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Journal articles on the topic 'Network traffic detection'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Chadrack, Irabaruta, and Dr Nyesheja Muhire Enan. "AI Powered Network Traffic Detection." Journal of Information and Technology 5, no. 2 (2025): 53–65. https://doi.org/10.70619/vol5iss2pp53-65.

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This study presents an AI-powered network traffic detection framework capable of recognizing anomalies and addressing cyber threats in real-time. Traditional detection systems struggle to keep pace with evolving threats, necessitating more adaptive and intelligent approaches. To this end, the research integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance detection accuracy and operational efficiency. The framework is evaluated using benchmark datasets such as UNSW-NB15 and CICIDS2017, focusing on performance metrics including accuracy, precision, re

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Katuk, Norliza, Mohamad Sabri Sinal, Mohammed Gamal Ahmed Al-Samman, and Ijaz Ahmad. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences 12, no. 2 (2023): 121. http://dx.doi.org/10.11591/ijaas.v12.i2.pp121-132.

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<span>This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results sugg

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Norliza, Katuk, Gamal Ahmed Al-Samman Mohammed, and Ahmad Ijaz. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences (IJAAS) 12, no. 2 (2023): 132. https://doi.org/10.11591/ijaas.v12.i2.pp121-132.

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This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results suggested that t

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Jiang, Ding De, Cheng Yao, Zheng Zheng Xu, Peng Zhang, Zhen Yuan, and Wen Da Qin. "An Continuous Wavelet Transform-Based Detection Approach to Traffic Anomalies." Applied Mechanics and Materials 130-134 (October 2011): 2098–102. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2098.

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Anomalous traffic often has a significant impact on network activities and lead to the severe damage to our networks because they usually are involved with network faults and network attacks. How to detect effectively network traffic anomalies is a challenge for network operators and researchers. This paper proposes a novel method for detecting traffic anomalies in a network, based on continuous wavelet transform. Firstly, continuous wavelet transforms are performed for network traffic in several scales. We then use multi-scale analysis theory to extract traffic characteristics. And these char

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Praveena, Nutakki, Dr Ujwal A. Lanjewar, and Chilakalapudi Meher Babu. "VIABLE NETWORK INTRUSION DETECTION ON WIRELESS ADHOC NETWORKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (2013): 29–34. http://dx.doi.org/10.24297/ijct.v5i1.4383.

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Control architecture for resource allocation in satellite networks is proposed, along with the specification of performance indexes and control strategies. The latter, besides being based on information on traffic statistics and network status, rely upon some knowledge of the fading conditions over the satellite network channels. The resource allocation problem consists of the assignment, by a master station, of a total available bandwidth among traffic earth stations in the presence of different traffic types. Traffic stations are assumed to measure continuously their signal fade level, but t

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Fu, Xingbing, Xuewen Zhang, Jianfeng Fu, Bingjin Wu, and Jianwu Zhang. "Deep metric learning based approach for network intrusion detection." Journal of Physics: Conference Series 2504, no. 1 (2023): 012037. http://dx.doi.org/10.1088/1742-6596/2504/1/012037.

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Abstract Today, intrusion detection systems are the way to defend network intrusion flows. In this paper, to categorize network traffic data, we proposed a novel method for detecting network intrusions. It builds intrusion detection models using a deep metric learning (DML) strategy that incorporates two multi-scale convolutional neural networks (MSCNN) and a Triplet network. During the phase of training MSCNN networks, the network traffic data are divided into attack network traffic data and normal data, and we train two distinct MSCNN networks on the basis of these two datasets. To determine

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Son, Vu Ngoc. "Optimizing Network Anomaly Detection Based on Network Traffic." International Journal of Emerging Technology and Advanced Engineering 11, no. 11 (2021): 53–60. http://dx.doi.org/10.46338/ijetae1121_07.

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Cyber-attack is a very hot topic today. Nowadays, systems must always be connected to the internet, and network infrastructure keeps growing in both scale and complexity. Therefore, the problem of detecting and warning cyber-attacks is now very urgent. To improve the effectiveness of detecting cyber-attacks, many methods and techniques were applied. In this paper, we propose to apply two methods of optimizing cyber-attack detection based on the IDS 2018 dataset using Principal Component Analysis (PCA) and machine learning algorithms. In the experimental section, we compare and evaluate the eff

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Pratomo, Baskoro A., Pete Burnap, and George Theodorakopoulos. "BLATTA: Early Exploit Detection on Network Traffic with Recurrent Neural Networks." Security and Communication Networks 2020 (August 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/8826038.

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Detecting exploits is crucial since the effect of undetected ones can be devastating. Identifying their presence on the network allows us to respond and block their malicious payload before they cause damage to the system. Inspecting the payload of network traffic may offer better performance in detecting exploits as they tend to hide their presence and behave similarly to legitimate traffic. Previous works on deep packet inspection for detecting malicious traffic regularly read the full length of application layer messages. As the length varies, longer messages will take more time to analyse,

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Oh, Changhyeon, and Yuseok Ban. "Cross-Modality Interaction-Based Traffic Accident Classification." Applied Sciences 14, no. 5 (2024): 1958. http://dx.doi.org/10.3390/app14051958.

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Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a

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Zhiwei Zhang, Zhiwei Zhang, Guiyuan Tang Zhiwei Zhang, Baoquan Ren Guiyuan Tang, Baoquan Ren Baoquan Ren, and Yulong Shen Baoquan Ren. "TV-ADS: A Smarter Attack Detection Scheme Based on Traffic Visualization of Wireless Network Event Cell." 網際網路技術學刊 25, no. 2 (2024): 301–11. http://dx.doi.org/10.53106/160792642024032502012.

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<p>To protect the increasing cyberspace assets, attack detection systems (ADSs) as well as intrusion detection systems (IDSs) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect maliciou

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Liu, Haitao, and Haifeng Wang. "Real-Time Anomaly Detection of Network Traffic Based on CNN." Symmetry 15, no. 6 (2023): 1205. http://dx.doi.org/10.3390/sym15061205.

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Network traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is easily lost, and the adjustment of dynamic network configuration becomes gradually complicated. To solve this problem, we designed and realized a new online anomaly detection system based on software defined networks. The system uses the convolutional neural network to directly extract the original f

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Mathan, Pinaki Shashishekhar. "Intrusion Detection Using Machine Learning Classification and Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42130.

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An Intrusion Detection System (IDS) is a crucial security mechanism designed to protect computer networks from unauthorized access and cyber threats. With the rapid expansion of Internet-based data transmission, ensuring network security has become increasingly challenging. IDS continuously monitors and analyzes network traffic to detect malicious activities, relying on datasets like KDD Cup 1999 for training and evaluation. Effective IDS development involves preprocessing steps such as feature selection, normalization, and addressing data imbalance to enhance detection accuracy. Various machi

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Mr., S.Manikandan. "The Potential of AI to Secure and Improve the Performance of Computer Networks." Journal of Scholastic Engineering Science and Management 2, no. 2 (2023): 42–50. https://doi.org/10.5281/zenodo.8255668.

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<strong>Artificial intelligence (AI) has the potential to revolutionize computer networking by improving security and performance. AI can be used to identify anomalies in network traffic that could indicate malicious activity, and to optimize network resources to improve performance. This paper reviews the potential of AI for computer networking, and presents a novel approach to anomaly detection in network traffic using deep learning. The proposed system was evaluated using a dataset of network traffic collected from a real-world network, and was able to achieve a high accuracy in detecting m

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Anwer, M., S. M. Khan, M. U. Farooq, and W. Waseemullah. "Attack Detection in IoT using Machine Learning." Engineering, Technology & Applied Science Research 11, no. 3 (2021): 7273–78. http://dx.doi.org/10.48084/etasr.4202.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and

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Lu, Kaibin. "Network Anomaly Traffic Analysis." Academic Journal of Science and Technology 10, no. 3 (2024): 65–68. http://dx.doi.org/10.54097/8as0rg31.

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This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, arti

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Journal, IJSREM. "Review of High Performance Network Intrusion Detection Engine." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28002.

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As the proliferation of connected devices and services increases, so does the demand for protective measures against cyber-attacks. Intrusion Detection Systems (IDS) are a crucial component of network perimeter security, detecting attacks by inspecting network traffic packets or operating system logs. While machine learning techniques have shown effectiveness in intrusion detection, few have utilized the time- series information of network traffic data, and none have included categorical information in neural network-based approaches. In this paper, we propose network intrusion detection model

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Barrionuevo, Mercedes, Mariela Lopresti, Natalia Miranda, and Fabiana Piccoli. "Secure Computer Network: Strategies and Challengers in Big Data Era." Journal of Computer Science and Technology 18, no. 03 (2018): e28. http://dx.doi.org/10.24215/16666038.18.e28.

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As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values fromlarge volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point toprevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage.

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Tao, Xiaoling, Yang Peng, Feng Zhao, Peichao Zhao, and Yong Wang. "A parallel algorithm for network traffic anomaly detection based on Isolation Forest." International Journal of Distributed Sensor Networks 14, no. 11 (2018): 155014771881447. http://dx.doi.org/10.1177/1550147718814471.

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With the rapid development of large-scale complex networks and proliferation of various social network applications, the amount of network traffic data generated is increasing tremendously, and efficient anomaly detection on those massive network traffic data is crucial to many network applications, such as malware detection, load balancing, network intrusion detection. Although there are many methods around for network traffic anomaly detection, they are all designed for single machine, failing to deal with the case that the network traffic data are so large that it is prohibitive for a singl

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Li, Yimin, Dezhi Han, Mingming Cui, Fan Yuan, and Yachao Zhou. "RESNETCNN: An abnormal network traffic flows detection model." Computer Science and Information Systems, no. 00 (2023): 4. http://dx.doi.org/10.2298/csis221124004l.

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Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network(RESNET) and parallel cross-convolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the

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Preethi, M. Srilakshmi, Neeraj Kumar Uppu, and K. Naveen Kumar. "Unbalanced Traffic Intrusion Detection Using Advanced Deep Learning Techniques." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 377–82. https://doi.org/10.47001/irjiet/2025.inspire61.

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The work attempts a deep learning-based approach unbalanced network traffic intrusion detection, applying AE, DBN networks, and SNN models. The proposed system will efficiently extract features from raw network traffic data by employing AE. For the better analysis of temporal dependencies in the sequence of traffic data, the work will make use of DBN and SNN models for increasing the accuracy in intrusion detection. Malicious intrusions that disturb normal traffic flow are identified by the model through the analysis of network traffic patterns. Network traffic datasets are used to train the m

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Duraj, Agnieszka. "Anomaly detection in network traffic." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 12 (2022): 207–10. http://dx.doi.org/10.15199/48.2022.12.46.

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Ali, Wasim Ahmed, Manasa K. N, Mohammed Aljunid, Malika Bendechache, and P. Sandhya. "Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic." Journal of Telecommunications and the Digital Economy 8, no. 4 (2020): 64–95. http://dx.doi.org/10.18080/jtde.v8n4.307.

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Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies i

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Gao, Minghui, Li Ma, Heng Liu, Zhijun Zhang, Zhiyan Ning, and Jian Xu. "Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis." Sensors 20, no. 5 (2020): 1452. http://dx.doi.org/10.3390/s20051452.

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Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional anomaly detection systems to effectively analyze and identify abnormal traffic. At present, deep neural network (DNN) technology achieved great results in terms of anomaly detection, and it can achieve automatic detection. However, there still exists misclassified traffic in the prediction results of deep neural networks, resulting in redundant

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Alam, Shumon, Yasin Alam, Suxia Cui, and Cajetan Akujuobi. "Data-Driven Network Analysis for Anomaly Traffic Detection." Sensors 23, no. 19 (2023): 8174. http://dx.doi.org/10.3390/s23198174.

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Cybersecurity is a critical issue in today’s internet world. Classical security systems, such as firewalls based on signature detection, cannot detect today’s sophisticated zero-day attacks. Machine learning (ML) based solutions are more attractive for their capabilities of detecting anomaly traffic from benign traffic, but to develop an ML-based anomaly detection system, we need meaningful or realistic network datasets to train the detection engine. There are many public network datasets for ML applications. Still, they have limitations, such as the data creation process and the lack of diver

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Liu, Dazhou, and Younghee Park. "Anonymous Traffic Detection Based on Feature Engineering and Reinforcement Learning." Sensors 24, no. 7 (2024): 2295. http://dx.doi.org/10.3390/s24072295.

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Anonymous networks, which aim primarily to protect user identities, have gained prominence as tools for enhancing network security and anonymity. Nonetheless, these networks have become a platform for adversarial affairs and sources of suspicious attack traffic. To defend against unpredictable adversaries on the Internet, detecting anonymous network traffic has emerged as a necessity. Many supervised approaches to identify anonymous traffic have harnessed machine learning strategies. However, many require access to engineered datasets and complex architectures to extract the desired informatio

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Lu, Jiazhong, Fengmao Lv, Zhongliu Zhuo, et al. "Integrating Traffics with Network Device Logs for Anomaly Detection." Security and Communication Networks 2019 (June 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/5695021.

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Advanced cyberattacks are often featured by multiple types, layers, and stages, with the goal of cheating the monitors. Existing anomaly detection systems usually search logs or traffics alone for evidence of attacks but ignore further analysis about attack processes. For instance, the traffic detection methods can only detect the attack flows roughly but fail to reconstruct the attack event process and reveal the current network node status. As a result, they cannot fully model the complex multistage attack. To address these problems, we present Traffic-Log Combined Detection (TLCD), which is

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Nguyen, Hoanh. "Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network." Journal of Sensors 2020 (June 19, 2020): 1–13. http://dx.doi.org/10.1155/2020/8844348.

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Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper propo

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Alrayes, Fatma S., Mohammed Zakariah, Maha Driss, and Wadii Boulila. "Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis." Sensors 23, no. 20 (2023): 8362. http://dx.doi.org/10.3390/s23208362.

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Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innov

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R., Ramkumar, Rahul R., and Gowtham Sri. "Anomaly Based Approach for Defending Denial of Service Attack in Web Traffic." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1657–64. https://doi.org/10.5281/zenodo.14776346.

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Distributed Denial of Service (DDOS) attacks has become a great threat for internet security. This attackis an advanced form of DOS (Denial of Service) attack. This attack changes its whole origin ID and it gives trouble to find it out and it has become a serious threat for internet security. Almost all traditional services such as bank websites, power resources, medical, educational institutions and military are extended to World Wide Web and thus many people widely use internet services. As many users of Internet is mandatory, network security for attacks are also increasing. Current DDoS at

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Alzyoud, Mazen, Najah Al-shanableh, Eman Nashnush, et al. "Enhanced Machine Learning Based Network Traffic Detection Model for IoT Network." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 19 (2024): 182–98. http://dx.doi.org/10.3991/ijim.v18i19.50315.

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Ensuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in tr

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Liu, Ying, Zhiqiang Wang, Shufang Pang, and Lei Ju. "Distributed Malicious Traffic Detection." Electronics 13, no. 23 (2024): 4720. http://dx.doi.org/10.3390/electronics13234720.

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With the wide deployment of edge devices, distributed network traffic data are rapidly increasing. Traditional detection methods for malicious traffic rely on centralized training, in which a single server is often used to aggregate private traffic data from edge devices, so as to extract and identify features. However, these methods face difficult data collection, heavy computational complexity, and high privacy risks. To address these issues, this paper proposes a federated learning-based distributed malicious traffic detection framework, FL-CNN-Traffic. In this framework, edge devices utili

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Li, Ming, Dezhi Han, Xinming Yin, Han Liu, and Dun Li. "Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features." Security and Communication Networks 2021 (August 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/7045823.

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With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic

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Tao, Zhimin, Wei Quan, and Hua Wang. "Innovative Smart Road Stud Sensor Network Development for Real-Time Traffic Monitoring." Journal of Advanced Transportation 2022 (May 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/8830276.

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Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detectio

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Han, Gang, Haohe Zhang, Zhongliang Zhang, Yan Ma, and Tiantian Yang. "AI-Based Malicious Encrypted Traffic Detection in 5G Data Collection and Secure Sharing." Electronics 14, no. 1 (2024): 51. https://doi.org/10.3390/electronics14010051.

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With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and una

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Aladaileh, Mohammad Adnan, Mohammed Anbar, Ahmed J. Hintaw, et al. "Effectiveness of an Entropy-Based Approach for Detecting Low- and High-Rate DDoS Attacks against the SDN Controller: Experimental Analysis." Applied Sciences 13, no. 2 (2023): 775. http://dx.doi.org/10.3390/app13020775.

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Software-defined networking (SDN) is a unique network architecture isolating the network control plane from the data plane, offering programmable elastic features that allow network operators to monitor their networks and efficiently manage them. However, the new technology is security deficient. A DDoS attack is one of the common attacks that threaten SDN controllers, leading to the degradation or even collapse of the entire SDN network. Entropy-based approaches and their variants are considered the most efficient approaches to detecting DDoS attacks on SDN controllers. Therefore, this work a

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Fotiadou, Konstantina, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Sofia Tsekeridou, and Theodore Zahariadis. "Network Traffic Anomaly Detection via Deep Learning." Information 12, no. 5 (2021): 215. http://dx.doi.org/10.3390/info12050215.

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Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs that were acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system. pfSense integrates several powerful security services such as firewall, URL filtering, and virtual private networking among others. The main goal of this study is to analyse t

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Zhang, Hengyuan, Suyao Zhao, Ruiheng Liu, Wenlong Wang, Yixin Hong, and Runjiu Hu. "Automatic Traffic Anomaly Detection on the Road Network with Spatial-Temporal Graph Neural Network Representation Learning." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/4222827.

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Traffic anomaly detection is an essential part of an intelligent transportation system. Automatic traffic anomaly detection can provide sufficient decision-support information for road network operators, travelers, and other stakeholders. This research proposes a novel automatic traffic anomaly detection method based on spatial-temporal graph neural network representation learning. We divide traffic anomaly detection into two steps: first is learning the implicit graph feature representation of multivariate time series of traffic flows based on a graph attention model to predict the traffic st

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H R, Bhargav. "Comparison of Machine Learning and Deep Learning algorithms for Detecting Intrusions in Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1863–70. http://dx.doi.org/10.22214/ijraset.2022.45588.

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Abstract: Due to the introduction of the devices for networking with the fast internet development in earlier years, the safety of the networks has developed to be important in this contemporary age. Intrusion Detection Systems are used in identifying unapproved, unacquainted and traffic that is suspicious through networks. This project pursues the anomaly detection through the design of a hybrid model that classifies a network traffic first either as benign or intrusive. When the traffic is established as intrusive, the model additionally detects the intrusive traffic category traveling throu

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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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Yeh, Tien-Wen, Huei-Yung Lin, and Chin-Chen Chang. "Traffic Light and Arrow Signal Recognition Based on a Unified Network." Applied Sciences 11, no. 17 (2021): 8066. http://dx.doi.org/10.3390/app11178066.

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We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The result

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

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

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Alabsi, Basim Ahmad, Mohammed Anbar, and Shaza Dawood Ahmed Rihan. "Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks." Sensors 23, no. 12 (2023): 5644. http://dx.doi.org/10.3390/s23125644.

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The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of critical services and financial losses. In this paper, we propose an Intrusion Detection System (IDS) based on a Conditional Tabular Generative Adversarial Network (CTGAN) for detecting DDoS and DoS attacks on IoT networks. Our CGAN-based IDS utilizes a generator network to produce synthetic traffic that mimics legitimate traffic patterns, while the d

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S, Dr Brindha, and Ms Dhamayanthi A. "Network Based Intrusion Detection using Convolutional Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42812.

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The diversification of wireless network traffic attack characteristics has led to the problems what traditional intrusion detection technology with high false positive rate, low detection efficiency, and poor generalization ability. In order to enhance the security and improve the detection ability of malicious intrusion behaviour in a wireless network, this paper proposes a wireless network intrusion detection method based on convolutional neural network (CNN). First, the network traffic data is characterized and pre-processed, then modelled the network intrusion traffic data by CNN. The low-

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Tian, Hui, Jingtian Liu, and Meimei Ding. "Promising techniques for anomaly detection on network traffic." Computer Science and Information Systems 14, no. 3 (2017): 597–609. http://dx.doi.org/10.2298/csis170201018h.

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In various networks, anomaly may happen due to network breakdown, intrusion detection, and end-to-end traffic changes. To detect these anomalies is important in diagnosis, fault report, capacity plan and so on. However, it?s challenging to detect these anomalies with high accuracy rate and time efficiency. Existing works are mainly classified into two streams, anomaly detection on link traffic and on global traffic. In this paper we discuss various anomaly detection methods on both types of traffic and compare their performance.

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Do Xuan, Cho, and Duc Duong. "Optimization of APT attack detection based on a model combining ATTENTION and deep learning." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 4135–51. http://dx.doi.org/10.3233/jifs-212570.

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Nowadays, early detecting and warning Advanced Persistent Threat (APT) attacks is a major challenge for intrusion monitoring and prevention systems. Current studies and proposals for APT attack detection often focus on combining machine-learning techniques and APT malware behavior analysis techniques based on network traffic. To improve the efficiency of APT attack detection, this paper proposes a new approach based on a combination of deep learning networks and ATTENTION networks. The proposed process for APT attack detection in this study is as follows: Firstly, all data of network traffic i

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Abuadlla, Yousef, Goran Kvascev, Slavko Gajin, and Zoran Jovanovic. "Flow-based anomaly intrusion detection system using two neural network stages." Computer Science and Information Systems 11, no. 2 (2014): 601–22. http://dx.doi.org/10.2298/csis130415035a.

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Computer systems and networks suffer due to rapid increase of attacks, and in order to keep them safe from malicious activities or policy violations, there is need for effective security monitoring systems, such as Intrusion Detection Systems (IDS). Many researchers concentrate their efforts on this area using different approaches to build reliable intrusion detection systems. Flow-based intrusion detection systems are one of these approaches that rely on aggregated flow statistics of network traffic. Their main advantages are host independence and usability on high speed networks, since the m

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Globa, Larysa, Andrii Astrakhantsev, and Serhii Tsukanov. "Classification of network traffic using machine learning methods." Problemi telekomunìkacìj, no. 2(33) (December 25, 2023): 3–13. http://dx.doi.org/10.30837/pt.2023.2.01.

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The growth of traffic sources and their diversity leads to increased traffic volumes. This makes existing traffic classification methods less effective. In addition, the expansion of the range of services provided leads to the emergence of new threats and vulnerabilities in the network. The task of detecting threats at an early stage is very important, as losses from threats have increased significantly worldwide in recent years, and early detection will help minimize possible risks. At the same time, implementing artificial intelligence software into all network elements, as part of the 5G/6G

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Tatarnikova, Tatyana M., and Pavel Yu Bogdanov. "Metric characteristics of anomalous traffic detection in internet of things." T-Comm 16, no. 1 (2022): 15–21. http://dx.doi.org/10.36724/2072-8735-2022-16-1-15-21.

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The urgent problem of timely detection of abnormal traffic in the Internet of Things networks, which wastes the energy of sensor devices, is discussed. Anomalous traffic means traffic that contains malicious software that implements an attacking effect on the nodes of the Internet of Things. Timely detection of abnormal traffic contributes to the preservation of the service life and, accordingly, the performance of the services provided by the Internet of Things. The subject of this research is the application of metric characteristics to detect abnormal traffic in the Internet of Things netwo

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Teja Gollapalli, Venkata Surya, and Thanjaivadivel M. "DCN and TCN-Based Intelligent SDN Solutions for Cloud Networks: A Deep Learning Approach to Traffic Optimization." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 1 (2020): 161–67. https://doi.org/10.54660/.ijmrge.2020.1.1.161-167.

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With the increasing complexity and scale of cloud-based networks, the optimization of network traffic and the enhancement of security in Software-Defined Networking (SDN) environments have become critical challenges. Traditional SDN solutions often struggle to handle dynamic traffic patterns and real-time anomaly detection efficiently. This paper proposes a hybrid framework that integrates Deep Convolutional Networks (DCNs) and Temporal Convolutional Networks (TCNs) to address these challenges by improving both traffic optimization and anomaly detection. The proposed framework is trained and e

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Son, Nguyen Hong, and Ha Thanh Dung. "A Lightweight Method for Detecting Cyber Attacks in High-traffic Large Networks based on Clustering Techniques." International journal of Computer Networks & Communications 15, no. 01 (2023): 35–51. http://dx.doi.org/10.5121/ijcnc.2023.15103.

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Protecting information systems is a difficult and long-term task. The size and traffic intensity of computer networks are diverse and no one protection solution is universal for all cases. A certain solution protects well in the campus network, but it is unlikely to protect well in the service provider's network. A key component of a cyber defence system is a network attack detector. This component needs to be designed to have a good way to scale detection capabilities with network size and traffic intensity beyond the size and intensity of a campus network. From this point of view, this paper

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