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Journal articles on the topic 'Networks anomalies detection'

Author: Grafiati

Published: 19 October 2024

Last updated: 31 July 2025

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1

Liao, Xiao Ju, Yi Wang, and Hai Lu. "Rule Anomalies Detection in Firewalls." Key Engineering Materials 474-476 (April 2011): 822–27. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.822.

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Firewall is the most prevalent and important technique to enforce the security inside the networks. However, effective and free anomalies rules management in large and fast growing networks becomes increasingly challenging. In this paper, we use a directed tree-based method to detect rule anomalies in firewall; in addition, this method can track the source of the anomalies. We believe the posed information will simplify the rules management and minimizing the networking vulnerability due to firewall rules misconfigurations.

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Rejito, Juli, Deris Stiawan, Ahmed Alshaflut, and Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.pp122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect

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Rejito, Juli, Deris Stiawan, Ahmed Alshaflut, and Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.p122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect

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Juli, Rejito, Stiawan Deris, Alshaflut Ahmed, and Budiarto Rahmat. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. https://doi.org/10.11591/csit.v5i2.pp122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect

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Navale, Manisha Pandurang, and Brijendra P. Gupta. "DEEP LEARNING ALGORITHMS FOR DETECTION AND CLASSIFICATION OF CONGENITAL BRAIN ANOMALY." ICTACT Journal on Image and Video Processing 13, no. 4 (2023): 2995–3001. http://dx.doi.org/10.21917/ijivp.2023.0426.

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Congenital brain anomalies are structural abnormalities that occur during fetal development and can have a significant impact on an individual neurological function. Detecting and classifying these anomalies accurately and efficiently is crucial for early diagnosis, intervention, and treatment planning. In recent years, recurrent neural networks (RNNs) have emerged as powerful tools for analyzing sequential and time-series data in various domains, including medical imaging. This research presents an overview of RNN-based algorithms for the detection and classification of congenital brain anoma

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Kumar D,, Ravi. "Anomaly Detection in Networks." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47520.

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-In Network Security is a major challenge in the digital world. Intrusion is common in many applications and intruders are sophisticated enough to change their attack pattern very often. To address this issue, the development of a model for the detection of network anomalies and intrusions. The approach utilizes the Borderline Synthetic Minority Over-Sampling Technique (SMOTE) along with Support Vector Machines (SVM) to enhance anomaly detection

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Alfardus, Asma, and Danda B. Rawat. "Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks." Electronics 13, no. 10 (2024): 1962. http://dx.doi.org/10.3390/electronics13101962.

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In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering

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Gonela Kavya Pavani, Bobba Veeramallu. "Hybrid Machine Learning Framework for Anomaly Detection in 5G Networks." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 733–39. https://doi.org/10.52783/jisem.v10i32s.5406.

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The rapid adoption of 5G networks has transformed the communication landscape, offering unprecedented speed, capacity, and connectivity for diverse applications such as IoT, autonomous vehicles, and critical infrastructure. However, this evolution also introduces vulnerabilities that can compromise network performance, security, and reliability. Anomaly detection, the process of identifying irregular patterns or deviations in network traffic, has emerged as a critical mechanism to ensure the resilience of 5G networks. It enables proactive identification of issues such as latency spikes, packet

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Mažeika, Dalius, and Saulius Jasonis. "NETWORK TRAFFIC ANOMALIES DETECTING USING MAXIMUM ENTROPY METHOD / KOMPIUTERIŲ TINKLO SRAUTO ANOMALIJŲ ATPAŽINIMAS MAKSIMALIOS ENTROPIJOS METODU." Mokslas – Lietuvos ateitis 6, no. 2 (2014): 162–67. http://dx.doi.org/10.3846/mla.2014.22.

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The problem of traffic anomalies in computer networks is analyzed. NetFlow packets are used as network traffic data and maximum entropy methods is used for anomalies detection. Method detects network anomalies by comparing the current network traffic against a baseline distribution. Method is adopted according to NetFow data and performace of the method is improved. Prototype of anomalies detection system was developed and experimental investigation carried out. Results of investigation confirmed that method is sensitive to deviations of the network traffic and can be successfully used for net

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Rizwan, Ramsha, Farrukh Aslam Khan, Haider Abbas, and Sajjad Hussain Chauhdary. "Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism." International Journal of Distributed Sensor Networks 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/684952.

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During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS

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Legashev, Leonid, Irina Bolodurina, Lubov Zabrodina, et al. "Message Authentication and Network Anomalies Detection in Vehicular Ad Hoc Networks." Security and Communication Networks 2022 (February 24, 2022): 1–18. http://dx.doi.org/10.1155/2022/9440886.

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Intelligent transport systems are the future in matters of safe roads and comfortable driving. Integration of vehicles into a unified intelligent network leads to all kinds of security issues and cyber threats common to conventional networks. Rapid development of mobile ad hoc networks and machine learning methods allows us to ensure security of intelligent transport systems. In this paper, we design an authentication scheme that can be used to ensure message integrity and preserve conditional privacy for the vehicle user. The proposed authentication scheme is designed with lightweight cryptog

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Millán-Roures, Laura, Irene Epifanio, and Vicente Martínez. "Detection of Anomalies in Water Networks by Functional Data Analysis." Mathematical Problems in Engineering 2018 (June 21, 2018): 1–13. http://dx.doi.org/10.1155/2018/5129735.

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A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of

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Rovatsos, Georgios, George V. Moustakides, and Venugopal V. Veeravalli. "Quickest Detection of Moving Anomalies in Sensor Networks." IEEE Journal on Selected Areas in Information Theory 2, no. 2 (2021): 762–73. http://dx.doi.org/10.1109/jsait.2021.3076043.

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Melnikov, Oleg, Yurii Dorofieiev, and Natalia Marchenko. "STATISTICAL APPROACH TO DETECTION OF ANOMALIES IN WATER DISTRIBUTION NETWORKS." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (13) (July 11, 2025): 3–9. https://doi.org/10.20998/2079-0023.2025.01.01.

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This paper is devoted to solving the problem of developing an automated system for detecting anomalies in water distribution networks. The main causes of such anomalies are background leaks and pipe breaks. To address this problem, a statistical approach is proposed, which consists in testing the null hypothesis that the readings of pressure and/or water flow sensors received in real time correspond to the standard conditions of the network. The paper proposes a three-stage anomaly detection scheme, which includes: statistical profiling of network sensors; system calibration to achieve the des

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Rajaboevich, Gulomov Sherzod, and Ganiev Abdukhalil Abdujalilovich. "Methods and models of protecting computer networks from un-wanted network traffic." International Journal of Engineering & Technology 7, no. 4 (2018): 2541. http://dx.doi.org/10.14419/ijet.v7i4.14744.

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In this article a method of measure network traffic to collect data about the header of packets and to analyze the traffic dump in computer networks are offered. A method for detecting anomalies and a formal model for protecting information from DDoS attacks, which make it possible to simplify the development of filter rule sets and improve the efficiency of computer networks, taking into account, the interaction of detection modules and the use of formal set-theoretic constructions are proposed.

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A, Nandini. "Anomaly Detection Using CNN with I3D Feature Extraction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29371.

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Anomaly detection is a critical task in various fields such as surveillance, healthcare, and industrial monitoring, aiming to identify patterns that deviate significantly from normal behavior.Video anomaly detection is inherently difficult due to visual complexity and variability. This work proposes a unique anomaly detection technique leveraging Convolutional Neural Networks (CNN) with Inflated 3D Convolutional Networks (I3D) for feature extraction. This involves training the CNN on a large dataset to learn normal behavior, enabling it to identify anomalies by recognizing deviations from lear

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Nabil, Meriem, Meriem Hnida, Abdelhay Haqiq, and Imane Hilal. "Advanced Anomaly Detection in Mobile Networks: A Hybrid Approach Based on Statistical and Machine Learning Techniques." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 13 (2025): 162–82. https://doi.org/10.3991/ijim.v19i13.54539.

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Network traffic analysis (NTA) is a technique used by network administrators to monitor network activity, ensure availability, and detect unusual patterns to identify potential anomalies. However, traditional traffic monitoring systems often struggle to detect these anomalies accurately because they rely on rigid models and a limited pool of data. Additionally, anomaly detection is particularly challenging, as anomalies exhibit patterns that differ from most network activities, making their identification based on prior knowledge difficult. This underscores the necessity for an automated and u

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Gutiérrez-Gómez, Leonardo, Alexandre Bovet, and Jean-Charles Delvenne. "Multi-Scale Anomaly Detection on Attributed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 678–85. http://dx.doi.org/10.1609/aaai.v34i01.5409.

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Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain ch

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Yallamanda Rajesh Babu, Et al. "Subgraph Anomaly Detection in Social Networks using Clustering-Based Deep Autoencoders." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1646–55. http://dx.doi.org/10.17762/ijritcc.v11i9.9150.

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Social networks are becoming more prevalent all across the globe. With all of its advantages, criminality and fraudulent conduct in this medium are on the rise. As a result, there is an urgent need to detect abnormalities in these networks before they do substantial harm. Traditional Non-Deep Learning (NDL) approaches fails to perform effectively when the size and scope of real-world social networks increase. As a result, DL techniques for anomaly detection in social networks are required. Several studies have been conducted using DL on node and edge anomaly detection. However, in the current

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Ma, Shu Hua, Jin Kuan Wang, Zhi Gang Liu, and Hou Yan Jiang. "Density-Based Distributed Elliptical Anomaly Detection in Wireless Sensor Networks." Applied Mechanics and Materials 249-250 (December 2012): 226–30. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.226.

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Data measured and collected by WSNs is often unreliable and a big amount of anomaly data exist. Detecting these anomaly in energy-constrained situations is an important challenge in managing these types of networks. To detect anomalies induced by the decrease of battery power, we use HyCARCE algorithm but it has the problem of low detection rate and high false positive rate when the input space consists of a mixture of dense and sparse regions which make the anomalies form clusters. The paper presents a density-based algorithm to separate the normal cluster from all clusters. The performance o

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M I, Tapasvi. "Anomaly Detection to Network Instrusion:A Systematic Literature Review." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46842.

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Abstract -In networking, anomaly detection is essential to preserving network infrastructure performance, security, and dependability. The increasing volume of network traffic and the dynamic and complicated character of contemporary networks pose serious problems for conventional anomaly detection techniques. The application of machine learning (ML) approaches to identify network traffic anomalies, such as malicious activity, performance deterioration, and configuration problems, is examined in this study. Supervised, unsupervised, and semi-supervised machine learning models provide improved

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Yu, Xiang, Hui Lu, Xianfei Yang, et al. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.

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With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account

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., Tanvirahmedshuvo, Asif Iqbal, Emon Ahmed, Ashequr Rahman, and Md Risalat Hossain Ontor. "ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS." American Journal of Engineering and Technology 06, no. 11 (2024): 78–91. https://doi.org/10.37547/tajet/volume06issue11-09.

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This study investigates the effectiveness of generative models and traditional classification models in detecting fraud and anomalies within the retail banking sector. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) were evaluated for their capability to generate realistic synthetic transaction data and identify anomalies, achieving anomaly detection accuracies of 91.2% and 93.5%, respectively. These models were also assessed using Inception Score and Fréchet Inception Distance (FID), with GANs exhibiting superior data realism. Among classification models, Gradient B

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medshuvo, Tanvirah, Asif Iqbal, Emon Ahmed, Ashequr Rahman, and Md Risalat Hossain Ontor. "ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS." International journal of networks and security 04, no. 01 (2024): 33–43. http://dx.doi.org/10.55640/ijns-04-01-07.

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This study investigates the effectiveness of generative models and traditional classification models in detecting fraud and anomalies within the retail banking sector. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) were evaluated for their capability to generate realistic synthetic transaction data and identify anomalies, achieving anomaly detection accuracies of 91.2% and 93.5%, respectively. These models were also assessed using Inception Score and Fréchet Inception Distance (FID), with GANs exhibiting superior data realism. Among classification models, Gradient B

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Lubis, Hartati Tammamah, Roslina Roslina, and Lili Tanti. "Anomaly Detection in Computer Networks Using Isolation Forest in Data Mining." JURNAL TEKNIK INFORMATIKA 18, no. 1 (2025): 77–86. https://doi.org/10.15408/jti.v18i1.44285.

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The rapid growth of network data has increased the complexity of detecting anomalies, which are crucial for ensuring the security and integrity of information systems. This study investigates the use of the Isolation Forest algorithm for anomaly detection in network traffic, utilizing the Luflow Network Intrusion Detection dataset, which contains 590,086 records with 16 features related to network activities. The methodology encompasses data preprocessing (cleaning, normalization, and feature scaling), feature selection (bytes in, bytes out, entropy, and duration), model training, and performa

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Badr, Malek, Shaha Al-Otaibi, Nazik Alturki, and Tanvir Abir. "Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays." BioMed Research International 2022 (July 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/7833516.

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X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning

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P. Chiranjeevi, Yadavalli Ramya, Chinthala Balaji, Bathini Shashank, and Abbdi Sainath Reddy. "Uncovering time series anomaly using deep learning technique." World Journal of Advanced Research and Reviews 22, no. 1 (2024): 879–87. http://dx.doi.org/10.30574/wjarr.2024.22.1.1129.

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Time series data, characterized by its sequential and temporal nature, plays a crucial role in various domains such as finance, healthcare, and industrial processes. Identifying anomalies within time series data is a critical task with applications ranging from fault detection to fraud prevention. Traditional anomaly detection techniques often struggle to capture complex temporal patterns and dependencies in time series data. This study presents a novel time series anomaly detection method using long short-term memory (LSTM) neural networks. LSTMs are a type of recurrent neural networks (RNNs)

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P., Chiranjeevi, Ramya Yadavalli, Balaji Chinthala, Shashank Bathini, and Sainath Reddy Abbdi. "Uncovering time series anomaly using deep learning technique." World Journal of Advanced Research and Reviews 22, no. 1 (2024): 879–87. https://doi.org/10.5281/zenodo.14210373.

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Time series data, characterized by its sequential and temporal nature, plays a crucial role in various domains such as finance, healthcare, and industrial processes. Identifying anomalies within time series data is a critical task with applications ranging from fault detection to fraud prevention. Traditional anomaly detection techniques often struggle to capture complex temporal patterns and dependencies in time series data. This study presents a novel time series anomaly detection method using long short-term memory (LSTM) neural networks. LSTMs are a type of recurrent neural networks (RNNs)

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M. Mari Selvam, Shaik Ansar, Moramreddy Praveen, Akula Sireesha, and Padarthi Surekha. "Target Detection by Optimizing Anomaly Detection in Hyperspectral Image Processing using AI/ML." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3391–97. https://doi.org/10.32628/cseit25112816.

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Anomaly detection in hyperspectral images involves identifying deviations or outliers within the high-dimensional spectral data captured across numerous contiguous wavelength bands. Hyperspectral imaging provides detailed spectral information, making it a powerful tool for detecting subtle variations in materials or objects that are not visible in traditional imaging techniques. The proposed system employs advanced machine learning techniques, including convolutional neural networks (CNNs) and autoencoders, to analyse hyperspectral images for anomalies. By training the models on a dataset of n

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Dehbozorgi, Leila, Reza Akbari-Hasanjani, and Reza Sabbaghi-Nadooshan. "Chaotic seismic signal modeling based on noise and earthquake anomaly detection." Facta universitatis - series: Electronics and Energetics 35, no. 4 (2022): 603–17. http://dx.doi.org/10.2298/fuee2204603d.

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Since ancient times, people have tried to predict earthquakes using simple perceptions such as animal behavior. The prediction of the time and strength of an earthquake is of primary concern. In this study chaotic signal modeling is used based on noise and detecting anomalies before an earthquake using artificial neural networks (ANNs). Artificial neural networks are efficient tools for solving complex problems such as prediction and identification. In this study, the effective features of chaotic signal model is obtained considering noise and detection of anomalies five minutes before an eart

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Tonejc, Jernej, Sabrina Güttes, Alexandra Kobekova, and Jaspreet Kaur. "Machine Learning Methods for Anomaly Detection in BACnet Networks." JUCS - Journal of Universal Computer Science 22, no. (9) (2016): 1203–24. https://doi.org/10.3217/jucs-022-09-1203.

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In recent years, the volume and the complexity of data in Building Automation System networks have increased exponentially. As a result, a manual analysis of network traffic data has become nearly impossible. Even automated but supervised methods are problematic in practice since the large amount of data makes manual labeling, required to train the algorithms to differentiate between normal traffic and anomalies, impractical. This paper introduces a framework which allows the characterization of BACnet network traffic data by means of unsupervised machine learning techniques. Specifically, we

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PETLIAK, Nataliia, Kostiantyn BILETSKYI, and Yana ZASTAVNA. "APPROACH TO DETECTION OF ANOMALOUS NETWORK TRAFFIC USING LOF AND HBOS ALGORITHMS." MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, no. 4 (November 28, 2024): 125–29. https://doi.org/10.31891/2219-9365-2024-80-15.

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The article is devoted to the problem of detecting anomalies in modern computer networks, which is one of the main threats to cyber security. With the development of Internet technologies, the number of devices and the volume of network traffic are constantly increasing, which leads to an increase in the risk of various cyber threats, such as DDoS attacks, zero-day attacks, and exploitation of protocol vulnerabilities. Abnormal network traffic can result from malicious activity and technical malfunctions, such as configuration errors or hardware failures. Specialised algorithms and methods of

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James Anderson, Emily Johnson, and Michael Brown. "IoT, Anomaly Detection, Machine Learning, K-Nearest Neighbors, Random Forest, Real-Time Detection." International Journal of Information Engineering and Science 1, no. 1 (2024): 01–06. http://dx.doi.org/10.62951/ijies.v1i1.50.

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The increase in connected IoT devices causes increased vulnerability to cyber attacks. This research develops a hybrid machine learning model to detect real-time anomalies in IoT networks. This model combines the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms to increase accuracy and efficiency. Evaluation was carried out using the UNSW-NB15 dataset to test model performance. The results show that this hybrid approach is able to detect anomalies with high accuracy and a low false positive rate.

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Račys, Donatas, and Dalius Mažeika. "NETWORK TRAFFIC ANOMALIES IDENTIFICATION BASED ON CLASSIFICATION METHODS / TINKLO SRAUTO ANOMALIJŲ IDENTIFIKAVIMAS, TAIKANT KLASIFIKAVIMO METODUS." Mokslas – Lietuvos ateitis 7, no. 3 (2015): 340–44. http://dx.doi.org/10.3846/mla.2015.796.

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A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can b

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Joseph, Jennifer E., Ngozi Tracy Aleke, and Onyinyechukwu Prisca Onyeanisi. "Deep Learning Based Intrusion Detection System for Network Security in IoT System." International Journal of Education, Management, and Technology 3, no. 1 (2025): 119–38. https://doi.org/10.58578/ijemt.v3i1.4539.

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The Internet of Things (IoT) has grown rapidly, leading to unparalleled connectivity and vast amounts of data. Anomaly detection plays a crucial role in identifying unusual behavior that deviates from the system's normal operation, enabling the swift detection and resolution of these anomalies. The integration of artificial intelligence (AI) with IoT significantly improves the effectiveness of anomaly detection, enhancing the performance, dependability, and security of IoT systems. AI-powered anomaly detection methods can recognize a wide array of threats within IoT environments, such as brute

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Wu, Nannan, Wenjun Wang, Feng Chen, Jianxin Li, Bo Li, and Jinpeng Huai. "Uncovering Specific-Shape Graph Anomalies in Attributed Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5433–40. http://dx.doi.org/10.1609/aaai.v33i01.33015433.

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As networks are ubiquitous in the modern era, point anomalies have been changed to graph anomalies in terms of anomaly shapes. However, the specific-shape priors about anomalous subgraphs of interest are seldom considered by the traditional approaches when detecting the subgraphs in attributed graphs (e.g., computer networks, Bitcoin networks, and etc.). This paper proposes a nonlinear approach to specific-shape graph anomaly detection. The nonlinear approach focuses on optimizing a broad class of nonlinear cost functions via specific-shape constraints in attributed graphs. Our approach can be

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Yan Lei. "Smart Network Forensics with Generative Adversarial Networks Leveraging Blockchain for Anomaly Detection and Immutable Audit Trails." Power System Technology 48, no. 1 (2024): 1625–42. http://dx.doi.org/10.52783/pst.432.

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Analyzing the specificity of the cybersecurity domain, the problem of ensuring the security and integrity of smart networks is multifaceted. This research explores the complexity of smart network forensics and seeks to meet theses challenges through different approaches. First, to establish the subject of the investigation, the context is described, which includes factors such as ever-fluctuating network traffic and increasing threat types. Further, a thorough analysis of the literature and research work available in the field of network forensics, anomaly detection methodologies, generative a

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Shreyas J, Et al. "Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2276–81. http://dx.doi.org/10.17762/ijritcc.v11i10.8948.

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The rapid proliferation of IOT (Internet of Things) networks has brought transformative benefits to industries and everyday life. However, it has also introduced unprecedented cyber security challenges, necessitating advanced techniques for anomaly detection. This research focuses on enhancing cyber security through the application of machine learning-based anomaly detection methods, specifically One-Class Support Vector Machine (SVM) and Isolation Forest, in the context of IOT networks. While Isolation Forest effectively isolates anomalies by building isolation trees, One-Class SVM models the

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Meneganti, M., F. S. Saviello, and R. Tagliaferri. "Fuzzy neural networks for classification and detection of anomalies." IEEE Transactions on Neural Networks 9, no. 5 (1998): 848–61. http://dx.doi.org/10.1109/72.712157.

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Burgueño, Jesús, Isabel de-la-Bandera, Jessica Mendoza, David Palacios, Cesar Morillas, and Raquel Barco. "Online Anomaly Detection System for Mobile Networks." Sensors 20, no. 24 (2020): 7232. http://dx.doi.org/10.3390/s20247232.

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The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous

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Geeta and Dr Renuka Arora. "Anomalies Detection in Wireless Sensor Networks with Exploring Various Machine Learning Techniques: Review." International Journal of Engineering and Advanced Technology 14, no. 4 (2025): 15–21. https://doi.org/10.35940/ijeat.d4588.14040425.

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Wireless Sensor Networks (WSNs) form the backbone of numerous critical applications, ranging from environmental monitoring to defense surveillance, necessitating highly reliable anomaly detection systems to ensure operational integrity and security. Traditional anomaly detection methods in WSNs often grapple with the high dimensionality of sensor data, dynamic environmental conditions, and resource constraints, leading to suboptimal performance. This research paper introduces a novel framework that leverages advanced machine learning techniques, focusing on utilizing deep learning techniques t

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Geeta. "Anomalies Detection in Wireless Sensor Networks with Exploring Various Machine Learning Techniques: Review." International Journal of Engineering and Advanced Technology (IJEAT) 14, no. 4 (2025): 15–21. https://doi.org/10.35940/ijeat.D4588.14040425.

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<strong>Abstract:</strong> Wireless Sensor Networks (WSNs) form the backbone of numerous critical applications, ranging from environmental monitoring to defense surveillance, necessitating highly reliable anomaly detection systems to ensure operational integrity and security. Traditional anomaly detection methods in WSNs often grapple with the high dimensionality of sensor data, dynamic environmental conditions, and resource constraints, leading to suboptimal performance. This research paper introduces a novel framework that leverages advanced machine learning techniques, focusing on utilizing

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Al-Mazrawe, Amer, and Bahaa Al-Musawi. "Anomaly Detection in Cloud Network: A Review." BIO Web of Conferences 97 (2024): 00019. http://dx.doi.org/10.1051/bioconf/20249700019.

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Cloud computing stands out as one of the fastest-growing technologies in the 21st century, offering enterprises opportunities to reduce costs, enhance scalability, and increase flexibility through rapid access to a shared pool of elastic computing resources. However, its security remains a significant challenge. As cloud networks grow in complexity and scale, the need for effective anomaly detection becomes crucial. Identifying anomalous behavior within cloud networks poses a challenge due to factors such as the voluminous data exchanged and the dynamic nature of underlying cloud infrastructur

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Sozol, Md Shariar, Golam Mostafa Saki, and Md Mostafizur Rahman. "Anomaly Detection in Cybersecurity with Graph-Based Approaches." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37061.

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The field of cybersecurity is changing dramatically in this dynamic age of digital revolution. This work on Anomaly Detection in Cybersecurity using Graph- Based Approaches represents a ground- breaking project that uses Graph Neural Networks' (GNNs'), Graph-Based Behavioural Anomaly Detection (GBBAD), Behavioural Identification Graph (BIG) and Graph-Based Botnet Detection (GBBD) capabilities to revolutionize the way we defend our digital borders. The discovery signifies a noteworthy progress in uncovering abnormalities. The precision and flexibility of this system has been emphasized by the a

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Airlangga, Gregorius, Denny Jean Cross Sihombing, and Oskar Ika Adi Nugroho. "Enhancing UAV Communication Security: Multi-Label Anomaly Detection Using Machine Learning in Imbalanced Data Environments." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 10, no. 1 (2025): 410. https://doi.org/10.30645/jurasik.v10i1.883.

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Unmanned Aerial Vehicle (UAV) communication networks are increasingly vulnerable to cyber threats, including spoofing, jamming, malware, and distributed denial-of-service (DDoS) attacks. Effective anomaly detection is crucial to maintaining network integrity and operational security. This study evaluates multiple machines learning models, including Support Vector Machines, Logistic Regression, XGBoost, Gradient Boosting, and Random Forest, to detect anomalies in UAV communication networks. A real-world dataset containing 44,016 instances of network telemetry and security indicators was utilize

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de Campos Souza, Paulo Vitor, Augusto Junio Guimarães, Thiago Silva Rezende, Vinicius Jonathan Silva Araujo, and Vanessa Souza Araujo. "Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks." AI 1, no. 1 (2020): 92–116. http://dx.doi.org/10.3390/ai1010005.

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The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow t

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Agboola, Olasoji O., Oludare Olukayode Kuye, and Thomas K. Adenowo. "Deep Learning Approaches to Identify Subtle Anomalies in Prenatal Ultrasound Imaging." Path of Science 11, no. 6 (2025): 3019. https://doi.org/10.22178/pos.119-20.

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This research investigated deep learning approaches for detecting subtle anomalies in prenatal ultrasound imaging. Congenital anomalies affect approximately 6% of births worldwide, with detection rates for subtle defects varying significantly based on operator expertise. A multi-institutional dataset comprising 12,450 prenatal ultrasound examinations from three tertiary care centres was employed to develop and evaluate multiple deep learning architectures, including modified convolutional neural networks, generative adversarial networks, autoencoders, and feature fusion approaches. The ensembl

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Dymora, Paweł, and Mirosław Mazurek. "An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis." Applied Sciences 10, no. 9 (2020): 3277. http://dx.doi.org/10.3390/app10093277.

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Fractal and multifractal analysis can help to discover the structure of the communication system, and in particular the pattern and characteristics of traffic, in order to understand the threats better and detect anomalies in network operation. The massive increase in the amount of data transmitted by different devices makes these systems the target of various types of attacks by cybercriminals. This article presents the use of fractal analysis in detecting threats and anomalies. The issues related to the construction and functioning of the Security Operations Centre (SOC) are presented. To ex

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Rana, Samir. "Anomaly Detection in Network Traffic using Machine Learning and Deep Learning Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 1063–67. http://dx.doi.org/10.17762/turcomat.v10i2.13626.

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Due to the rise of sophisticated cyberattacks, network security has become an increasingly important field. One of the most common threats to the security of networks is network anomalies, which can cause system malfunctions and prevent them from working properly. Detecting such anomalies is very important to ensure the continued operation of the network. Deep learning and machine learning algorithms have demonstrated their ability to detect network anomalies, but their effectiveness is still not widely known. This paper presents an evaluation of the performance of three algorithms against the

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Patel, Darsh, Kathiravan Srinivasan, Chuan-Yu Chang, Takshi Gupta, and Aman Kataria. "Network Anomaly Detection inside Consumer Networks—A Hybrid Approach." Electronics 9, no. 6 (2020): 923. http://dx.doi.org/10.3390/electronics9060923.

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With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic

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