Relevant bibliographies by topics / Malicious traffic

Contents

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

Academic literature on the topic 'Malicious traffic'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Malicious traffic"

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Thanushiya.S, Kiruthika.S, Mary selja. J, and Mr. JohnLivingston. "Network Traffic Analysis To Classify Malicious And Non-Malicious Traffic." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 205–9. https://doi.org/10.47392/irjaeh.2025.0028.

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In the face of increasingly sophisticated cyber threats, ensuring network security is crucial for organizations aiming to protect sensitive data, maintain service continuity, and avoid financial losses. Effective network traffic monitoring is essential for identifying malicious activities that can compromise network integrity. Traditional methods, however, often struggle to keep up with evolving attack techniques, especially when real-time detection and rapid response are needed. This project presents an innovative network traffic analysis system that integrates the capabilities of Wireshark,
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Li, Minghui, Zhendong Wu, Keming Chen, and Wenhai Wang. "Adversarial Malicious Encrypted Traffic Detection Based on Refined Session Analysis." Symmetry 14, no. 11 (2022): 2329. http://dx.doi.org/10.3390/sym14112329.

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The detection of malicious encrypted traffic is an important part of modern network security research. The producers of the current malware do not pay attention to the fact that malicious encrypted traffic can also be detected; they do not construct further adversarial malicious encrypted traffic to deceive existing malicious encrypted traffic detection methods. However, with the increasing confrontation between attack and defense, adversarial malicious encrypted traffic samples will appear gradually, which will make the existing malicious encrypted traffic detection methods obsolete. In this
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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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Boukhtouta, Amine, Nour-Eddine Lakhdari, Serguei A. Mokhov, and Mourad Debbabi. "Towards Fingerprinting Malicious Traffic." Procedia Computer Science 19 (2013): 548–55. http://dx.doi.org/10.1016/j.procs.2013.06.073.

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Yang, Jin, Xinyun Jiang, Gang Liang, Siyu Li, and Zicheng Ma. "Malicious Traffic Identification with Self-Supervised Contrastive Learning." Sensors 23, no. 16 (2023): 7215. http://dx.doi.org/10.3390/s23167215.

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As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is able to overcome the shortcomings of traditional methods that rely on labeled samples and is able to learn data feature representations carrying semantic information from unlabeled data, thus improving the model accuracy. In this paper, a new malicious traffic feature extraction mod
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Zhang, Shuai, Yu Fan, Haoyi Zhou, and Bo Li. "MalDetectFormer: Leveraging Sparse SpatioTemporal Information for Effective Malicious Traffic Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22533–41. https://doi.org/10.1609/aaai.v39i21.34411.

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Malicious traffic detection is one of the main challenges in the field of cybersecurity. Although modern deep learning methods have made progress in identifying malicious traffic, they often overlook the persistent nature of attack behaviors, making it difficult to distinguish between malicious and normal traffic at a single observation point. To address this issue, we propose MalDetectFormer, which aims to accurately capture the spatiotemporal dynamics of malicious traffic. By incorporating a sparse attention mechanism, MalDetectFormer can efficiently focus on key characteristics of traffic n
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Bie, Mu, and Haoyu Ma. "Malicious Mining Behavior Detection System of Encrypted Digital Currency Based on Machine Learning." Mathematical Problems in Engineering 2021 (November 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/2983605.

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With the gradual increase of malicious mining, a large amount of computing resources are wasted, and precious power resources are consumed maliciously. Many detection methods to detect malicious mining behavior have been proposed by scholars, but most of which have pure defects and need to collect sensitive data (such as memory and register data) from the detected host. In order to solve these problems, a malicious mining detection system based on network timing signals is proposed. When capturing network traffic, the system does not need to know the contents of data packets but only collects
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Hou, Botao, Ke Zhang, Xiaojun Zuo, Jianli Zhao, and Bo Xi. "PIoT Malicious Traffic Detection Method Based on GAN Sample Enhancement." Security and Communication Networks 2022 (March 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/9223412.

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To solve the problem of network traffic data imbalance under the background of power Internet of things and improve the poor generalization ability of the model, a PIoT malicious traffic detection method based on GAN sample enhancement is developed. Firstly, network traffic samples are preprocessed. Aiming at the imbalance of network traffic, malicious samples generation based on GAN is adopted, which uses the advantages of confrontation training in GAN to generate a small amount of malicious traffic to balance the PIoT malicious traffic. Secondly, 33 features are selected serially to construc
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Wang, Wei, Cheng Sheng Sun, and Jia Ning Ye. "A Method for TLS Malicious Traffic Identification Based on Machine Learning." Advances in Science and Technology 105 (April 2021): 291–301. http://dx.doi.org/10.4028/www.scientific.net/ast.105.291.

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With more and more malicious traffic using TLS protocol encryption, efficient identification of TLS malicious traffic has become an increasingly important task in network security management in order to ensure communication security and privacy. Most of the traditional traffic identification methods on TLS malicious encryption only adopt the common characteristics of ordinary traffic, which results in the increase of coupling among features and then the low identification accuracy. In addition, most of the previous work related to malicious traffic identification extracted features directly fr
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Shi, Zhaolei, Nurbol Luktarhan, Yangyang Song, and Huixin Yin. "TSFN: A Novel Malicious Traffic Classification Method Using BERT and LSTM." Entropy 25, no. 5 (2023): 821. http://dx.doi.org/10.3390/e25050821.

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Traffic classification is the first step in network anomaly detection and is essential to network security. However, existing malicious traffic classification methods have several limitations; for example, statistical-based methods are vulnerable to hand-designed features, and deep learning-based methods are vulnerable to the balance and adequacy of data sets. In addition, the existing BERT-based malicious traffic classification methods only focus on the global features of traffic and ignore the time-series features of traffic. To address these problems, we propose a BERT-based Time-Series Fea
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Dissertations / Theses on the topic "Malicious traffic"

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Jin, Zhihua. "Visualization of Network Traffic to Detect Malicious Network Activity." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8951.

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<p>Today, enormous logging data monitoring the traffics of the Internet is generated everyday. However, the network administrators still have very limited insight into the logging data, mainly due to the lack of efficient analyzing approaches. Most of the existing network monitoring or analysis tools either mainly focus on the throughput of the network in order to assist network structure planning and optimization, which is too high level for security analysis, or dig to too low level into every packet, which is too inefficient in practice. Unfortunately, not all network traffics are legitimat
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Nichols, Tony. "MalWebID_Autodetection and Identification of Malicious Web Hosts Through Live Traffic Analysis." Monterey, California. Naval Postgraduate School, 2013. http://hdl.handle.net/10945/32875.

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Approved for public release; distribution is unlimited<br>This thesis investigates the ability for recently devised packet-level Transmission Control Protocols (TCP) transport classifiers to discover abusive traffic flows, especially those not found via traditional methods, e.g., signatures and real-time blocklists. Transport classification is designed to identify hosts considered to be part of abusive infrastructure without deep packet inspection. A particular focus is to understand the applicability of such methods to live, real-world network traffic obtained from the Naval Postgraduate Scho
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Sandford, Peter. "Inferring malicious network events in commercial ISP networks using traffic summarisation." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9580.

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With the recent increases in bandwidth available to home users, traffic rates for commercial national networks have also been increasing rapidly. This presents a problem for any network monitoring tool as the traffic rate they are expected to monitor is rising on a monthly basis. Security within these networks is para- mount as they are now an accepted home of trade and commerce. Core networks have been demonstrably and repeatedly open to attack; these events have had significant material costs to high profile targets. Network monitoring is an important part of network security, providing in-
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Kälkäinen, J. (Juha). "Collection and analysis of malicious SSH traffic in Oulu University network." Bachelor's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201812053224.

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Secure Shell (SSH) is a commonly used tool by many organizations to establish secure data communication and remote access to systems that store confidential information and resources. Assessing, defending against and studying the different threats the systems using this protocol are subjected to can be done by using a honeypot. This thesis studied malicious SSH traffic directed at Oulu university network by using an SSH honeypot Cowrie. The honeypot was deployed in the panOULU network located at the Oulu University campus. Two other identical honeypots were deployed from different networks to
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Wulff, Tobias. "Evaluation of and Mitigation against Malicious Traffic in SIP-based VoIP Applications in a Broadband Internet Environment." Thesis, University of Canterbury. Computer Science and Software Engineering, 2010. http://hdl.handle.net/10092/5120.

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Voice Over IP (VoIP) telephony is becoming widespread, and is often integrated into computer networks. Because of his, it is likely that malicious software will threaten VoIP systems the same way traditional computer systems have been attacked by viruses, worms, and other automated agents. While most users have become familiar with email spam and viruses in email attachments, spam and malicious traffic over telephony currently is a relatively unknown threat. VoIP networks are a challenge to secure against such malware as much of the network intelligence is focused on the edge devices and acces
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Токмань, І. О. "Інформаційна інтелектуальна технологія детектування шкідливого трафіку в мережах інтернету речей". Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72290.

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У роботі розглянуто ефективність застосування ряду методів та підходів машинного навчання, визначено закономірності та доцільність реалізацій. Розроблено інформаційну систему класифікації мережевого трафіку з урахування наявних проблем та на основі вивчення типових сценаріїв атак за участю IoT-ботнетів. Побудована інтелектуальна модель реалізовано у формі програмного забезпечення, створеного за допомогою інтерактивної оболонки Jupyter Notebook.
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FAVALE, THOMAS. "Strengthening Privacy and Cybersecurity through Anonymization and Big Data." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975701.

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Alberdi, Ion. "Malicious trafic observation using a framework to parallelize and compose midpoint inspection devices." Thesis, Toulouse, INSA, 2010. http://www.theses.fr/2010ISAT0008/document.

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Notre thèse stipule qu'au vu de l'ampleur des agissements malveillants dans l'Internet, les logiciels d'extrémité doivent être surveillés. Pour limiter le nombre de points de surveillance, nous proposons de surveiller les logiciels depuis un point d'interconnexion. Nous avons dans ce but conçu Luth, un outil permettant de composer et de paralléliser un ensemble d'inspecteurs de points d'interconnexion (appelés MI) qui implémentent des mini IDS, IPS ou pare-feux, tout en vérifiant la correction et l'optimalité de ces derniers, à l'aide d'un langage de configuration et des algorithmes associés.
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Alberdi, Ion. "Malicious trafic observation using a framework to parallelize and compose midpoint inspection devices." Electronic Thesis or Diss., Toulouse, INSA, 2010. http://www.theses.fr/2010ISAT0008.

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Notre thèse stipule qu'au vu de l'ampleur des agissements malveillants dans l'Internet, les logiciels d'extrémité doivent être surveillés. Pour limiter le nombre de points de surveillance, nous proposons de surveiller les logiciels depuis un point d'interconnexion. Nous avons dans ce but conçu Luth, un outil permettant de composer et de paralléliser un ensemble d'inspecteurs de points d'interconnexion (appelés MI) qui implémentent des mini IDS, IPS ou pare-feux, tout en vérifiant la correction et l'optimalité de ces derniers, à l'aide d'un langage de configuration et des algorithmes associés.
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Lakhdari, Nour-Eddine. "FINGERPRINTING MALICIOUS IP TRAFFIC." Thesis, 2014. http://spectrum.library.concordia.ca/978357/1/Lakhdari_MASc_S2014.pdf.

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In the new global economy, cyber-attacks have become a central issue. The detection, mitigation and attribution of such cyber-attacks require efficient and practical techniques to fingerprint malicious IP traffic. By fingerprinting, we refer to: (1) the detection of malicious network flows and, (2) the attribution of the detected flows to malware families that generate them. In this thesis, we firstly address the detection problem and solve it by using a classification technique. The latter uses features that exploit only high-level properties of traffic flows and therefore does not rely on de
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Books on the topic "Malicious traffic"

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Gattis, Ryan. System. Pan Macmillan, 2021.

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Gattis, Ryan. The System: A Novel. Picador, 2021.

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System. Pan Macmillan, 2020.

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The System: A Novel. MCD, 2020.

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Book chapters on the topic "Malicious traffic"

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Meda, Lakshmi N. K., and Hamid Jahankhani. "Artificial Intelligence Based Malicious Traffic Detection." In Blockchain and Other Emerging Technologies for Digital Business Strategies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98225-6_2.

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Yu, Yuqi, Hanbing Yan, Yuan Ma, Hao Zhou, and Hongchao Guan. "DeepHTTP: Anomalous HTTP Traffic Detection and Malicious Pattern Mining Based on Deep Learning." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_11.

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AbstractHypertext Transfer Protocol (HTTP) accounts for a large portion of Internet application-layer traffic. Since the payload of HTTP traffic can record website status and user request information, many studies use HTTP protocol traffic for web application attack detection. In this work, we propose DeepHTTP, an HTTP traffic detection framework based on deep learning. Unlike previous studies, this framework not only performs malicious traffic detection but also uses the deep learning model to mine malicious fields of the traffic payload. The detection model is called AT-Bi-LSTM, which is bas
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Kolbusz, J., P. Rozycki, and J. Korniak. "The Simulation of Malicious Traffic Using Self-similar Traffic Model." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23187-2_21.

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Wu, Mengying, Zhendong Wu, Hao Lv, and Jingjing Wang. "A Method of Malicious Bot Traffic Detection." In Cyberspace Safety and Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_6.

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Millar, K., A. Cheng, H. G. Chew, and C. C. Lim. "Deep Learning for Classifying Malicious Network Traffic." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04503-6_15.

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Zhang, Shijun, Shuo Li, and Jing Zeng. "Deep Learning-Based Malicious Illegal Traffic Identification." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1157-8_85.

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Fang, Yong, Yijia Xu, Cheng Huang, Liang Liu, and Lei Zhang. "Against Malicious SSL/TLS Encryption: Identify Malicious Traffic Based on Random Forest." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9343-4_10.

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Kobayashi, Tiago H., Aguinaldo B. Batista, João Paulo S. Medeiros, José Macedo F. Filho, Agostinho M. Brito, and Paulo S. Motta Pires. "Analysis of Malicious Traffic in Modbus/TCP Communications." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03552-4_18.

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Hublikar, Shivaraj, and N. Shekar V. Shet. "Hybrid Malicious Encrypted Network Traffic Flow Detection Model." In Computer Networks and Inventive Communication Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3035-5_28.

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Shen, Yi, Yuhan Zhang, Yuwei Li, et al. "IoT Malicious Traffic Detection Based on Federated Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56580-9_15.

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Conference papers on the topic "Malicious traffic"

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Li, Fuhao, Hongyu Wu, and Jielun Zhang. "Lightweight Diffusion Model for Synthesizing Malicious Network Traffic." In NAECON 2024 - IEEE National Aerospace and Electronics Conference. IEEE, 2024. http://dx.doi.org/10.1109/naecon61878.2024.10670640.

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Siwakoti, Yuba R., and Danda B. Rawat. "Detecting Malicious Traffic using JA3 Fingerprints Attributed ML Approach." In 2024 IEEE 44th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2024. http://dx.doi.org/10.1109/icdcsw63686.2024.00024.

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Wang, Jie, Lili Yang, Jie Wu, and Jemal H. Abawajy. "Clustering analysis for malicious network traffic." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997375.

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Ghafir, Ibrahim, and Vaclav Prenosil. "Blacklist-based malicious IP traffic detection." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342657.

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Djap, Ryandy, Charles Lim, and Kalpin Erlangga Silaen. "Malicious traffic analysis using Markov chain." In ICONETSI '22: International Conference on Engineering and Information Technology for Sustainable Industry. ACM, 2022. http://dx.doi.org/10.1145/3557738.3557849.

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Morais, Sávyo V., and Claudio M. Farias. "INXU: A Flow-Based Intrusion Prevention System for Home IoT Networks." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbseg_estendido.2022.224947.

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Due to the low resources and maintainability in home Internet of Things (IoT) devices, they can represent a risk to end-user’s security and privacy. Several proposals tried to manage new vulnerabilities in this scenario, but it is difficult to keep signatures updated or identify anomalous traffic. To reinforce home IoT security, we propose INXU, a flow-based Intrusion Prevention System that protects home IoT devices by blocking traffic related to well known malicious activities. INXU introduces the concept of Malicious Traffic Description (MTD), a data-model to describe traffic related to mali
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Hussain, Alefiya, Yuri Pradkin, and John Heidemann. "Replay of malicious traffic in network testbeds." In 2013 IEEE International Conference on Technologies for Homeland Security (HST). IEEE, 2013. http://dx.doi.org/10.1109/ths.2013.6699022.

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Ghafir, Ibrahim, and Vaclav Prenosil. "DNS traffic analysis for malicious domains detection." In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2015. http://dx.doi.org/10.1109/spin.2015.7095337.

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Yarochkin, Fyodor, Vladimir Kropotov, Yennun Huang, Guo-Kai Ni, Sy-Yen Kuo, and Ing-Yi Chen. "Investigating DNS traffic anomalies for malicious activities." In 2013 43rd Annual IEEE/IFIP Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2013. http://dx.doi.org/10.1109/dsnw.2013.6615506.

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Ling, Zhen, Junzhou Luo, Kui Wu, Wei Yu, and Xinwen Fu. "TorWard: Discovery of malicious traffic over Tor." In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications. IEEE, 2014. http://dx.doi.org/10.1109/infocom.2014.6848074.

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Reports on the topic "Malicious traffic"

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Bardhan, Shuvo, Mitsuhiro Hatada, James Filliben, Douglas Montgomery, and Alexander Jia. An Evaluation Design for Comparing Netflow Based Network Anomaly Detection Systems Using Synthetic Malicious Traffic. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.tn.2142.

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Harriss, Lydia, and Zara Mir. Misuse of civilian drones. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn610.

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Drones (also known as unmanned aircraft) are flying systems that do not carry a pilot. As the technology has become cheaper and more sophisticated, the use of drones for recreational and commercial purposes has grown, with the Civil Aviation Authority (CAA) reporting a significant increase in the number of permissions obtained for operating commercial drones in the UK. Despite their potential to reduce costs, improve efficiency and provide new services, drones may be misused accidentally or for malicious purposes. For example, reports of drone sightings at Gatwick Airport in December 2018 grou
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