement for the 4G networks used by most current phones. Hundreds of businesses, organizations, and governments suffer from cyberattacks that compromise sensitive information in which 5G is one of them. Those breaches of the data would not have occurred if there is a way to detect strange behaviors in a 5G network, and this is what this paper presenting. Network Anomaly Detection (NAD) in 5G is a way to observe the network constantly to detect any unusual behavior. Ho

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Wei-Yi Jing, Wei-Yi Jing, Zhong-Jie Zhu Wei-Yi Jing, Yong-Qiang Bai Zhong-Jie Zhu, Long Li Yong-Qiang Bai, Wei-Feng Cui Long Li, and Wen-Bo Yu Wei-Feng Cui. "Violation Behavior Detection for Non-motor Vehicles." 電腦學刊 34, no. 1 (2023): 175–86. http://dx.doi.org/10.53106/199115992023023401013.

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<p>Non-motor vehicles are widely used in the urban and rural transportation system for their portability, but the related violations also occur frequently and are difficult to be supervised intelligently, considering their colossal quantity, various styles, and small volumes. To solve this problem, this paper presents a non-motor vehicle violation detection algorithm with efficient target detection and deliberate logical calculation. A target detection network with high speed and accuracy is constructed firstly by fusing two different types of attention mechanism. Specifically, the Squee

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Jasmin Salma, S., and B. Aysha Banu. "Revealing of Reducing Manners in Ad Hoc Networks with Crosslayer Approach Using SVM and FDA in Distributed Architecture." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 76–79. http://dx.doi.org/10.51983/ajcst-2012.1.1.1666.

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Ad hoc network is a structure less network with independent nodes. In the ad hoc network, the nodes have to cooperate for services like routing and data forwarding. The routing attacks in ad hoc networks have given rise to the need for designing novel intrusion detection algorithms, different from those present in conventional networks. In this work, distributed intrusion detection system (IDS) have proposed for detecting malicious sinking behavior in ad hoc network. Detection process of that sinking behavior node is very important to do the further forwarding process in network. Intrusion det

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Qu, Zhe, Lizhen Cui, and Xiaohui Yang. "HAR-Net: An Hourglass Attention ResNet Network for Dangerous Driving Behavior Detection." Electronics 13, no. 6 (2024): 1019. http://dx.doi.org/10.3390/electronics13061019.

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Ensuring safety while driving relies heavily on normal driving behavior, making the timely detection of dangerous driving patterns crucial. In this paper, an Hourglass Attention ResNet Network (HAR-Net) is proposed to detect dangerous driving behavior. Uniquely, we separately input optical flow data, RGB data, and RGBD data into the network for spatial–temporal fusion. In the spatial fusion part, we combine ResNet-50 and the hourglass network as the backbone of CenterNet. To improve the accuracy, we add the attention mechanism to the network and integrate center loss into the original Softmax

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Shrikant, Vanve* Prof. Sarita Patil. "OGEDIDS: OPPOSITIONAL GENETIC PROGRAMMING ENSEMBLE FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 756–62. https://doi.org/10.5281/zenodo.57737.

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Due to the wide range application of internet and computer networks, the securing of information is indispensable one. In order to secure the information system more effectively, various distributed intrusion detection has been developed in the literature. In this paper, we utilize the oppositional genetic algorithm for Distributed Network Intrusion Detection utilizing the oppositional set based population selection mechanism. This system is mostly useful for detecting unauthorized & malicious attack in distributed network. Here, Oppositional genetic algorithm (OGA) is utilized in OGA ense

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Parres-Peredo, Alvaro, Ivan Piza-Davila, and Francisco Cervantes. "Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity." Applied Sciences 9, no. 20 (2019): 4381. http://dx.doi.org/10.3390/app9204381.

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Anomaly-based intrusion detection systems use profiles to characterize expected behavior of network users. Most of these systems characterize the entire network traffic within a single profile. This work proposes a user-level anomaly-based intrusion detection methodology using only the user’s network traffic. The proposed profile is a collection of TopK rankings of reached services by the user. To detect unexpected behaviors, the real-time traffic is organized into TopK rankings and compared to the profile using similarity measures. The experiments demonstrated that the proposed methodology wa

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Mohan, Mr B. "AN ADVANCED APPROACH FOR DETECTING BEHAVIOR BASED INTRANET ATTACKS BY MACHINE LEARNING." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45158.

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In the realm of cybersecurity, the detection of intranet attacks poses a significant challenge due to the evolving nature of malicious behaviors. This paper proposes an advanced approach for detecting behavior-based intranet attacks utilizing machine learning techniques. By leveraging the power of machine learning algorithms, the proposed approach aims to effectively identify and mitigate intranet attacks based on their behavioral patterns. Through the analysis of network traffic and system logs, the model learns to distinguish between normal and anomalous behaviors, thereby enabling proactive

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Meng, Yongwei, Tao Qin, Shancang Li, and Pinghui Wang. "Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection." Security and Communication Networks 2022 (June 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/9139321.

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Accurately detecting and identifying abnormal behaviors on the Internet are a challenging task. In this work, an anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns. Then, anomaly detection is conducted by analyzing the residual matrix. By analyzing network traffic and anomaly characteristics, we construct the behavior attribute matrix, which incorporates seven features that characterize user behavior patterns. To include the effects of network environment, we employ the similarity between IP addresses to f

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Li, Yinjia, Zeyuan Hu, Yixi Zhang, Jihang Liu, Wan Tu, and Hong Yu. "DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments." Fishes 9, no. 6 (2024): 242. http://dx.doi.org/10.3390/fishes9060242.

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Accurately detecting and counting abnormal fish behaviors in aquaculture is essential. Timely detection allows farmers to take swift action to protect fish health and prevent economic losses. This paper proposes an enhanced high-precision detection algorithm based on YOLOv9, named DDEYOLOv9, to facilitate the detection and counting of abnormal fish behavior in industrial aquaculture environments. To address the lack of publicly available datasets on abnormal behavior in fish, we created the “Abnormal Behavior Dataset of Takifugu rubripes”, which includes five categories of fish behaviors. The

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Xuan, Cho Do, Duc Duong, and Hoang Xuan Dau. "A multi-layer approach for advanced persistent threat detection using machine learning based on network traffic." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11311–29. http://dx.doi.org/10.3233/jifs-202465.

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Advanced Persistent Threat (APT) is a dangerous network attack method that is widely used by attackers nowadays. During the APT attack process, attackers often use advanced techniques and tools, thus, causing many difficulties for information security systems. In fact, to detect the APT attacks, intrusion detection systems cannot rely on one technique or method but often combine multiple techniques and methods. In addition, the approach for APT attack detection using behavior analysis and evaluation techniques is facing many difficulties due to the lack of characteristic data of attack campaig

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Peng, Shuyun, Xiaopei Zhang, Luoyu Zhou, and Peng Wang. "YOLO-CBD: Classroom Behavior Detection Method Based on Behavior Feature Extraction and Aggregation." Sensors 25, no. 10 (2025): 3073. https://doi.org/10.3390/s25103073.

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Classroom behavior can effectively reflect learning states, and thus classroom behavior detection is crucial for improving teaching methods and enhancing teaching quality. To address issues such as severe occlusions and large scale variations in student behavior detection, this paper proposes a classroom behavior detection model, named YOLO-CBD (YOLOv10s Classroom Behavior Detection). Firstly, BiFormer attention is introduced to redesign the Efficientv2 network, leading to a novel backbone network for efficient feature extraction of student classroom behaviors. The proposed attention module en

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Feng, Yan, Zhihai Yang, Qindong Sun, and Yanxiao Liu. "SEDAT: A Stacked Ensemble Learning-Based Detection Model for Multiscale Network Attacks." Electronics 13, no. 15 (2024): 2953. http://dx.doi.org/10.3390/electronics13152953.

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Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on pr

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Guo, Dajun. "Application of Big Data Analysis and Cloud Computing Technology." International Journal of Grid and High Performance Computing 16, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijghpc.349891.

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While networks bring convenience to people, more attention must be paid to the security of the network platform. This study combines big-data technology and machine learning (ML) to investigate the application of big-data analysis and cloud-computing technology in network security. First, the data-collection technology of abnormal network behavior is introduced, and the Flume data-collection component and Kafka distributed technology are discussed. Second, the data-processing process of abnormal network behavior and the corresponding algorithm processing are analyzed. Finally, an abnormal netw

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S.Sankar Ganesh. "Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms." Journal of Electrical Systems 20, no. 5s (2024): 2078–85. http://dx.doi.org/10.52783/jes.2545.

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As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detec

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S.Sankar Ganesh. "Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms." Journal of Electrical Systems 20, no. 5s (2024): 2034–41. http://dx.doi.org/10.52783/jes.2540.

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As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detec

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Xie, Bin, Xinyu Dong, and Changguang Wang. "An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning." Wireless Communications and Mobile Computing 2021 (August 4, 2021): 1–15. http://dx.doi.org/10.1155/2021/9322368.

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The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each partici

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Wang, Zhifeng, Minghui Wang, Chunyan Zeng, and Longlong Li. "SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms." Applied Sciences 14, no. 18 (2024): 8357. http://dx.doi.org/10.3390/app14188357.

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Detecting student behavior in smart classrooms is a critical area of research in educational technology that significantly enhances teaching quality and student engagement. This paper introduces an innovative approach using advanced computer vision and artificial intelligence technologies to monitor and analyze student behavior in real time. Such monitoring assists educators in adjusting their teaching strategies effectively, thereby optimizing classroom instruction. However, the application of this technology faces substantial challenges, including the variability in student sizes, the divers

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Haseeb-ur-rehman, Rana M. Abdul, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, et al. "High-Speed Network DDoS Attack Detection: A Survey." Sensors 23, no. 15 (2023): 6850. http://dx.doi.org/10.3390/s23156850.

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting

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Jayashree, Devasagayam, V. Uma Rani, and K. Soma Sundaram. "Trust Based Misbehavior Detection in Wireless Sensor Networks." Applied Mechanics and Materials 622 (August 2014): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amm.622.191.

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Due to emerging technology Wireless Sensor Network (WSN), it is necessary to monitor the behavior of sensor nodes and establish the secure communication in network. Security is a challenging task in wireless environment. Several encryption mechanisms are available to prevent outsider attacks, but no mechanism available for insider attacks. A trust model is a collection of rules used to establish co-operation or collaboration among nodes as well as monitoring misbehavior of wireless sensor networks. Trust model is necessary to enhance secure localization, communication or routing, aggregation,

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Qin, Zhi-Quan, Hong-Zuo Xu, Xing-Kong Ma, and Yong-Jun Wang. "Interaction Context-Aware Network Behavior Anomaly Detection for Discovering Unknown Attacks." Security and Communication Networks 2022 (April 11, 2022): 1–24. http://dx.doi.org/10.1155/2022/3595304.

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Network behavior anomaly detection is an effective approach to discover unknown attacks, where generating high-efficacy network behavior representation is one of the most crucial parts. Nowadays, complicated network environments and advancing attack techniques make it more challenging. Existing methods cannot yield satisfied representations that express the semantics of network behaviors comprehensively. To tackle this problem, we propose XNBAD, a novel unsupervised network behavior anomaly detection framework, in this work. It integrates the timely high-order host states under the dynamic int

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Wang, Zhifeng, Jialong Yao, Chunyan Zeng, Longlong Li, and Cheng Tan. "Students’ Classroom Behavior Detection System Incorporating Deformable DETR with Swin Transformer and Light-Weight Feature Pyramid Network." Systems 11, no. 7 (2023): 372. http://dx.doi.org/10.3390/systems11070372.

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Artificial intelligence (AI) and computer vision technologies have gained significant prominence in the field of education. These technologies enable the detection and analysis of students’ classroom behaviors, providing valuable insights for assessing individual concentration levels. However, the accuracy of target detection methods based on Convolutional Neural Networks (CNNs) can be compromised in classrooms with multiple targets and varying scales, as convolutional operations may result in the loss of location information. In contrast, transformers, which leverage attention mechanisms, hav

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Zhang, Shuzhuang, Yingjun Qiu, Hao Luo, and Zhigang Wu. "Application Communities Detection in Network." Applied Sciences 9, no. 1 (2018): 31. http://dx.doi.org/10.3390/app9010031.

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The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication

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Qi, Haixia, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang, and Xiwen Luo. "Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer." Applied Sciences 15, no. 6 (2025): 3333. https://doi.org/10.3390/app15063333.

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Detecting the daily behavior of broiler chickens allows early detection of irregular activity patterns and, thus, problems in the flock. In an attempt to resolve the problems of the slow detection speed, low accuracy, and poor generalization ability of traditional detection models in the actual breeding environment, we propose a chicken behavior detection method called FCBD-DETR (Faster Chicken Behavior Detection Transformer). The FasterNet network based on partial convolution (PConv) was used to replace the Resnet18 backbone network to reduce the computational complexity of the model and to i

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Rustamiy, Dr Salima, Nargis Kurbanazarova, Gulbakhor Atayeva, et al. "Multi-Scale Attention-based Wireless Network Algorithm for Enhancing Language Learning Outcomes." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 94–103. https://doi.org/10.58346/jowua.2025.i1.005.

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Identifying students' behaviors in language learning classrooms can serve as a criterion for evaluating the efficacy of instructional methods. This research introduces an algorithm for detecting language learning classroom behavior with an enhanced object detection framework (i.e., YOLOv5) through wireless networks. The feature pyramidal framework in the cervical network of the initial YOLOv5 system is integrated with a balanced bidirectional feature pyramidal network. Their subsequent processing involves feature fusion across several object sizes to extract fine-grained characteristics of dis

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Chugh, Neeraj, Geetam Singh Tomar, Robin Singh Bhadoria, and Neetesh Saxena. "A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks." Electronics 10, no. 14 (2021): 1635. http://dx.doi.org/10.3390/electronics10141635.

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To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing pro

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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayakamurthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec

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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayaka Murthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577–86. https://doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec

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K, Rajesh Kumar. "DETECTION OF WIRELESS NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33590.

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The security and integrity of computer networks are seriously threatened by network assaults. Keeping a safe network environment requires the capacity to anticipate and stop these threats. Supervised machine learning methods have become powerful instruments for attacking network traffic and spotting patterns that point to malicious behaviour. We provide an in-depth examination of supervised machine learning methods for network attack prediction. We gather the data, preprocess it, extract pertinent features, and structure it so that machine learning algorithms may use it.We assess these algorit

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Li, Tao, Wenzhe Dong, Aiqun Hu, and Jinguang Han. "Task-Oriented Network Abnormal Behavior Detection Method." Security and Communication Networks 2022 (June 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/3105291.

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Since network systems have become increasingly large and complex, the limitations of traditional abnormal packet detection have gradually emerged. The existing detection methods mainly rely on the recognition of packet features, which lack the association of specific applications and result in hysteresis and inaccurate judgement. In this paper, a task-oriented abnormal packet behavior detection method is proposed, which creatively collects action identifications during the execution of network tasks and inserts security labels into communication packets. Specifically, this paper defines the ne

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He, Yukun, Qiang Li, Jian Cao, Yuede Ji, and Dong Guo. "Understanding socialbot behavior on end hosts." International Journal of Distributed Sensor Networks 13, no. 2 (2017): 155014771769417. http://dx.doi.org/10.1177/1550147717694170.

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Server-side socialbot detection approaches can identify malicious accounts and spams in online social networks. However, they cannot detect socialbot processes, residing on user hosts, which control these accounts. Therefore, new approaches are needed to detect socialbots on hosts. The fundamental to design host-side detecting approaches is to gain an insight into the behaviors of socialbots on host. In this article, we analyzed a series of representative socialbots in depth and summarized the typical features of socialbot behaviors. We proposed a new approach to defense against socialbots on

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Wang, Yonghui, Mengjie Wang, and Qingquan Meng. "Video based behavior detection algorithm." Journal of Physics: Conference Series 2504, no. 1 (2023): 012024. http://dx.doi.org/10.1088/1742-6596/2504/1/012024.

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Abstract Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is cond

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Hong, Ni, Xuefeng Wang, and Zhonghua Wang. "Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities." Mobile Information Systems 2021 (April 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/9977736.

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In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and

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Maulana, Asep, and Martin Atzmueller. "Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks." Applied Sciences 11, no. 9 (2021): 4005. http://dx.doi.org/10.3390/app11094005.

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Anomaly detection in complex networks is an important and challenging task in many application domains. Examples include analysis and sensemaking in human interactions, e.g., in (social) interaction networks, as well as the analysis of the behavior of complex technical and cyber-physical systems such as suspicious transactions/behavior in financial or routing networks; here, behavior and/or interactions typically also occur on different levels and layers. In this paper, we focus on detecting anomalies in such complex networks. In particular, we focus on multi-layer complex networks, where we c

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Tatarnikova, Tatiana, and Pavel Bogdanov. "Intrusion detection in internet of things networks based on machine learning methods." Information and Control Systems, no. 6 (December 16, 2021): 42–52. http://dx.doi.org/10.31799/1684-8853-2021-6-42-52.

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Introduction: The growing amount of digital data generated, among others, by smart devices of the Internet of Things makes it important to study the application of machine learning methods to the detection of network traffic anomalies, namely the presence of network attacks. Purpose: To propose a unified approach to detecting attacks at different levels of IoT network architecture, based on machine learning methods. Results: It was shown that at the wireless sensor network level, attack detection is associated with the detection of anomalous behavior of IoT devices, when the deviation of an Io

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Liu, Shida, Xuyun Wang, Honghai Ji, Li Wang, and Zhongsheng Hou. "A Novel Driver Abnormal Behavior Recognition and Analysis Strategy and Its Application in a Practical Vehicle." Symmetry 14, no. 10 (2022): 1956. http://dx.doi.org/10.3390/sym14101956.

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In this work, a novel driver abnormal behavior analysis system based on practical facial landmark detection (PFLD) and you only look once version 5 (YOLOv5) were developed to solve the recognition and analysis of driver abnormal behaviors. First, a library for analyzing the abnormal behavior of vehicle drivers was designed, in which the factors that cause an abnormal behavior of drivers were divided into three categories according to the behavioral characteristics including natural behavioral factors, unnatural behavioral factors, and passive behavioral factors. Then, different neural network

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He, Mingshu, Xiaojuan Wang, Junhua Zhou, Yuanyuan Xi, Lei Jin, and Xinlei Wang. "Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection." Security and Communication Networks 2021 (March 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/6659022.

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With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly dete

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Han, Zhen-Hui, Xing-Shu Chen, Xue-Mei Zeng, Yi Zhu, and Ming-Yong Yin. "Detecting Proxy User Based on Communication Behavior Portrait." Computer Journal 62, no. 12 (2019): 1777–92. http://dx.doi.org/10.1093/comjnl/bxz065.

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Abstract Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it

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Chen, Binbin, Xiuhui Wang, Qifu Bao, Bo Jia, Xuesheng Li, and Yaru Wang. "An Unsafe Behavior Detection Method Based on Improved YOLO Framework." Electronics 11, no. 12 (2022): 1912. http://dx.doi.org/10.3390/electronics11121912.

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In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce

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Hiba, Fathima K. P.* Anugraha P. P. "A Review on Network Intrusion Detection." International Journal of Scientific Research and Technology 2, no. 1 (2025): 60–66. https://doi.org/10.5281/zenodo.14598998.

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Network intrusion detection is a critical component of cybersecurity, aimed at safeguarding networks from unautho- rized access and malicious activities. This review paper provides an extensive examination of the current landscape of network intrusion detection techniques, encompassing both signature- based and anomaly-based approaches.The review delves into the intricacies of signature-based methods, which rely on predefined patterns and signatures to identify known threats. It analyzes the strengths and limitations of this approach, exploring recent ad- vancements in signature-based detectio

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Shou, Zhaoyu, Mingbang Yan, Hui Wen, Jinghua Liu, Jianwen Mo, and Huibing Zhang. "Research on Students’ Action Behavior Recognition Method Based on Classroom Time-Series Images." Applied Sciences 13, no. 18 (2023): 10426. http://dx.doi.org/10.3390/app131810426.

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Students’ action behavior performance is an important part of classroom teaching evaluation. To detect the action behavior of students in classroom teaching videos, and based on the detection results, the action behavior sequence of individual students in the teaching time of knowledge points is obtained and analyzed. This paper proposes a method for recognizing students’ action behaviors based on classroom time-series images. First, we propose an improved Asynchronous Interaction Aggregation (AIA) network for student action behavior detection. By adding a Multi-scale Temporal Attention (MsTA)

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R.Surya Prabha, S.Saraswathi. "Enhancing MANET Security: A Collaborative Dynamic Multi-Agent Approach for Wormhole Attack Detection and Mitigation (CDMA-Worm)." Journal of Information Systems Engineering and Management 10, no. 15s (2025): 528–43. https://doi.org/10.52783/jisem.v10i15s.2491.

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The proposed research focus on the Collaborative Dynamic Multi-Agent Wormhole Detection and Anomaly Mitigation Framework for Secure Networks (CDMA-Worm). It is a comprehensive solution designed to protect large dynamic networks from wormhole attacks and other malicious activities. The architecture is built around three key algorithms namely- Dynamic Multi-Agent Generation and Broadcasting (DMGB), Anomaly Detection and Isolation (ADI), and Collaborative Wormhole Detection and Network-Wide Threat Mitigation(CWD-NWTM). The first algorithm, Dynamic Multi-Agent Generation and Broadcasting, focuses

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Namayanja, Josephine M., and Vandana P. Janeja. "Change Detection in Large Evolving Networks." International Journal of Data Warehousing and Mining 15, no. 2 (2019): 62–79. http://dx.doi.org/10.4018/ijdwm.2019040104.

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This article presents a novel technique for the detection of change in massive evolving communication networks. This approach utilizes a novel hybrid sampling methodology to select central nodes and key subgraphs from networks over time. The objective is to select and utilize a much smaller targeted sample of the network, represented as a graph, without loss of any knowledge derived from graph properties as compared to the entire massive graph. This article uses the targeted samples to detect micro- and macro-level changes in the network. This approach can be potentially useful in the domain o

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Zhao, Ying, Junjun Chen, Di Wu, et al. "Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern." Information 10, no. 8 (2019): 262. http://dx.doi.org/10.3390/info10080262.

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Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distingu

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Li, Jun, Wentao Jiang, Jianyi Zhang, Yanhua Shao, and Wei Zhu. "Fake User Detection Based on Multi-Model Joint Representation." Information 15, no. 5 (2024): 266. http://dx.doi.org/10.3390/info15050266.

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The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by an

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Tang, Longyu, Tao Xie, Yunong Yang, and Hong Wang. "Classroom Behavior Detection Based on Improved YOLOv5 Algorithm Combining Multi-Scale Feature Fusion and Attention Mechanism." Applied Sciences 12, no. 13 (2022): 6790. http://dx.doi.org/10.3390/app12136790.

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The detection of students’ behaviors in classroom can provide a guideline for assessing the effectiveness of classroom teaching. This study proposes a classroom behavior detection algorithm using an improved object detection model (i.e., YOLOv5). First, the feature pyramid structure (FPN+PAN) in the neck network of the original YOLOv5 model is combined with a weighted bidirectional feature pyramid network (BiFPN). They are subsequently processed with feature fusion of different scales of the object to mine the fine-grained features of different behaviors. Second, a spatial and channel convolut

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Zhang, Bing, Zhiyang Liu, Yanguo Jia, Jiadong Ren, and Xiaolin Zhao. "Network Intrusion Detection Method Based on PCA and Bayes Algorithm." Security and Communication Networks 2018 (November 13, 2018): 1–11. http://dx.doi.org/10.1155/2018/1914980.

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Intrusion detection refers to monitoring network data information, quickly detecting intrusion behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion detection methods are mainly focused on rule files and data mining. They have the disadvantage of not being able to detect new types of attacks and have the slow detection speed. To address these issues, an intrusion detection method based on improved PCA combined with Gaussian Naive Bayes was proposed. By weighting the first few feature vectors of the traditional PCA, data pollution can be reduced. The number

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Xu, Yalei, Jing Nie, Honglei Cen, et al. "An Image Detection Model for Aggressive Behavior of Group Sheep." Animals 13, no. 23 (2023): 3688. http://dx.doi.org/10.3390/ani13233688.

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Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These

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Tsai, Wen-Chia, Jhih-Sheng Lai, Kuan-Chou Chen, Vinay M.Shivanna, and Jiun-In Guo. "A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications." Electronics 10, no. 6 (2021): 692. http://dx.doi.org/10.3390/electronics10060692.

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This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the Y

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