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Journal articles on the topic 'Malicious traffic'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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Haidur, Halyna, Sergii Gakhov, and Dmytro Hamza. "USING SUPPORT VECTORS TO BUILD A RULE-BASED SYSTEM FOR DETECTING MALICIOUS PROCESSES IN AN ORGANISATION'S NETWORK TRAFFIC." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 4 (2024): 90–96. https://doi.org/10.35784/iapgos.6366.

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The growing complexity and sophistication of cyberattacks on organisational information resources and the variety of malware processes in unprotected networks necessitate the development of advanced methods for detecting malicious processes in network traffic. Systems for detecting malicious processes based on machine learning and rule-based methods have their advantages and disadvantages. We have investigated the possibility of using support vectors to create a rule-based system for detecting malicious processes in an organisation's network traffic. We propose a method for building a rule-bas

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Wang, Zhiqiang, Man Li, Haiwen Ou, Shufang Pang, and Ziyan Yue. "A Few-Shot Malicious Encrypted Traffic Detection Approach Based on Model-Agnostic Meta-Learning." Security and Communication Networks 2023 (April 13, 2023): 1–12. http://dx.doi.org/10.1155/2023/3629831.

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Existing malicious encrypted traffic detection approaches need to be trained with many samples to achieve effective detection of a specified class of encrypted traffic data. With the rapid development of encryption technology, various new types of encrypted traffic are emerging and difficult to label. Therefore, it is an urgent problem to train a deep learning model using only a small number of samples to detect new classes of malicious encrypted traffic. This paper proposes a few-shot malicious encrypted traffic detection (FMETD) approach based on model-agnostic meta-learning (MAML), integrat

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Tang, Jian, Zhao Huang, and Chunqiang Li. "MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT." Future Internet 17, no. 8 (2025): 323. https://doi.org/10.3390/fi17080323.

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The rising frequency of network intrusions has significantly impacted critical infrastructures, leading to an increased focus on the detection of malicious network traffic in recent years. However, traditional port-based and classical machine learning-based malicious network traffic detection methods suffer from a dependence on expert experience and limited generalizability. In this paper, we propose a malicious traffic detection method based on an efficient federated learning framework of Bidirectional Encoder Representations from Transformers (BERT), called MT-FBERT. It offers two major adva

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Ferriyan, Andrey, Achmad Husni Thamrin, Keiji Takeda, and Jun Murai. "Encrypted Malicious Traffic Detection Based on Word2Vec." Electronics 11, no. 5 (2022): 679. http://dx.doi.org/10.3390/electronics11050679.

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Network-based intrusion detections become more difficult as Internet traffic is mostly encrypted. This paper introduces a method to detect encrypted malicious traffic based on the Transport Layer Security handshake and payload features without waiting for the traffic session to finish while preserving privacy. Our method, called TLS2Vec, creates words from the extracted features and uses Long Short-Term Memory (LSTM) for inference. We evaluated our method using traffic from three malicious applications and a benign application that we obtained from two publicly available datasets. Our results

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Jung, In-Su, Yu-Rae Song, Lelisa Adeba Jilcha, et al. "Enhanced Encrypted Traffic Analysis Leveraging Graph Neural Networks and Optimized Feature Dimensionality Reduction." Symmetry 16, no. 6 (2024): 733. http://dx.doi.org/10.3390/sym16060733.

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With the continuously growing requirement for encryption in network environments, web browsers are increasingly employing hypertext transfer protocol security. Despite the increase in encrypted malicious network traffic, the encryption itself limits the data accessible for analyzing such behavior. To mitigate this, several studies have examined encrypted network traffic by analyzing metadata and payload bytes. Recent studies have furthered this approach, utilizing graph neural networks to analyze the structural data patterns within malicious encrypted traffic. This study proposed an enhanced e

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Fox, Garett, and Rajendra V. Boppana. "Detection of Malicious Network Flows with Low Preprocessing Overhead." Network 2, no. 4 (2022): 628–42. http://dx.doi.org/10.3390/network2040036.

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Machine learning (ML) is frequently used to identify malicious traffic flows on a network. However, the requirement of complex preprocessing of network data to extract features or attributes of interest before applying the ML models restricts their use to offline analysis of previously captured network traffic to identify attacks that have already occurred. This paper applies machine learning analysis for network security with low preprocessing overhead. Raw network data are converted directly into bitmap files and processed through a Two-Dimensional Convolutional Neural Network (2D-CNN) model

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Zheng, Juan, Zhiyong Zeng, and Tao Feng. "GCN-ETA: High-Efficiency Encrypted Malicious Traffic Detection." Security and Communication Networks 2022 (January 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/4274139.

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Encrypted network traffic is the principal foundation of secure network communication, and it can help ensure the privacy and integrity of confidential information. However, it hides the characteristics of the data, increases the difficulty of detecting malicious traffic, and protects such malicious behavior. Therefore, encryption alone cannot fundamentally guarantee information security. It is also necessary to monitor traffic to detect malicious actions. At present, the more commonly used traffic classification methods are the method based on statistical features and the method based on grap

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18

Wang, Maoli, Bowen Zhang, Xiaodong Zang, Kang Wang, and Xu Ma. "Malicious Traffic Classification via Edge Intelligence in IIoT." Mathematics 11, no. 18 (2023): 3951. http://dx.doi.org/10.3390/math11183951.

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The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for detecting malicious traffic. Although it is a labor-intensive and time-consuming process to gather large labeled datasets, the majority of prior studies on the classification of malicious traffic use supervised learning approaches and provide decent classification results when a substantial quantity of

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19

Płaczek, Bartłomiej, Marcin Bernas, and Marcin Cholewa. "A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks." Information 11, no. 11 (2020): 496. http://dx.doi.org/10.3390/info11110496.

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This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introdu

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Liu, Ming, Qichao Yang, Wenqing Wang, and Shengli Liu. "Semi-Supervised Encrypted Malicious Traffic Detection Based on Multimodal Traffic Characteristics." Sensors 24, no. 20 (2024): 6507. http://dx.doi.org/10.3390/s24206507.

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The exponential growth of encrypted network traffic poses significant challenges for detecting malicious activities online. The scale of emerging malicious traffic is significantly smaller than that of normal traffic, and the imbalanced data distribution poses challenges for detection. However, most existing methods rely on single-category features for classification, which struggle to detect covert malicious traffic behaviors. In this paper, we introduce a novel semi-supervised approach to identify malicious traffic by leveraging multimodal traffic characteristics. By integrating the sequence

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21

Arivudainambi, D., K. A. Varun Kumar, and Suresh Chandra Satapathy. "Correlation based malicious traffic analysis system." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (2021): 195–200. http://dx.doi.org/10.3233/kes-210064.

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Artificial intelligence methods have often been applied to carry out specific functions or errands in the cyber-defense realm. However, as adversary methods become more complex and difficult to divine, piecemeal efforts to understand cyber-attacks, and malware-based attacks in particular, are not providing sufficient means for malware analysts to understand the past, present and future distinctiveness of malware. Because, most of the malware communications take place-utilizing services. These services are completely anonymous and monitoring such services is a hard task. To address this issue,

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Shin, Dong Hyuk, Kwang Kue An, Sung Chune Choi, and Hyoung-Kee Choi. "Malicious Traffic Detection Using K-means." Journal of Korean Institute of Communications and Information Sciences 41, no. 2 (2016): 277–84. http://dx.doi.org/10.7840/kics.2016.41.2.277.

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Shekhawat, Anish Singh, Fabio Di Troia, and Mark Stamp. "Feature analysis of encrypted malicious traffic." Expert Systems with Applications 125 (July 2019): 130–41. http://dx.doi.org/10.1016/j.eswa.2019.01.064.

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Mishra, Shailendra. "Network Traffic Analysis Using Machine Learning Techniques in IoT Networks." International Journal of Software Innovation 9, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijsi.289172.

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Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and preve

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Liu, Jianyi, Lanting Wang, Wei Hu, et al. "Spatial-Temporal Feature with Dual-Attention Mechanism for Encrypted Malicious Traffic Detection." Security and Communication Networks 2023 (January 7, 2023): 1–13. http://dx.doi.org/10.1155/2023/7117863.

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While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification. How to effectively detect encrypted malicious traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. Most of the current research only uses a single CNN, RNN, and SAE network to detect encrypted malicious traffic, which does not consider the forward and backward correlation between data packets, so it is difficult to effectively identi

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Wang, Jiayu, Xuehu Yan, Lintao Liu, Longlong Li, and Yongqiang Yu. "CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN." Sensors 22, no. 14 (2022): 5243. http://dx.doi.org/10.3390/s22145243.

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Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is imbalanced, which makes the algorithm have a low identification rate for small categories of malicious traffic samples. This paper presents a traffic sample synthesizing model named Conditional Tabular Traffic Generative Adversarial Network (CTTGAN), which uses a Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to expand the s

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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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Sandriana, Ari, Rianto Rianto, and Firmansyah Maulana. "Klasifikasi serangan malware terhadap lalu lintas jaringan Internet of Things menggunakan Algoritma K-Nearest Neighbour (K-NN)." E-JOINT (Electronica and Electrical Journal Of Innovation Technology) 3, no. 1 (2022): 12–22. http://dx.doi.org/10.35970/e-joint.v1i3.1336.

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Penerapan internet of things (IoT) dapat membuat semuanya terhubung ke internet tetapi sistem IoT dapat menjadi sasaran yang sangat mudah untuk disusupi penyerang dengan menggunakan malware, lebih dari 1,6 miliar atau tepatnya 1.637.973.022 anomali traffic atau serangan siber (cyber attack) yang terjadi diseluruh wilayah Indonesia sepanjang tahun 2021, teknik machine learning dapat dimanfaatkan untuk proses pengklasifikasian anomali traffic dengan menggunakan algoritma k-nearest neighbour (KNN) sehingga dapat membedakan data traffic yang bersifat benign atau malicious. Data anomali traffic yan

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Ari Sandriana, Rianto, and Firmansyah Maulana. "Klasifikasi serangan Malware terhadap Lalu Lintas Jaringan Internet of Things menggunakan Algoritma K-Nearest Neighbour (K-NN)." E-JOINT (Electronica and Electrical Journal Of Innovation Technology) 3, no. 1 (2022): 12–22. http://dx.doi.org/10.35970/e-joint.v3i1.1559.

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Penerapan Internet of Things (IoT) dapat membuat semuanya terhubung ke internet tetapi sistem IoT dapat menjadi sasaran yang sangat mudah untuk disusupi penyerang dengan menggunakan malware, lebih dari 1,6 miliar atau tepatnya 1.637.973.022 anomali traffic atau serangan siber (cyberattack) yang terjadi diseluruh wilayah Indonesia sepanjang tahun 2021, teknik machine learning dapat dimanfaatkan untuk proses pengklasifikasian anomali traffic dengan menggunakan algoritma k-nearest neighbour (KNN) sehingga dapat membedakan data traffic yang bersifat benign atau malicious. Data anomali traffic yang

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Zhou, Mingwei, Xian Mu, and Yanyan Liang. "SOE: A Multi-Objective Traffic Scheduling Engine for DDoS Mitigation with Isolation-Aware Optimization." Mathematics 13, no. 11 (2025): 1853. https://doi.org/10.3390/math13111853.

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Distributed Denial-of-Service (DDoS) attacks generate deceptive, high-volume traffic that bypasses conventional detection mechanisms. When interception fails, effectively allocating mixed benign and malicious traffic under resource constraints becomes a critical challenge. To address this, we propose SchedOpt Engine (SOE), a scheduling framework formulated as a discrete multi-objective optimization problem. The goal is to optimize four conflicting objectives: a benign traffic acceptance rate (BTAR), malicious traffic interception rate (MTIR), server load balancing, and malicious traffic isolat

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Chen, Tieming, Yunpeng Chen, Mingqi Lv, et al. "A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning." Applied Sciences 11, no. 16 (2021): 7188. http://dx.doi.org/10.3390/app11167188.

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Malicious HTTP traffic detection plays an important role in web application security. Most existing work applies machine learning and deep learning techniques to build the malicious HTTP traffic detection model. However, they still suffer from the problems of huge training data collection cost and low cross-dataset generalization ability. Aiming at these problems, this paper proposes DeepPTSD, a deep learning method for payload based malicious HTTP traffic detection. First, it treats the malicious HTTP traffic detection as a text classification problem and trains the initial detection model us

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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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Hwang, Ren-Hung, Min-Chun Peng, Van-Linh Nguyen, and Yu-Lun Chang. "An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level." Applied Sciences 9, no. 16 (2019): 3414. http://dx.doi.org/10.3390/app9163414.

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Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packe

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Hu, Ying, Ben Liu, Jianyong Li, and Linlin Jia. "Decentralized Federated Learning with Node Incentive and Role Switching Mechanism for Network Traffic Prediction in NFV Environment." Symmetry 17, no. 6 (2025): 970. https://doi.org/10.3390/sym17060970.

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In network function virtualization (NFV) environments, dynamic network traffic prediction with unique symmetric and asymmetric traffic patterns is critical for efficient resource orchestration and service chain optimization. Traditional centralized prediction models face risks of cross-provider data privacy leakage when network service providers collaborate with resource providers to deliver services. To address this issue, we propose a decentralized federated learning method for network traffic prediction, which ensures that historical network traffic data remain stored locally without requir

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Wu, Zhaoli, and Junwei Liu. "Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm." Scalable Computing: Practice and Experience 25, no. 2 (2024): 928–37. http://dx.doi.org/10.12694/scpe.v25i2.2610.

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With the rapid growth of network traffic, in order to monitor network traffic, the author proposes a baseline based traffic inspection method. The main objective is to develop a global system for identifying malicious traffic, rather than a precise method for detecting the types of worms produced by malicious traffic. Although traffic is caused by the causes, network administrators can use this international search technique to detect malicious traffic data. The system based approach mainly includes designing time based on the traditional traffic model, detecting various equipments and network

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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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Wang, Ruonan, Jinlong Fei, Min Zhao, et al. "DA-Transfer: A Transfer Method for Malicious Network Traffic Classification with Small Sample Problem." Electronics 11, no. 21 (2022): 3577. http://dx.doi.org/10.3390/electronics11213577.

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Deep learning is successful in providing adequate classification results in the field of traffic classification due to its ability to characterize features. However, malicious traffic captures insufficient data and identity tags, which makes it difficult to reach the data volume required to drive deep learning. The problem of classifying small-sample malicious traffic has gradually become a research hotspot. This paper proposes a small-sample malicious traffic classification method based on deep transfer learning. The proposed DA-Transfer method significantly improves the accuracy and efficien

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Jagadeeswari, G., and V. Sarala Devi. "Filtering of Malicious Traffic with Secret Sharing." International Journal of Communication and Networking System 003, no. 001 (2014): 6–11. http://dx.doi.org/10.20894/ijcnes.103.003.001.002.

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Yang, Hao, Qin He, Zhenyan Liu, and Qian Zhang. "Malicious Encryption Traffic Detection Based on NLP." Security and Communication Networks 2021 (August 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/9960822.

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The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encr

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Zhang, Xueqin, Min Zhao, Jiyuan Wang, Shuang Li, Yue Zhou, and Shinan Zhu. "Deep-Forest-Based Encrypted Malicious Traffic Detection." Electronics 11, no. 7 (2022): 977. http://dx.doi.org/10.3390/electronics11070977.

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The SSL/TLS protocol is widely used in data encryption transmission. Aiming at the problem of detecting SSL/TLS-encrypted malicious traffic with small-scale and unbalanced training data, a deep-forest-based detection method called DF-IDS is proposed in this paper. According to the characteristics of SSL/TSL protocol, the network traffic was split into sessions according to the 5-tuple information. Each session was then transformed into a two-dimensional traffic image as the input of a deep-learning classifier. In order to avoid information loss and improve the detection efficiency, the multi-g

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Soldo, Fabio, Katerina Argyraki, and Athina Markopoulou. "Optimal Source-Based Filtering of Malicious Traffic." IEEE/ACM Transactions on Networking 20, no. 2 (2012): 381–95. http://dx.doi.org/10.1109/tnet.2011.2161615.

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Du, Xiaodong, Ming-Zhong Wang, Xiaoping Zhang, and Liehuang Zhu. "Traffic-based Malicious Switch Detection in SDN." International Journal of Security and Its Applications 8, no. 5 (2014): 119–30. http://dx.doi.org/10.14257/ijsia.2014.8.5.12.

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Zhang, Dahua, Lei Mei, Baiji Hu, Shuang Yao, and Yayun Zhu. "Enseble learning-based technology malicious traffic detection." IET Conference Proceedings 2024, no. 21 (2025): 147–51. https://doi.org/10.1049/icp.2024.4216.

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Goseva-Popstojanova, Katerina, Goce Anastasovski, Ana Dimitrijevikj, Risto Pantev, and Brandon Miller. "Characterization and classification of malicious Web traffic." Computers & Security 42 (May 2014): 92–115. http://dx.doi.org/10.1016/j.cose.2014.01.006.

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Manggalanny, Muhammad Salahuddien, and Kalamullah Ramli. "ENHANCED DESIGN FOR DNS MALICIOUS TRAFFIC ANALYSIS." Far East Journal of Electronics and Communications 17, no. 5 (2017): 1221–28. http://dx.doi.org/10.17654/ec017051221.

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Li, Qiankun, Juan Li, Yao Li, Feng Jiu, and Yunxia Chu. "An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System." International Journal of Information Technologies and Systems Approach 17, no. 1 (2024): 1–17. http://dx.doi.org/10.4018/ijitsa.343317.

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A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model,

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Liu, Junhao, Guolin Shao, Hong Rao, Xiangjun Li, and Xuan Huang. "AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic." Applied Sciences 14, no. 22 (2024): 10366. http://dx.doi.org/10.3390/app142210366.

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While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental

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Zhang, Hao, Ye Liang, Yuanzhuo Li, et al. "Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning." Sensors 25, no. 8 (2025): 2614. https://doi.org/10.3390/s25082614.

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With the rapid development of the internet, the increasing amount of malicious traffic poses a significant challenge to the network security of critical infrastructures, including power monitoring systems. As the core part of the power grid operation, the network security of power monitoring systems directly affects the stability of the power system and the safety of electricity supply. Nowadays, network attacks are complex and diverse, and traditional rule-based detection methods are no longer adequate. With the advancement of machine learning technologies, researchers have introduced them in

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Dremov, Artem. "METHODS AND MEANS TO IMPROVE THE EFFICIENCY OF NETWORK TRAFFIC SECURITY MONITORING BASED ON ARTIFICIAL INTELLIGENCE." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (10) (December 19, 2023): 73–78. http://dx.doi.org/10.20998/2079-0023.2023.02.11.

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This paper aims to provide a solution for malicious network traffic detection and categorization. Remote attacks on computer systems are becoming more common and more dangerous nowadays. This is due to several factors, some of which are as follows: first of all, the usage of computer networks and network infrastructure overall is on the rise, with tools such as messengers, email, and so on. Second, alongside increased usage, the amount of sensitive information being transmitted over networks has also grown. Third, the usage of computer networks for complex systems, such as grid and cloud compu

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Mahmood, K. Mohammed Zaid A. Abod Alharith A. Abdullah. "Secure SDN Traffic based on Machine Learning Classifier." LC International Journal of STEM (ISSN: 2708-7123) 3, no. 1 (2022): 118–28. https://doi.org/10.5281/zenodo.6786157.

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Nowadays, the majority of human activities are carried out utilizing a variety of services or applications that rely on the local and Internet connectivity services provided by private or public networks. With the developments in Machine Learning and Software Defined Networking, traffic classification has become an essential study subject.  As a consequence of the segregation of control and data planes, Software Defined Networks have some security flaws. To cope with malicious code in SDN, certain operational security techniques have been devised. In this paper, a machine learning model,

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