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Relevant bibliographies by topics / Network Flow Classifier

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Academic literature on the topic 'Network Flow Classifier'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Network Flow Classifier"

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Ullah, Imtiaz, and Qusay H. Mahmoud. "A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks." Electronics 9, no. 3 (2020): 530. http://dx.doi.org/10.3390/electronics9030530.

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The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifie

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Bakirci, Ekim, and Haydar Aygun. "An audio-based vehicle classifier using convolutional neural network." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 2 (2023): 5252–58. http://dx.doi.org/10.3397/in_2022_0766.

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Audio-based event and scene classification are getting more attention in recent years. Many examples of environmental noise detection, vehicle classification, and soundscape analysis are developed using state of art deep learning techniques. The major noise source in urban and rural areas is traffic noise. Environmental noise parameters for urban and rural small roads have not been investigated due to some practical reasons. The purpose of this study is to develop an audio-based traffic classifier for rural and urban small roads which have limited or no traffic flow data to supply values for n

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Rehman, Khalid, and Zahid Ullah. "PackeX: Low-Power High-Performance Packet Classifier Using Memory on FPGAs." Wireless Communications and Mobile Computing 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/5544435.

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Networks are continuously growing, and the demand for fast communication is rapidly increasing. With the increase in network bandwidth requirement, efficient packet-classification techniques are required. To achieve the requirements of these future networks at component level, every module such as routers, switches, and gateways needs to be upgraded. Packet classification is one of the main characteristics of a stable network which differentiates the incoming flow into defined streams. Existing packet classifiers have lower throughput to cope with the higher demand of the network. In this work

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Wang, Yunhui, Weichu Zheng, Zifei Liu, et al. "A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation." Electronics 12, no. 19 (2023): 4049. http://dx.doi.org/10.3390/electronics12194049.

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The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed ne

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Oldenburg, Lennart, Marc Juarez, Enrique Argones Rúa, and Claudia Diaz. "MixMatch: Flow Matching for Mixnet Traffic." Proceedings on Privacy Enhancing Technologies 2024, no. 2 (2024): 276–94. http://dx.doi.org/10.56553/popets-2024-0050.

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Mixnets provide communication anonymity against network adversaries by routing packets independently via multiple hops, delaying them artificially at each hop, and introducing cover traffic. We show that these features (particularly the use of cover traffic) significantly diminish the effectiveness of state-of-the-art flow correlation techniques developed to link the two ends of a Tor connection. In this work, we propose novel methods to determine whether a set of endpoints exchanges packets via a mixnet and demonstrate their effectiveness by applying them to the Nym mixnet. We consider Nym in

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Algelal, Zahraa M., Eman Abdulaziz Ghani Aldhaher, Dalia N. Abdul-Wadood, and Radhwan Hussein Abdulzhraa Al-Sagheer. "Botnet detection using ensemble classifiers of network flow." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2543. http://dx.doi.org/10.11591/ijece.v10i3.pp2543-2550.

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Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability

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Zahraa, M. Algelal, Abdulaziz Ghani Aldhaher Eman, N. Abdul-Wadood Dalia, and Hussein Abdulzhraa Al-Sagheer Radhwan. "Botnet detection using ensemble classifiers of network flow." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2543–50. https://doi.org/10.11591/ijece.v10i3.pp2543-2550.

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Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability

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AL-Azawee, Sanarya Jamal, and Nadia Adnan Shiltagh Al-Jamali. "Handling Heterogeneous Traffic for Software Defined Data-Center Network Using Spike Neural Network." Journal of Engineering 31, no. 5 (2025): 21–34. https://doi.org/10.31026/j.eng.2025.05.02.

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Software Defined Networking (SDN) allows for more flexible network administration than traditional architectures. Software-defined networks (SDNs) efficiently manage data flows and optimize network resources. However, heterogeneity influences the quality of the services. (QoS) needs and network resource demands. They behave differently when traveling to their last point. Currently, numerous data center networks (DCNs) struggle with the unfair use of several network resources by big packets (Elephant flowing) arriving during any instant affecting specific flows (mice flow). Elephant Flows (EF)

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Et. al., Gopal Patil,. "REVIEW THE DEEP LEARNING TECHNIQUE FOR MISSING DATA CLASSIFICATION IN IOT APPLICATIONS FOR NETWORK PERFORMANCE IMPROVEMENT." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 365–69. http://dx.doi.org/10.17762/itii.v9i2.356.

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In order to ensure product safety and increase production quality, The construction of mine Internet of Things networks continues to accelerate mining enterprises. Given the large increase in the number of networked devices connectivity capability in the mine, there is considerable strain on the mine network communication facilities. We suggest an Innovative Solution Using Deep Learning for Missing Data Classification in IoT Network Performance Enhancement System Market Classifier based on neural networks to improve the quality of service in the connectivity infrastructure of mine networks. Th

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Güler, İnan, and Elif Derya Übeyli. "A recurrent neural network classifier for Doppler ultrasound blood flow signals." Pattern Recognition Letters 27, no. 13 (2006): 1560–71. http://dx.doi.org/10.1016/j.patrec.2006.03.001.

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Dissertations / Theses on the topic "Network Flow Classifier"

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Gutierrez, Munoz Alejandro. "Analysis of current flows in electrical networks for error-tolerant graph matching." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002705.

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Deivard, Johannes. "How accuracy of estimated glottal flow waveforms affects spoofed speech detection performance." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48414.

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In the domain of automatic speaker verification, one of the challenges is to keep the malevolent people out of the system. One way to do this is to create algorithms that are supposed to detect spoofed speech. There are several types of spoofed speech and several ways to detect them, one of which is to look at the glottal flow waveform (GFW) of a speech signal. This waveform is often estimated using glottal inverse filtering (GIF), since, in order to create the ground truth GFW, special invasive equipment is required. To the author’s knowledge, no research has been done where the correl

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Staněk, Miroslav. "Určování stresu z řečového signálu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255289.

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Předložená disertační práce se zabývá vývojem algoritmů pro detekci stresu z řečového signálu. Inovativnost této práce se vyznačuje dvěma typy analýzy řečového signálu, a to za použití samohláskových polygonů a analýzy hlasivkových pulsů. Obě tyto základní analýzy mohou sloužit k detekci stresu v řečovém signálu, což bylo dokázáno sérií provedených experimentů. Nejlepších výsledků bylo dosaženo pomocí tzv. Closing-To-Opening phase ratio příznaku v Top-To-Bottom kritériu v kombinaci s vhodným klasifikátorem. Detekce stresu založená na této analýze může být definována jako jazykově i fonémově ne

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(9786254), Silvio Cesare. "Fast automated unpacking and classification of malware." Thesis, 2010. https://figshare.com/articles/thesis/Fast_automated_unpacking_and_classification_of_malware/13459517.

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"Malware is a pervasive problem in distributed computer and network systems. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in classificaiton empoying flowgraph based signatures. Static analysis is widely used to construct the signatures but can be ineffective if malware undergoes a code packing transforrmation to hide its real content. This thesis proposes a novel system, names Malwise, for malware classification using a fast application level emulator to rever

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Books on the topic "Network Flow Classifier"

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Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase spac

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Book chapters on the topic "Network Flow Classifier"

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Amoo, Oseni Taiwo, Hammed Olabode Ojugbele, Abdultaofeek Abayomi, and Pushpendra Kumar Singh. "Hydrological Dynamics Assessment of Basin Upstream–Downstream Linkages Under Seasonal Climate Variability." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_116.

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AbstractThe impacts of climate change are already being felt, not only in terms of increase in temperature but also in respect of inadequate water availability. The Mkomazi River Basins (MRB) of the KwaZulu-Natal region, South Africa serves as major source of water and thus a mainstay of livelihood for millions of people living downstream. It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability. Artificial Neural Network (ANN) pattern classifier was utilized for the se

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Estrella-Guayasamín, Marcelo, Victor Vivar Quiroz, Aaron Delgado Quinto, and Fernando Gomez Berrezueta. "Effect of Oxyhydrogen Gas (HHO) Addition on Fuel Consumption of M2 Category Vehicle by Road Tests." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_21.

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Abstract A change in fossil fuel consumption within the automotive sector is crucial due to its significant impact on climate change. This study assessed the effect of electrolyte concentration and HHO flow rate on fuel consumption in an M2 vehicle. A 2023 KARRY Q22L AC 1.2 5P 4X2 TM van, classified as M2, was equipped with a wet cell HHO gas generator powered by a variable DC source. The cell consisted of two 316 stainless steel plates, each 10 cm by 10 cm by 1.5 mm, containing 250 cm³ of electrolyte. The electrolyte was made from 1 liter of distilled water and KOH concentrations ranging from

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Munivara Prasad, K., V. Samba Siva, P. Krishna Kishore, and M. Sreenivasulu. "DITFEC: Drift Identification in Traffic-Flow Streams for DDoS Attack Defense Through Ensemble Classifier." In Lecture Notes in Networks and Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7150-9_32.

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Schulte, Volker, and Andreas Hinz. "Emigration and Start-up Setting. New Russian and Ukrainian Intelligentsia in a Historical Perspective." In Start-up Cultures in Times of Global Crises. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53942-8_8.

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AbstractThis chapter describes the current situation of Russian entrepreneurs, on the one hand, and Ukrainian entrepreneurs, on the other hand, who have emigrated to a safe third country due to the warlike conflict and the increasingly repressive attitude of the Russian regime. Four Ukrainian and four Russian entrepreneurs were interviewed in addition to extensive source research. These findings are incorporated into the interpretation. Individual statements are quoted. Due to the delicate nature of statements and at the request of the interviewees, they have been anonymized. The new waves of

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Hambebo, Bereket M., Marco Carvalho, and Fredric M. Ham. "A Scalable Approach to Network Traffic Classification for Computer Network Defense using Parallel Neural Network Classifier Architectures." In Efficiency and Scalability Methods for Computational Intellect. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3942-3.ch009.

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The ability to recognize network traffics plays an important role in securing modern computer network infrastructures. In this chapter, we propose a machine learning approach that is based on statistical features of communication flow between two end-points. The statistical features are then used to develop and test a Parallel Neural Network Classifier Architecture (PNNCA), which is trained to recognize specific HTTP session patterns in a controlled environment, and then used to classify general traffic. The classifier’s performance and scalability measures have been compared with other neural

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Arora, Parul, Smriti Srivastava, and Shivank Singhal. "Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch025.

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This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the stu

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C, Lohith, Amaresh A M, Pundalik Chavan, and Triveni N. "CHAUFFEUR BEHAVIOR RECOGNITION USING FACE RECOGNITION AND DEEP LEARNING." In Futuristic Trends in Artificial Intelligence Volume 3 Book 10. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bgai10p5ch1.

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High levels of focus are necessary for safe driving, but these behaviors are frequently overridden by distractions like tiredness, eating, drinking, talking, and phone calls. Sadly, these distractions play a significant role in the worrying increase in traffic accidents nowadays. The creation of software that can proactively inform drivers is essential to resolving this pressing problem. This study suggests a novel, lightweight architecture for convolutional neural networks that is intended to recognize different driving styles, enabling warning systems to provide accurate information and dram

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Kandiero, Agripah, Panashe Chiurunge, and Jacob Munodawafa. "Detection of DDoS Attacks Using Variational Autoencoder-Based Deep Neural Network." In Privacy Preservation and Secured Data Storage in Cloud Computing. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-0593-5.ch017.

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Distributed denial of service (DDoS) attacks are one of the most commonly used tools to disrupt web services. DDoS is used by groups of diverse backgrounds with diverse motives. To counter DDoS, machine learning-based detection systems have been developed. Proposed is a variational autoencoder (VAE) based deep neural network (VAE-DNN) classifier that can be trained on an unbalanced dataset without needing feature engineering. A variational autoencoder is a type of deep neural network that learns the underlying distribution of computer network flows and models how the benign and DDoS classes we

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Nayak, Manjushree, Ashish Kumar Dass, and Sapna Singh Kshatri. "An AI-Based Efficient Model for the Classification of Traffic Signals Using Convolutional Neural Network." In Building Secure Business Models Through Blockchain Technology. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7808-0.ch002.

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The objective of this study is to build a model for the classification of traffic signs available in the image into many categories using a CNN and Keras library to detect the traffic sign. The goal of the traffic sign recognition is to build a deep neural network (DNN), which is used to classify traffic signs. The authors suggest training the model so it can decode traffic signs from natural images using the German Traffic Sign Dataset. This data should be firstly preprocessed in order to maximize the model performance. After choosing model architecture, fine tuning, and training, the model w

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Kumar, Nagesh, and Yashwant Singh. "Routing Protocols in Wireless Sensor Networks." In Sensor Technology. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch003.

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In Wireless Sensor Network (WSN), the routing protocols have been given attention because most of the routing protocols are application and architecture dependent. This chapter presents routing protocols for wireless sensor networks and also classifies routing in WSN. Chapter gives five main classifications of routing protocols in WSN which are data-centric, hierarchical, location-based, network flow and QoS aware and opportunistic routing protocols. The focus has been given on advancement of routing in WSN in form of opportunistic routing, in which the sensor nodes utilize broadcasting nature

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Conference papers on the topic "Network Flow Classifier"

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Merkli, Yannick, Roland Meier, Martin Strohmeier, and Vincent Lenders. "Defeating and Improving Network Flow Classifiers Through Adversarial Machine Learning." In 2024 16th International Conference on Cyber Conflict: Over the Horizon (CyCon). IEEE, 2024. http://dx.doi.org/10.23919/cycon62501.2024.10685592.

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Lianyuan Li, Zemin Liu, and Zheng Zhou. "A Hopfield neural network flow classifier in IP switching." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861502.

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Zhao, Ruijie, Yiteng Huang, Xianwen Deng, et al. "Flow Transformer: A Novel Anonymity Network Traffic Classifier with Attention Mechanism." In 2021 17th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2021. http://dx.doi.org/10.1109/msn53354.2021.00045.

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Wang, Rui, Cen Wang, Xiong Gao, Hongxiang Guo, and Jian Wu. "Neural Network based Online Flow Classifier Implemented by FPGA in Optical DCN." In 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC). IEEE, 2019. http://dx.doi.org/10.23919/ps.2019.8817639.

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Li, Lingqi, Wei Cheng, Kazuhiko Tsukada, and Koichi Hanasaki. "Flaw Classification by Using Artificial Neural Network and Wavelet." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2815.

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This paper presents a methodology to 2-D flaw-shape recognition by combining a neural network and the wavelet feature extractor. This approach consists of three stages. First, the 2-D pattern of an object is retrieved from image and then transformed to complex contour, which is described by the coordinates of its shape. Then, feature extraction is performed to this contour representation. Fourier descriptor (FD), principal component analysis (PCA) and wavelet descriptor (WD) are employed in this stage, and their performances are compared and discussed. In the third stage, artificial neural net

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Głuch, Jerzy, and Jerzy Krzyz˙anowski. "Application of Preprocessed Classifier Type Neural Network for Searching of Faulty Components of Power Cycles in Case of Incomplete Measurement Data." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30028.

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Thermal and flow diagnostics of power units makes use of diagnostic relations i.e. relations between fault signatures (sets of symptoms) and geometry degradation of its components. Determining symptoms may base on thorough thermal measurements of the cycle. However, numerous apparatuses in the cycle are not or cannot be properly equipped for necessary measurements. Examples of such apparatuses in a steam turbine are external glands and nozzle box sealings. The paper studies the applicability of a selected type of Artificial Neural Network, ANN, as a diagnostic relation for locating faulty appa

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Severi, Giorgio, Simona Boboila, Alina Oprea, John Holodnak, Kendra Kratkiewicz, and Jason Matterer. "Poisoning Network Flow Classifiers." In ACSAC '23: Annual Computer Security Applications Conference. ACM, 2023. http://dx.doi.org/10.1145/3627106.3627123.

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Butt, Talha Hanif, Prayag Tiwari, and Fernando Alonso-Fernandez. "Predicting Overtakes In Trucks Using Can Data." In 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp208018.

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Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to extract CAN features. We observe that the prediction scores of the overtake class tend

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Goda, Hiroshi, Seungjin Kim, Ye Mi, Joshua P. Finch, Mamoru Ishii, and Jennifer Uhle. "Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22088.

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Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the me

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Kodama, Koki, and Masahiro Hayashi. "A new method to evaluate flow classified one-to-all reliability." In 2016 18TH Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2016. http://dx.doi.org/10.1109/apnoms.2016.7737280.

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Reports on the topic "Network Flow Classifier"

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McDonald, Jacob, Eric Starkey, Christopher Cooper, and Wendy wright. Wadeable stream habitat monitoring at Chattahoochee River National Recreation Area: 2017 baseline report. National Park Service, 2019. https://doi.org/10.36967/2267301.

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The Southeast Coast Network (SECN) stream habitat monitoring protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream assessments are currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park, Kennesaw Mountain National Battlefield, Ocmulgee Mounds National Historical Park, Chattahoochee River National Recreation Area, and Congaree National Park. Streams chosen for assessment were specifically target

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McDonald, Jacob, Eric Starkey, and Wendy Wright. Wadeable stream habitat monitoring at Kennesaw Mountain National Battlefield Park: 2017 baseline report. National Park Service, 2019. https://doi.org/10.36967/2267298.

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The Southeast Coast Network (SECN) stream habitat monitoring protocol provides guidance for wadeable stream data collection in Southeast Coast Network parks. The results of this effort are intended to provide resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream assessments are currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park, Kennesaw Mountain National Battlefield, Ocmulgee Mounds National Historical Park, Chattahoochee Ri

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Bateman McDonald, Jacob. Wadeable Stream Habitat Monitoring at Kennesaw Mountain National Battlefield Park: 2023 Change Report. National Park Service, 2025. https://doi.org/10.36967/2313926.

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The Southeast Coast Network (SECN) stream habitat monitoring protocol collects data to give park resource managers insight into the status of, and trends in, stream and near-channel habitat conditions. Streams chosen for assessment were specifically targeted for management interest or to provide a context for similar-sized stream(s) within the park. This report documents the 2023 resurvey of wadeable stream habitats in Kennesaw Mountain National Battlefield Park (KEMO), focusing on two third-order streams—Noses Creek (KEMO001) and John Ward Creek (KEMO002). It is the third monitoring event, fo

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Cooper, Christopher, Jacob McDonald, Eric Starkey, and Wendy Wright. Wadeable stream habitat monitoring at Ocmulgee Mounds National Historical Park: 2017 baseline report. National Park Service, 2019. https://doi.org/10.36967/2268263.

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The Southeast Coast Network (SECN) stream habitat monitoring protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream assessments are currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park (HOBE), Kennesaw Mountain National Battlefield (KEMO), Ocmulgee Mounds National Historical Park (OCMU), Chattahoochee River National Recreation Area (CHAT), and Congaree National Park (CONG). Streams chosen for

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Cooper, Christopher, Jacob McDonald, and Eric Starkey. Wadeable stream habitat monitoring at Congaree National Park: 2018 baseline report. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2286621.

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The Southeast Coast Network (SECN) Wadeable Stream Habitat Monitoring Protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream monitoring is currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park (HOBE), Kennesaw Mountain National Battlefield Park (KEMO), Ocmulgee Mounds National Historical Park (OCMU), Chattahoochee River National Recreation Area (CHAT), and Congaree National Park (CONG). Streams

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Sanders, Suzanne, and Jessica Kirschbaum. Forest health monitoring at Mississippi National River and Recreation Area: 2022 field season. National Park Service, 2023. http://dx.doi.org/10.36967/2301407.

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Abstract:

The Mississippi National River and Recreation area (MISS), situated along a 116 km stretch of the Mississippi River through the Minneapolis and St. Paul urban corridor, encompasses ~21,800 ha of public and private land. In 2022, the Great Lakes Inventory and Monitoring Network (GLKN) resampled permanent forest monitoring sites in the park, marking the second assessment of these sites, which were established and initially sampled in 2011. The goal of this long-term monitoring project is to provides managers with routine updates on which to base management decisions; these data can also be used

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