Relevant bibliographies by topics / Network behavior detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network behavior detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Network behavior detection"

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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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Dissertations / Theses on the topic "Network behavior detection"

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Carlsson, Oskar, and Daniel Nabhani. "User and Entity Behavior Anomaly Detection using Network Traffic." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14636.

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Zhou, Mian. "Network Intrusion Detection: Monitoring, Simulation and Visualization." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4063.

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This dissertation presents our work on network intrusion detection and intrusion sim- ulation. The work in intrusion detection consists of two different network anomaly-based approaches. The work in intrusion simulation introduces a model using explicit traffic gen- eration for the packet level traffic simulation. The process of anomaly detection is to first build profiles for the normal network activity and then mark any events or activities that deviate from the normal profiles as suspicious. Based on the different schemes of creating the normal activity profiles, we introduce two approaches
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Alipour, Hamid Reza. "An Anomaly Behavior Analysis Methodology for Network Centric Systems." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/305804.

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Information systems and their services (referred to as cyberspace) are ubiquitous and touch all aspects of our life. With the exponential growth in cyberspace activities, the number and complexity of cyber-attacks have increased significantly due to an increase in the number of applications with vulnerabilities and the number of attackers. Consequently, it becomes extremely critical to develop efficient network Intrusion Detection Systems (IDS) that can mitigate and protect cyberspace resources and services against cyber-attacks. On the other hand, since each network system and application has
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Satam, Pratik. "An Anomaly Behavior Analysis Intrusion Detection System for Wireless Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595654.

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Wireless networks have become ubiquitous, where a wide range of mobile devices are connected to a larger network like the Internet via wireless communications. One widely used wireless communication standard is the IEEE 802.11 protocol, popularly called Wi-Fi. Over the years, the 802.11 has been upgraded to different versions. But most of these upgrades have been focused on the improvement of the throughput of the protocol and not enhancing the security of the protocol, thus leaving the protocol vulnerable to attacks. The goal of this research is to develop and implement an intrusion detection
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GOMES, FERREIRA CARLOS HENRIQUE. "Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2966351.

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Khasgiwala, Jitesh. "Analysis of Time-Based Approach for Detecting Anomalous Network Traffic." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1113583042.

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Ghosh, Dastidar Samanwoy. "Models of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180459585.

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Stafford, John, and John Stafford. "Behavior-based Worm Detection." Thesis, University of Oregon, 2012. http://hdl.handle.net/1794/12341.

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The Internet has become a core component of our lives and businesses. Its reliability and availability are of paramount importance. There are many types of malware that impact the availability of the Internet, including network worms, bot-nets, viruses, etc. Detecting such attacks is a critical component of defending against them. This dissertation focuses on detecting and understanding self-propagating network worms, a type of malware with a proven record of disrupting the Internet. According to
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Farrell, Alan D. (Alan Douglas) Carleton University Dissertation Engineering Electrical. "Detection of abnormal router behaviour in a Wide Area Network." Ottawa, 1993.

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Teknős, Martin. "Rozšíření behaviorální analýzy síťové komunikace určené pro detekci útoků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234931.

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This thesis is focused on network behavior analysis (NBA) designed to detect network attacks. The goal of the thesis is to increase detection accuracy of obfuscated network attacks. Methods and techniques used to detect network attacks and network traffic classification were presented first. Intrusion detection systems (IDS) in terms of their functionality and possible attacks on them are described next. This work also describes principles of selected attacks against IDS. Further, obfuscation methods which can be used to overcome NBA are suggested. The tool for automatic exploitation, attack o
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Books on the topic "Network behavior detection"

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L, Commons Michael, and Symposium on Quantitative Analyses of Behavior., eds. Behavioral approaches to pattern recognition and concept formation. Erlbaum Associates, 1990.

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L, Commons Michael, and Symposium on Quantitative Analyses of Behavior. (8th : 1985 : Harvard University), eds. Computational and clinical approaches to pattern recognition and concept formation. Lawrence Erlbaum Associates, 1990.

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(Editor), Michael L. Commons, Stephen Grossberg (Editor), and John E.R. Staddon (Editor), eds. Neural Network Models of Conditioning and Action: Quantitative Analyses of Behavior (Quantitative Analysis of Behavior Series). Lawrence Erlbaum, 1991.

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(Editor), Michael L. Commons, Stephen Grossberg (Editor), and John E.R. Staddon (Editor), eds. Neural Network Models of Conditioning and Action: Quantitative Analyses of Behavior. Lawrence Erlbaum, 1991.

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Rajchel, Brett. Unsupervised Learning of Network Traffic Behaviors for Insider Threat Detection. Independently Published, 2021.

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(Editor), Michael L. Commons, John A. Nevin (Editor), Michael C. Davison (Editor), and Michael Davidson (Editor), eds. Signal Detection: Mechanisms, Models, and Applications (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1991.

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Hosmer, Chet. Defending IoT Infrastructures with the Raspberry Pi: Monitoring and Detecting Nefarious Behavior in Real Time. Apress, 2018.

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(Editor), Michael L. Commons, Richard J. Herrnstein (Editor), Stephen M. Kosslyn (Editor), and David B. Mumford (Editor), eds. Computational and Clinical Approaches to Pattern Recognition and Concept Formation: Quantitative Analyses of Behavior, Volume IX (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1990.

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(Editor), Michael L. Commons, James E. Mazur (Editor), John A. Nevin (Editor), and Howard Rachlin (Editor), eds. The Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1986.

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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research
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Book chapters on the topic "Network behavior detection"

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Ko, Mon Mon, and Mie Mie Su Thwin. "Anomalous Behavior Detection in Mobile Network." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23207-2_15.

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Chen, Wenwu, Su Yang, Xu An Wang, Wei Zhang, and Jindan Zhang. "Network Malicious Behavior Detection Using Bidirectional LSTM." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93659-8_57.

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Zhang, Pengyuan, and Baojiang Cui. "Network Scanning Detection Based on Spatiotemporal Behavior." In Advances in Internet, Data & Web Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53555-0_11.

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Wang, Sheng, Jiaming Song, and Ruixu Guo. "Char-Level Neural Network for Network Anomaly Behavior Detection." In Human Centered Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_6.

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Kim, Misun, Minsoo Kim, and JaeHyun Seo. "Network Anomaly Behavior Detection Using an Adaptive Multiplex Detector." In Computational Science and Its Applications - ICCSA 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751595_17.

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Tong, Yan, Jian Zhang, Wei Chen, Mingdi Xu, and Tao Qin. "Dynamic Group Behavior Analysis and Its Application in Network Abnormal Behavior Detection." In Communications and Networking. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78139-6_30.

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Zhang, Yuanzhe, Qiqiang Jin, Maohan Liang, Ruixin Ma, and Ryan Wen Liu. "Vessel Behavior Anomaly Detection Using Graph Attention Network." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_23.

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Abu-Helo, Hamdi, and Huthaifa Ashqar. "Early Ransomware Detection System Based on Network Behavior." In Advanced Information Networking and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57931-8_43.

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Garg, Shree, Anil K. Sarje, and Sateesh Kumar Peddoju. "Improved Detection of P2P Botnets through Network Behavior Analysis." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54525-2_30.

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Chen, Junjie, Shaoyong Guo, Wencui Li, Jing Shen, Xuesong Qiu, and Sujie Shao. "Network Abnormal Behavior Detection Method Based on Affinity Propagation." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8086-4_55.

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Conference papers on the topic "Network behavior detection"

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Dalal, Harshita, Gunjan Thakkar, Himanshu Naik, Kamlesh Kalbande, Asakti Rautkar, and Nekita Chavhan Morris. "Phishing Detection in Dynamic Environments Using Network Behavior." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011414.

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Zhang, Hua. "Dangerous Behavior Detection Based on Convolutional Neural Network Algorithm." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00055.

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She, Xiangyang, and Zhiqi Xu. "Human Abnormal Behavior Detection Based on Multimodal Data Fusion." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691026.

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Tong, Xiaochun, and Mary Jane C. Samonte. "Research on dangerous driving behavior recognition method based on convolutional neural network." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050403.

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Koyama, Yusuke, Hideaki Miyaji, and Hiroshi Yamamoto. "Abnormal Behavior Detection Network System Using 3D LiDAR for Station Platforms." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10929963.

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Ma, Tianfu, Jian Bao, Hao Yang, Xuejiao Zhao, Qingwang Zhang, and Wanting Lv. "Network Anomaly Behavior Detection and Security Protection based on Clustering Algorithm." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934590.

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Ai-Fen Sui, Dai-Fei Guo, Tao Guo, and Ming-zhu Li. "Network behavior based mobile virus detection." In 2012 IEEE 14th International Conference on Communication Technology (ICCT). IEEE, 2012. http://dx.doi.org/10.1109/icct.2012.6511430.

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Cao, Jin, Lawrence Drabeck, and Ran He. "Statistical network behavior based threat detection." In 2017 IEEE Conference on Computer Communications: Workshops (INFOCOM WKSHPS). IEEE, 2017. http://dx.doi.org/10.1109/infcomw.2017.8116413.

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Donetti, Luca. "Improved spectral algorithm for the detection of network communities." In MODELING COOPERATIVE BEHAVIOR IN THE SOCIAL SCIENCES. AIP, 2005. http://dx.doi.org/10.1063/1.2008598.

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Wighneswara, Alifiannisa Alyahasna, Anita Sjahrunnisa, Yasinta Romadhona, et al. "Network Behavior Anomaly Detection using Decision Tree." In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2023. http://dx.doi.org/10.1109/csnt57126.2023.10134589.

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Reports on the topic "Network behavior detection"

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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, contro
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Smit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, 2022. http://dx.doi.org/10.57022/qpsm1481.

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Overview Skin cancer prevention is a component of the new Cancer Plan 2022–27, which guides the work of the Cancer Institute NSW. To lessen the impact of skin cancer on the community, the Cancer Institute NSW works closely with the NSW Skin Cancer Prevention Advisory Committee, comprising governmental and non-governmental organisation representatives, to develop and implement the NSW Skin Cancer Prevention Strategy. Primary Health Networks and primary care providers are seen as important stakeholders in this work. To guide improvements in skin cancer prevention and inform the development of th
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