A Review on Real-Time Network Traffic Monitoring and Anomaly Detection System : A Comprehensive Study with User-Friendly Interface and Historical Analysis Capabilities

Authors

  • Sakshi Bakhare Department of Computer Science and Engineering, BDCE, Sevagram, Wardha, Maharashtra, India Author
  • Dr. Sudhir W. Mohod Professor & HOD at Department of Computer Science and Engineering, BDCE, Sevagram, Wardha, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRSET

Keywords:

Network Traffic Monitoring, Anomaly Detection System, User-Friendly Interface Historical Analysis, Comprehensive Study

Abstract

Protecting the integrity and security of computer networks is crucial in an era of all-pervasive digital connectedness. The user-friendly interface and powerful historical analytical capabilities of the Real-Time Network Traffic Monitoring and Anomaly Detection System are highlighted in this review paper's thorough investigation of the system. This technology takes the stage in a setting where conventional security measures frequently fall short against developing threats that blend in with regular network traffic. It provides experienced administrators and newcomers with real-time insights into network traffic. Additionally, its skills for historical analysis come in handy for post-incident investigations and boosting general network security. This assessment emphasizes the system's crucial function in thwarting existing and new network threats, establishing it as a cornerstone of successful cybersecurity plans for both businesses and private citizens. 

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References

Xingyu Gong, Ke Cao, Na Li, Pengtao Jia, "Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN", Computational Intelligence and Neuroscience, vol. 2022, Article ID 8315442, 9 pages, 2022. https://doi.org/10.1155/2022/8315442

Qian Ma, Cong Sun, Baojiang Cui, "A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering", Security and Communication Networks, vol. 2021, Article ID 2170788, 11 pages, 2021. https://doi.org/10.1155/2021/2170788

Alam, Shumon & Alam, Yasin & Cui, Suxia & Akujuobi, Cajetan. (2023). Data-Driven Network Analysis for Anomaly Traffic Detection. Sensors. 23. 8174. 10.3390/s23198174.

Liu, Haitao & Wang, Haifeng. (2023). Real-Time Anomaly Detection of Network Traffic Based on CNN. Symmetry. 15. 1205. 10.3390/sym15061205.

Huang, Yanling & Huang, Liusong. (2023). Design of Network Traffic Anomaly Monitoring System Based on Data Mining. 10.1007/978-3-031-28787-9_41.

Duan, Xueyuan & Fu, Yu & Wang, Kun. (2023). Data Preprocessing Technology in Network Traffic Anomaly Detection. 10.1007/978-981-99-0880-6_25.

Patel, Niranjan & Hiwarkar, Tryambak. (2022). Design and Analysis of System to Detect Anomaly from Network Traffic to Improve the Security and Improve Performance. International Journal of Computer Science and Mobile Computing. 11. 87-104. 10.47760/ijcsmc.2022.v11i06.007.

Zhao, Ying & Chen, Junjun & Wu, Di & Teng, Jian & Sharma, Nabin & Sajjanhar, Atul & Blumenstein, Michael. (2019). Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern. Information. 10. 262. 10.3390/info10080262.

Wang, Wei & Zhang, Xiangliang & Shi, Wenchang & Lian, Shiguo & Feng, Dengguo. (2011). Network Traffic Monitoring, Analysis and Anomaly Detection. IEEE Network. 25. 6-7. 10.1109/MNET.2011.5772054.

Wang, Zhurong & Zhou, Jing & Wang, Zhanmin & Hei, Xinhong. (2023). Research on Network Traffic Anomaly Detection for Class Imbalance. 10.1007/978-981-99-0301-6_11.

Chih-Yuan Lin, Simin Nadjm-Tehrani,Protocol study and anomaly detection for server-driven traffic in SCADA networks, International Journal of Critical Infrastructure Protection,Volume 42, 2023,100612, ISSN 1874-5482, https://doi.org/10.1016/j.ijcip.2023.100612.

Xin Yue, Guangming Bo, Jianxun Zhang,Research and Application of Network Anomaly Traffic Detection System, Procedia Computer Science,Volume 208,2022,Pages524531,ISSN18770509, https://doi.org/10.1016/j.procs.2022.10.072.

Łukasz Wawrowski, Marcin Michalak, Andrzej Białas, Rafał Kurianowicz, Marek Sikora, Mariusz Uchroński, Adrian Kajzer,Detecting anomalies and attacks in network traffic monitoring with classification methods and XAI-based explainability, Procedia Computer Science, Volume 192, 2021, Pages 2259-2268, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2021.08.239.

Ahmed Tamer Assy, Yahia Mostafa, Ahmed Abd El-khaleq, Maggie Mashaly, Anomaly-Based Intrusion Detection System using One-Dimensional Convolutional Neural Network, Procedia Computer Science, Volume 220, 2023, Pages 78-85, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.013.

Llorenç Cerdà-Alabern, Gabriel Iuhasz, Gabriele Gemmi, Anomaly detection for fault detection in wireless community networks using machine learning, Computer Communications, Volume 202, 2023, Pages 191-203,ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2023.02.019.

Wenping Lei, Chenyang Li, Xinmin Dong, Junhui Wang, Huajie Liu, "Multi Working Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder", Shock and Vibration, vol. 2023, Article ID 2332669, 14 pages, 2023. https://doi.org/10.1155/2023/2332669

Xiaodan Xu, Huawen Liu, Minghai Yao, "Recent Progress of Anomaly Detection", Complexity, vol. 2019, Article ID 2686378, 11 pages, 2019. https://doi.org/10.1155/2019/2686378

Zeyuan Fu, "Computer Network Intrusion Anomaly Detection with Recurrent Neural Network", Mobile Information Systems, vol. 2022, Article ID 6576023, 11 pages, 2022. https://doi.org/10.1155/2022/6576023

Xingyu Gong, Ke Cao, Na Li, Pengtao Jia, "Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN", Computational Intelligence and Neuroscience, vol. 2022, Article ID 8315442, 9 pages, 2022. https://doi.org/10.1155/2022/8315442

Leila Khatibzadeh, Zarrintaj Bornaee, Abbas Ghaemi Bafghi, "Applying Catastrophe Theory for Network Anomaly Detection in Cloud Computing Traffic", Security and Communication Networks, vol. 2019, Article ID 5306395, 11 pages, 2019. https://doi.org/10.1155/2019/5306395

Taimur Bakhshi, Bogdan Ghita, "Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning", Security and Communication Networks, vol. 2021, Article ID 5363750, 16 pages, 2021. https://doi.org/10.1155/2021/5363750

Feilu Hang, Wei Guo, Hexiong Chen, Linjiang Xie, Xiaoyu Bai, Yao Liu, "Network Intrusion Anomaly Detection Model Based on Multi Classifier Fusion Technology", Mobile Information Systems, vol. 2023, Article ID 1594622, 11 pages, 2023. https://doi.org/10.1155/2023/1594622

Yongwei Meng, Tao Qin, Shancang Li, Pinghui Wang, "Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection", Security and Communication Networks, vol. 2022, Article ID 9139321, 17 pages, 2022. https://doi.org/10.1155/2022/9139321

Xindong Duan, "Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm", Mathematical Problems in Engineering, vol. 2022, Article ID 4751844, 9 pages, 2022. https://doi.org/10.1155/2022/4751844

Chuitian Rong, Shuxin OuYang, Huabo Sun, "Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism", Mobile Information Systems, vol. 2022, Article ID 8378187, 14 pages, 2022. https://doi.org/10.1155/2022/8378187

Gang Long, Zhaoxin Zhang, "Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction", Computational Intelligence and Neuroscience, vol. 2023, Article ID 3316642, 12 pages, 2023. https://doi.org/10.1155/2023/3316642

S. T. Zhang, X. B. Lin, L. Wu, Y. Q. Song, N. D. Liao, Z. H. Liang, "Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System", Mathematical Problems in Engineering, vol. 2020, Article ID 7219659, 21 pages, 2020. https://doi.org/10.1155/2020/7219659

Renjie Li, Zhou Zhou, Xuan Liu, Da Li, Wei Yang, Shu Li, Qingyun Liu, "GTF: An Adaptive Network Anomaly Detection Method at the Network Edge", Security and Communication Networks, vol. 2021, Article ID 3017797, 12 pages, 2021. https://doi.org/10.1155/2021/3017797

Dan He, Jiwon Kim, Hua Shi, Boyu Ruan, Autonomous anomaly detection on traffic flow time series with reinforcement learning, Transportation Research Part C: Emerging Technologies, Volume 150, 2023, 104089, ISSN 0968-090X, https://doi.org/10.1016/j.trc.2023.104089.

Zishuai Cheng, Baojiang Cui, Tao Qi, Wenchuan Yang, Junsong Fu, "An Improved Feature Extraction Approach for Web Anomaly Detection Based on Semantic Structure", Security and Communication Networks, vol. 2021, Article ID 6661124, 11 pages, 2021. https://doi.org/10.1155/2021/6661124

V. Anupriya, S. Sangavi, P. C. Shivani and P. Vinu Dharany, "An Efficient Approach for Vehicle Traffic Monitoring by Collaborating Vehicular Mobility Module and Network Simulator 3," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 379-384, doi: 10.1109/ICICCS48265.2020.9121007.

S. Anveshrithaa and K. Lavanya, "Real-Time Vehicle Traffic Analysis using Long Short Term Memory Networks in Apache Spark," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-5, doi: 10.1109/ic-ETITE47903.2020.97.

M. F. AbdelHaq and A. Salman, "Wireless Sensor Network for Traffic Monitoring," 2020 International Conference on Promising Electronic Technologies (ICPET), Jerusalem, Palestine, 2020, pp. 16-21, doi: 10.1109/ICPET51420.2020.00012.

D. Masuda, R. Shinkuma, Y. Inagaki and E. Oki, "Blockchain framework for real-time streaming data generated in image sensor networks for smart monitoring," 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France, 2020, pp. 217-221, doi: 10.1109/BRAINS49436.2020.9223311.

S. SASI PRIYA, S. Rajarajeshwari, K. Sowmiya and P. Vinesha, "Road Traffic Condition Monitoring using Deep Learning," 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 330-335, doi: 10.1109/ICICT48043.2020.9112408.

L. F. P. Oliveira, P. D. G. Luz and L. T. Manera, "Development of a Wireless Traffic Light Controller System, with Real-Time Traffic Monitoring and Green Wave Coordination applied to Smart Cities," 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentina, 2020, pp. 1-1, doi: 10.1109/ARGENCON49523.2020.9505576.

K. B. Q. Chowdhury, M. R. Khan and M. A. Razzak, "Automation of Rail Gate Control with Obstacle Detection and Real Time Tracking in the Development of Bangladesh Railway," 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), Kuching, Malaysia, 2020, pp. 1-6, doi: 10.1109/R10-HTC49770.2020.9356986.

Y. Zhou et al., "HyperSight: Towards Scalable, High-Coverage, and Dynamic Network Monitoring Queries," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 6, pp. 1147-1160, June 2020, doi: 10.1109/JSAC.2020.2986690.

W. Guo, J. Li, X. Liu and Y. Yang, "Privacy-Preserving Compressive Sensing for Real-Time Traffic Monitoring in Urban City," in IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14510-14522, Dec. 2020, doi: 10.1109/TVT.2020.3042794.

B. -H. Oh, S. Vural and N. Wang, "A Lightweight Scheme of Active-Port-Aware Monitoring in Software-Defined Networks," in IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2888-2901, Sept. 2021, doi: 10.1109/TNSM.2021.3066273.

S. Seid, M. Zennaro, M. Libsie, E. Pietrosemoli and P. Manzoni, "A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring," 2020 16th International Conference on Mobility, Sensing and Networking (MSN), Tokyo, Japan, 2020, pp. 762-767, doi: 10.1109/MSN50589.2020.00130.

L. Khoukhi and R. Khatoun, "Safe Traffic Adaptation Model in Wireless Mesh Networks," 2020 4th Cyber Security in Networking Conference (CSNet), Lausanne, Switzerland, 2020, pp. 1-4, doi: 10.1109/CSNet50428.2020.9265456.

W. A. C. J. K. Chandrasekara, R. M. K. T. Rathnayaka and L. L. G. Chathuranga, "A Real-Time Density-Based Traffic Signal Control System," 2020 5th International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2020, pp. 1-6, doi: 10.1109/ICITR51448.2020.9310906.

V. Demianiuk, S. Gorinsky, S. I. Nikolenko and K. Kogan, "Robust Distributed Monitoring of Traffic Flows," in IEEE/ACM Transactions on Networking, vol. 29, no. 1, pp. 275-288, Feb. 2021, doi: 10.1109/TNET.2020.3034890.

M. Tamai, A. Hasegawa and H. Yokoyama, "Comprehensive Visualization of Physical and MAC Layer Data for Wireless Network Monitoring," 2020 29th International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, 2020, pp. 1-2, doi: 10.1109/ICCCN49398.2020.9209677.

S. M. A. Karim, N. Ranjan and D. Shah, "A Scalable Approach to Time Series Anomaly Detection & Failure Analysis for Industrial Systems," 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020, pp. 0678-0683, doi: 10.1109/CCWC47524.2020.9031262.

C. Boudehenn, J. -C. Cexus and A. A. Boudraa, "A Data Extraction Method for Anomaly Detection in Naval Systems," 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), Dublin, Ireland, 2020, pp. 1-4, doi: 10.1109/CyberSA49311.2020.9139656.

S. Zhu, H. S. Yuchi and Y. Xie, "Adversarial Anomaly Detection for Marked Spatio-Temporal Streaming Data," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 8921-8925, doi: 10.1109/ICASSP40776.2020.9053837.

Z. S. Lee, H. Guo and L. Zhou, "Rail System Anomaly Detection via Machine Learning Approaches," 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, Japan, 2020, pp. 824-828, doi: 10.1109/TENCON50793.2020.9293809.

M. Zhao, R. Furuhata, M. Agung, H. Takizawa and T. Soma, "Failure Prediction in Datacenters Using Unsupervised Multimodal Anomaly Detection," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 3545-3549, doi: 10.1109/BigData50022.2020.9378419.

M. V. Ngo, T. Luo, H. Chaouchi and T. Q. S. Quek, "Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing," 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), Singapore, Singapore, 2020, pp. 1227-1230, doi: 10.1109/ICDCS47774.2020.00191.

K. Sakata, S. Fujita, K. Sawada, S. Shin, I. Maeta and S. Hosokawa, "On the multiple anomaly detection of a third-party monitoring system for secured control," 2020 IEEE/SICE International Symposium on System Integration (SII), Honolulu, HI, USA, 2020, pp. 1254-1258, doi: 10.1109/SII46433.2020.9026265.

M. Babaei and M. Imani, "Anomaly Detection Improvement Using Sparse Representation and Morphological Profile," 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1-5, doi: 10.1109/ICSPIS51611.2020.9349597.

I. Dutt, S. Borah and I. K. Maitra, "Immune System Based Intrusion Detection System (IS-IDS): A Proposed Model," in IEEE Access, vol. 8, pp. 34929-34941, 2020, doi: 10.1109/ACCESS.2020.2973608.

W. Hao, T. Yang and Q. Yang, "Hybrid Statistical-Machine Learning for Real-Time Anomaly Detection in Industrial Cyber–Physical Systems," in IEEE Transactions on Automation Science and Engineering, vol. 20, no. 1, pp. 32-46, Jan. 2023, doi: 10.1109/TASE.2021.3073396.

T. M. Tran, T. N. Vu, T. V. Nguyen and K. Nguyen, "UIT-ADrone: A Novel Drone Dataset for Traffic Anomaly Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 5590-5601, 2023, doi: 10.1109/JSTARS.2023.3285905.

P. Nandi, A. Mishra, P. Kedia and M. Rao, "Design of a real-time autonomous in-cabin sensory system to detect passenger anomaly," 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, 2020, pp. 202-206, doi: 10.1109/IV47402.2020.9304666.

A. Putina and D. Rossi, "Online Anomaly Detection Leveraging Stream-Based Clustering and Real-Time Telemetry," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 839-854, March 2021, doi: 10.1109/TNSM.2020.3037019.

C. Wang and J. Liu, "An Efficient Anomaly Detection for High-Speed Train Braking System Using Broad Learning System," in IEEE Access, vol. 9, pp. 63825-63832, 2021, doi: 10.1109/ACCESS.2021.3074929.

H. Ren, F. Zhou and K. Fujisawa, "Real-Time Automatic Anomaly Detection Approach Designed for Electrified Railway Power System," 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE), Budapest, Hungary, 2021, pp. 116-120, doi: 10.1109/ICMRE51691.2021.9384838.

Shi, Yuanquan & Shen, Hong. (2022). Unsupervised anomaly detection for network traffic using artificial immune networks. Neural Computing and Applications. 34. 10.1007/s00521-022-07156-x.

Wei, Guanglu & Wang, Zhonghua. (2021). Adoption and realization of deep learning in network traffic anomaly detection device design. Soft Computing. 25. 10.1007/s00500-020-05210-1.

Bhuyan, Monowar & Bhattacharyya, Dhruba K & Kalita, Jugal. (2017). Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools. 10.1007/978-3-319-65188-0.

Yang, Dong & Liu, Ze & Wei, Songjie. (2023). Interactive Learning for Network Anomaly Monitoring and Detection with Human Guidance in the Loop. Sensors. 23. 7803. 10.3390/s23187803.

Y. Sun, H. Ochiai and H. Esaki, "Deep Learning-Based Anomaly Detection in LAN from Raw Network Traffic Measurement," 2021 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2021, pp. 1-5, doi: 10.1109/CISS50987.2021.9400241.

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Published

12-05-2024

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Research Articles

How to Cite

[1]
Sakshi Bakhare and Dr. Sudhir W. Mohod, “A Review on Real-Time Network Traffic Monitoring and Anomaly Detection System : A Comprehensive Study with User-Friendly Interface and Historical Analysis Capabilities”, Int J Sci Res Sci Eng Technol, vol. 11, no. 3, pp. 23–41, May 2024, doi: 10.32628/IJSRSET.

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