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Articoli di riviste sul tema "Analysis of encrypted network flow"

Autore: Grafiati

Pubblicato: 16 novembre 2024

Ultima modifica: 31 luglio 2025

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1

Yan, Xiaodan. "Deep Learning-Based Efficient Analysis for Encrypted Traffic." Applied Sciences 13, no. 21 (2023): 11776. http://dx.doi.org/10.3390/app132111776.

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Abstract (sommario):

To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and

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2

Jiang, Ziyu. "Bidirectional Flow-Based Image Representation Method for Detecting Network Traffic Service Categories." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 89–95. http://dx.doi.org/10.54097/mwyge502.

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Abstract (sommario):

Network traffic identification is crucial for network resource management and improving service quality. Traditional methods, such as port-based and deep packet inspection approaches, face challenges due to the increasing complexity of network environments, privacy concerns, and the emergence of encrypted traffic. This paper aims to address the issues of low accuracy and slow operation speed in encrypted traffic classification while ensuring the protection of user privacy. We propose a data processing method that transforms network traffic into images representing bidirectional flow packet arr

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3

Ma, Chencheng, Xuehui Du, and Lifeng Cao. "Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow." Electronics 9, no. 2 (2020): 324. http://dx.doi.org/10.3390/electronics9020324.

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Abstract (sommario):

The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the i

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4

Meghdouri, Fares, Tanja Zseby, and Félix Iglesias. "Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic." Applied Sciences 8, no. 11 (2018): 2196. http://dx.doi.org/10.3390/app8112196.

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Abstract (sommario):

The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific lite

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5

Afzal, Asmara, Mehdi Hussain, Shahzad Saleem, M. Khuram Shahzad, Anthony T. S. Ho, and Ki-Hyun Jung. "Encrypted Network Traffic Analysis of Secure Instant Messaging Application: A Case Study of Signal Messenger App." Applied Sciences 11, no. 17 (2021): 7789. http://dx.doi.org/10.3390/app11177789.

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Abstract (sommario):

Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential art

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6

Ravi, V., and A. S. Poornima. "SecMa: A Novel Multimodal Autoencoder Framework for Encrypted IoT Traffic Analysis and Attack Detection." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23020–26. https://doi.org/10.48084/etasr.10336.

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The exponential growth of encrypted Internet of Things (IoT) traffic has introduced significant cybersecurity challenges, including the complexity of analyzing encrypted payload data, managing heterogeneous device behavior, and addressing resource constraints. Traditional methods achieve low detection rates (45-60%) and struggle to balance accuracy, efficiency, and privacy. This paper proposes SecMa, a novel multimodal autoencoder framework designed to address these limitations in encrypted IoT traffic analysis and attack detection. SecMa processes three complementary feature modalities—networ

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7

Oh, Chaeyeon, Joonseo Ha, and Heejun Roh. "A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers." Applied Sciences 12, no. 1 (2021): 155. http://dx.doi.org/10.3390/app12010155.

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Abstract (sommario):

Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detect

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8

Haywood, Gregor Tamati, and Saleem Noel Bhatti. "Defence against Side-Channel Attacks for Encrypted Network Communication Using Multiple Paths." Cryptography 8, no. 2 (2024): 22. http://dx.doi.org/10.3390/cryptography8020022.

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Abstract (sommario):

As more network communication is encrypted to provide data privacy for users, attackers are focusing their attention on traffic analysis methods for side-channel attacks on user privacy. These attacks exploit patterns in particular features of communication flows such as interpacket timings and packet sizes. Unsupervised machine learning approaches, such as Hidden Markov Models (HMMs), can be trained on unlabelled data to estimate these flow attributes from an exposed packet flow, even one that is encrypted, so it is highly feasible for an eavesdropper to perform this attack. Traditional defen

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9

Hu, Xinyi, Chunxiang Gu, Yihang Chen, and Fushan Wei. "CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method." Sensors 21, no. 24 (2021): 8231. http://dx.doi.org/10.3390/s21248231.

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Abstract (sommario):

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments

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10

Vizitiu, Anamaria, Cosmin-Ioan Nita, Radu Miron Toev, Tudor Suditu, Constantin Suciu, and Lucian Mihai Itu. "Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection." Applied Sciences 11, no. 19 (2021): 9049. http://dx.doi.org/10.3390/app11199049.

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Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML—a library for fast implementation and deploy

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11

Choudhary, Swapna, and Sanjay Dorle. "Secured SDN Based Blockchain: An Architecture to Improve the Security of VANET." International journal of electrical and computer engineering systems 13, no. 2 (2022): 145–53. http://dx.doi.org/10.32985/ijeces.13.2.7.

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Abstract (sommario):

Vehicular Ad-hoc networks (VANETs) during the communication process, nodes are always varying and the process is always under security threats like Sybil attacks, masquerading attacks, etc. In order to reduce the probability of these attacks and to regulate traffic flow in the network, a software-defined network (SDN) is used. The SDN is used for implementing protocols like OpenFlow and reducing the routing load in the network, but it doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockch

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12

Demertzis, Konstantinos, Panayiotis Kikiras, Nikos Tziritas, Salvador Sanchez, and Lazaros Iliadis. "The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence." Big Data and Cognitive Computing 2, no. 4 (2018): 35. http://dx.doi.org/10.3390/bdcc2040035.

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Abstract (sommario):

A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network’s services, but also for attacks identi

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13

Pettorru, Giovanni, Matteo Flumini, and Marco Martalò. "Balancing Complexity and Performance in Convolutional Neural Network Models for QUIC Traffic Classification." Sensors 25, no. 15 (2025): 4576. https://doi.org/10.3390/s25154576.

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Abstract (sommario):

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detectio

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14

E.B., Adigun, Ismaila W.O., Baale A.A., and Ismaila F.M. "Optimized DenseNet Architecture for Efficient Classification of Encrypted Internet Traffic." Asian Journal of Research in Computer Science 18, no. 2 (2025): 197–205. https://doi.org/10.9734/ajrcos/2025/v18i2571.

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Abstract (sommario):

The increasing reliance on Internet-based services has rendered secure and efficient network traffic classification critical. Conventional methods for categorising traffic, such as port and payload methods, often struggle with the challenges posed by encrypted traffic. Deep learning techniques have emerged as a predominant method for traffic classification given their success in domains such as image recognition, document analysis, and genomics. This research proposes an enhanced DenseNet architecture that leverages deep learning to accurately classify encrypted Internet traffic categories. Th

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15

Lienkov, S. V., V. M. Dzhuliy, and I. V. Muliar. "METHOD OF CLASSIFICATION OF PSEUDO-RANDOM SEQUENCES OF COMPRESSED AND ENCRYPTED DATA TO PREVENT INFORMATION LEAKAGE." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 82 (2024): 77–93. http://dx.doi.org/10.17721/2519-481x/2024/82-09.

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Abstract (sommario):

The considered task of developing a method for classifying pseudo-random sequences of protection against the leakage of confidential information based on the division of compressed and encrypted data can be used to detect network attacks on data transmission networks, in means of prevention and detection of information leakage, as well as in software products that implement services of electronic mail. It is shown that data security threats are characterized by a set of qualitative and quantitative vector indicators, and their formalization requires the application of fuzzy set theory and disc

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16

He, Gaofeng, Bingfeng Xu, and Haiting Zhu. "AppFA: A Novel Approach to Detect Malicious Android Applications on the Network." Security and Communication Networks 2018 (April 17, 2018): 1–15. http://dx.doi.org/10.1155/2018/2854728.

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Abstract (sommario):

We propose AppFA, an Application Flow Analysis approach, to detect malicious Android applications (simply apps) on the network. Unlike most of the existing work, AppFA does not need to install programs on mobile devices or modify mobile operating systems to extract detection features. Besides, it is able to handle encrypted network traffic. Specifically, we propose a constrained clustering algorithm to classify apps network traffic, and use Kernel Principal Component Analysis to build their network behavior profiles. After that, peer group analysis is explored to detect malicious apps by compa

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17

Zhang, Haozhen, Haodong Yue, Xi Xiao, et al. "Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 1048–56. https://doi.org/10.1609/aaai.v39i1.32091.

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With the growing significance of network security, the classification of encrypted traffic has emerged as an urgent challenge. Traditional byte-based traffic analysis methods are constrained by the rigid granularity of information and fail to fully exploit the diverse correlations between bytes. To address these limitations, this paper introduces MH-Net, a novel approach for classifying network traffic that leverages multi-view heterogeneous traffic graphs to model the intricate relationships between traffic bytes. The essence of MH-Net lies in aggregating varying numbers of traffic bits into

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18

Ren, Guoqiang, Guang Cheng, and Nan Fu. "Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network." Applied Sciences 13, no. 3 (2023): 1483. http://dx.doi.org/10.3390/app13031483.

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Abstract (sommario):

Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic security analysis. The existing attacks, often using encryption technology and distributed attack methods, increase the number and complexity of attacks. However, the traditional methods need more analysis of encrypted malicious traffic interaction patterns and cannot explore the potential correlations of interaction patterns in a macroscopic and com

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19

Subach, Ihor, Dmytro Sharadkin, and Ihor Yakoviv. "APPLICATION OF METRIC METHODS OF HISTOGRAM COMPARISON FOR DETECTING CHANGES IN ENCRYPTED NETWORK TRAFFIC." Cybersecurity: Education, Science, Technique 1, no. 25 (2024): 434–48. http://dx.doi.org/10.28925/2663-4023.2024.25.434448.

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Abstract (sommario):

With the increase in the share of encrypted traffic transmitted over the Internet, it has become impossible to directly identify the causes of anomalies in network behavior due to the lack of access to the contents of encrypted packets. This has significantly complicated the task of identifying information security threats. Only external symptoms are available for analysis, which manifest as changes in certain basic traffic parameters, such as volume, intensity, delays between packets, etc. As a result, the role and importance of algorithms for detecting changes in traffic have increased. Thes

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20

Lapshichyov, Vitaly, and Oleg Makarevich. "Identification of the "Tor" Network https-Connection Version tls v1.3." Voprosy kiberbezopasnosti, no. 6(40) (2020): 57–62. http://dx.doi.org/10.21681/2311-3456-2020-06-57-62.

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Abstract (sommario):

Purpose of the study: compilation of a set of features that allow to detect and identify the establishment of a connection between the client and the anonymous network Tor in conditions of using encryption of the data stream using the TLS v1.3 protocol. Method: software analysis of the data flow, frequency methods, decomposition of the content of data packets according to their number, sequence, finding frames in a packet and sizes, a comparative method in point of different versions of the encryption protocol and resources making the connection were used. Results: a set of features of the Tor

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21

Chaddad, Louma, Ali Chehab, Imad H. Elhajj, and Ayman Kayssi. "Optimal Packet Camouflage Against Traffic Analysis." ACM Transactions on Privacy and Security 24, no. 3 (2021): 1–23. http://dx.doi.org/10.1145/3442697.

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Abstract (sommario):

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app th

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22

Selvaraj, Prabha, Vijay Kumar Burugari, S. Gopikrishnan, Abdullah Alourani , Gautam Srivastava, and Mohamed Baza. "An Enhanced and Secure Trust-Aware Improved GSO for Encrypted Data Sharing in the Internet of Things." Applied Sciences 13, no. 2 (2023): 831. http://dx.doi.org/10.3390/app13020831.

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Wireless sensors and actuator networks (WSNs) are the physical layer implementation used for many smart applications in this decade in the form of the Internet of Things (IoT) and cyber-physical systems (CPS). Even though many research concerns in WSNs have been answered, the evolution of the WSN into an IoT network has exposed it to many new technical issues, including data security, multi-sensory multi-communication capabilities, energy utilization, and the age of information. Cluster-based data collecting in the Internet of Things has the potential to address concerns with data freshness an

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23

Singh, Purushottam, Sandip Dutta, and Prashant Pranav. "Optimizing GANs for Cryptography: The Role and Impact of Activation Functions in Neural Layers Assessing the Cryptographic Strength." Applied Sciences 14, no. 6 (2024): 2379. http://dx.doi.org/10.3390/app14062379.

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Generative Adversarial Networks (GANs) have surfaced as a transformative approach in the domain of cryptography, introducing a novel paradigm where two neural networks, the generator (akin to Alice) and the discriminator (akin to Bob), are pitted against each other in a cryptographic setting. A third network, representing Eve, attempts to decipher the encrypted information. The efficacy of this encryption–decryption process is deeply intertwined with the choice of activation functions employed within these networks. This study conducted a comparative analysis of four widely used activation fun

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24

Sudhanshu, Sekhar Tripathy, and Behera Bichitrananda. "EVALUATION OF FUTURE PERSPECTIVES ON SNORT AND WIRESHARK AS TOOLS AND TECHNIQUES FOR INTRUSION DETECTION SYSTEM." Industrial Engineering Journal 53, no. 10 (2024): 18–40. https://doi.org/10.5281/zenodo.14213834.

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The increasing reliance on inter-organizational information exchange has raised significant concerns about the security of data and network infrastructures. Network monitoring plays a crucial role in mitigating these concerns, with tools like Wireshark and Snort forming the backbone of Intrusion Detection Systems (IDS). Initially developed as a packet inspection application, Wireshark is widely regarded for its user-friendly interface and intuitive packet-enhancement features, making it effective for classifying various types of network traffic. This research explores the practical application

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25

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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Abstract (sommario):

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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26

Salim, Mikail Mohammed, Inyeung Kim, Umarov Doniyor, Changhoon Lee, and Jong Hyuk Park. "Homomorphic Encryption Based Privacy-Preservation for IoMT." Applied Sciences 11, no. 18 (2021): 8757. http://dx.doi.org/10.3390/app11188757.

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Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edg

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27

Li, Mengyao, Xianwen Fang, and Asimeng Ernest. "A Color Image Encryption Method Based on Dynamic Selection Chaotic System and Singular Value Decomposition." Mathematics 11, no. 15 (2023): 3274. http://dx.doi.org/10.3390/math11153274.

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As the basis for guiding business process decisions, flowcharts contain sensitive information pertaining to process-related concepts. Therefore, it is necessary to encrypt them to protect the privacy or security of stakeholders. Using the principles of image singular value decomposition, chaotic system randomness, and neural network camouflage, a business flow chart encryption method based on dynamic selection chaotic system and singular value decomposition is proposed. Specifically, a dynamic selected chaotic system is constructed based on the nonlinear combination of one-dimensional chaotic

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28

Gao, Shu-Yang, Xiao-Hong Li, and Mao-De Ma. "A Malicious Behavior Awareness and Defense Countermeasure Based on LoRaWAN Protocol." Sensors 19, no. 23 (2019): 5122. http://dx.doi.org/10.3390/s19235122.

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Low power wide area network (LoRaWAN) protocol has been widely used in various fields. With its rapid development, security issues about the awareness and defense against malicious events in the Internet of Things must be taken seriously. Eavesdroppers can exploit the shortcomings of the specification and the limited consumption performance of devices to carry out security attacks such as replay attacks. In the process of the over-the-air-activation (OTAA) for LoRa nodes, attackers can modify the data because the data is transmitted in plain text. If the user’s root key is leaked, the wireless

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29

Chen, Xu-Yang, Lu Han, De-Chuan Zhan, and Han-Jia Ye. "MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15922–29. https://doi.org/10.1609/aaai.v39i15.33748.

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Network traffic includes data transmitted across a network, such as web browsing and file transfers, and is organized into packets (small units of data) and flows (sequences of packets exchanged between two endpoints). Classifying encrypted traffic is essential for detecting security threats and optimizing network management. Recent advancements have highlighted the superiority of foundation models in this task, particularly for their ability to leverage large amounts of unlabeled data and demonstrate strong generalization to unseen data. However, existing methods that focus on token-level rel

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Sattar, Kanza Abdul, Takreem Haider, Umar Hayat, and Miguel D. Bustamante. "An Efficient and Secure Cryptographic Algorithm Using Elliptic Curves and Max-Plus Algebra-Based Wavelet Transform." Applied Sciences 13, no. 14 (2023): 8385. http://dx.doi.org/10.3390/app13148385.

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With the advent of communication networks, protecting data from security threats has become increasingly important. To address this issue, we present a new text encryption scheme that uses a combination of elliptic curve cryptography and max-plus algebra-based wavelet transform to provide enhanced security and efficiency. The proposed encryption process consists of three main phases. In the first phase, the plaintext is encoded using ASCII characters, followed by the introduction of diffusion in its representation. In the second phase, points are computed on an elliptic curve, and a mapping me

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Pachilakis, Michalis, Panagiotis Papadopoulos, Nikolaos Laoutaris, Evangelos P. Markatos, and Nicolas Kourtellis. "YourAdvalue." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 41–42. http://dx.doi.org/10.1145/3547353.3522629.

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The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktopRTB prices in the wild, and compare them with desktop and mobileRTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how

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Pachilakis, Michalis, Panagiotis Papadopoulos, Nikolaos Laoutaris, Evangelos P. Markatos, and Nicolas Kourtellis. "YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 3 (2021): 1–26. http://dx.doi.org/10.1145/3491044.

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Abstract (sommario):

The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and ho

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Wang, Guanyu, and Yijun Gu. "Multi-Task Scenario Encrypted Traffic Classification and Parameter Analysis." Sensors 24, no. 10 (2024): 3078. http://dx.doi.org/10.3390/s24103078.

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Abstract (sommario):

The widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of deep learning technology in encrypted traffic classification significantly improves the accuracy of models. This study focuses primarily on encrypted traffic classification in the fields of network analysis and network security. To address the shortcomings of existing deep learning-based encrypted traffic classification methods in terms of com

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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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Abstract (sommario):

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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Alwhbi, Ibrahim A., Cliff C. Zou, and Reem N. Alharbi. "Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning." Sensors 24, no. 11 (2024): 3509. http://dx.doi.org/10.3390/s24113509.

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Abstract (sommario):

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffi

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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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Abstract (sommario):

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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Cao, Jie, Xing-Liang Yuan, Ying Cui, Jia-Cheng Fan, and Chin-Ling Chen. "A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning." Applied Sciences 12, no. 13 (2022): 6434. http://dx.doi.org/10.3390/app12136434.

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Abstract (sommario):

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To

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Sharma M, Prof Sahana. "Encrypted Flow Intelligence: A Literature Review of AI Models for Traffic-Based Threat Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47998.

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Abstract (sommario):

Abstract The growth of encrypted online communication has strengthened user privacy but also introduced major obstacles for threat detection systems. Since traditionally intrusion detection relies heavily on inspecting readable data, these systems often fail when faced with encrypted traffic. To address this issue, recent studies have turned toward artificial intelligence, particularly approaches using machine and deep learning, which can infer suspicious behavior without decrypting data. This review consolidates findings from recent literature, evaluating model architectures, training techniq

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Farooq, Irfan, Syed Aale Ahmed, Asfar Ali, Muhammad Ali Warraich, Muhammad Aqeel, and Hamayun Khan. "Enhanced Classification of Networks Encrypted Traffic: A Conceptual Analysis of Security Assessments, Implementation, Trends and Future Directions." Asian Bulletin of Big Data Management 4, no. 4 (2024): 500–522. https://doi.org/10.62019/abbdm.v4i4.287.

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Abstract (sommario):

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. With the widespread use of encrypted data transport, network traffic encryption is becoming a standard nowadays. This presents a challenge for traffic measurement, especially for analysis and anomaly detection methods, which are dependent on the type of network traffic. In this paper, we survey existing approaches for classification and analysis of encrypted trafficIn response to the prolifera

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Sivaranjani, Dr R. "Extensible Machine Learning for Encrypted Network Traffic." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2214–21. https://doi.org/10.22214/ijraset.2025.67793.

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Abstract (sommario):

: In the age of increasing cybersecurity threats, the need for effectively classifying encrypted network traffic has become paramount. This project explores an innovative approach that combines extensible machine learning techniques with uncertainty quantification to enhance the classification of encrypted network data. Traditional methods often struggle to accurately classify encrypted traffic due to its opaque nature, leading to challenges in identifying malicious activities. This research proposes a framework that integrates machine learning algorithms capable of adapting to evolving traffi

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Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (2022): 67. http://dx.doi.org/10.3390/fi14020067.

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Abstract (sommario):

In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previ

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Jeng, Tzung-Han, Wen-Yang Luo, Chuan-Chiang Huang, Chien-Chih Chen, Kuang-Hung Chang, and Yi-Ming Chen. "Cloud Computing for Malicious Encrypted Traffic Analysis and Collaboration." International Journal of Grid and High Performance Computing 13, no. 3 (2021): 12–29. http://dx.doi.org/10.4018/ijghpc.2021070102.

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Abstract (sommario):

As the application of network encryption technology expands, malicious attacks will also be protected by encryption mechanism, increasing the difficulty of detection. This paper focuses on the analysis of encrypted traffic in the network by hosting long-day encrypted traffic, coupled with a weighted algorithm commonly used in information retrieval and SSL/TLS fingerprint to detect malicious encrypted links. The experimental results show that the system proposed in this paper can identify potential malicious SSL/TLS fingerprints and malicious IP which cannot be recognized by other external thre

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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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Abstract (sommario):

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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Qiu, Xiaozong, Guohua Yan, and Lihua Yin. "CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv CNN and BiLSTM) and Mean Teacher Collaborative Learning." Applied Sciences 15, no. 9 (2025): 5089. https://doi.org/10.3390/app15095089.

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Abstract (sommario):

The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficient allocation of network resources and enhancing user experience. However, the widespread use of traffic encryption technology, while improving data transmission security, also obscures the content of traffic, making it challenging to accurately classify and identify encrypted traffic. This limitati

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Taylor, Vincent F., Riccardo Spolaor, Mauro Conti, and Ivan Martinovic. "Robust Smartphone App Identification via Encrypted Network Traffic Analysis." IEEE Transactions on Information Forensics and Security 13, no. 1 (2018): 63–78. http://dx.doi.org/10.1109/tifs.2017.2737970.

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46

Karaçay, Leyli, Erkay Savaş, and Halit Alptekin. "Intrusion Detection Over Encrypted Network Data." Computer Journal 63, no. 4 (2019): 604–19. http://dx.doi.org/10.1093/comjnl/bxz111.

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Abstract (sommario):

Abstract Effective protection against cyber-attacks requires constant monitoring and analysis of system data in an IT infrastructure, such as log files and network packets, which may contain private and sensitive information. Security operation centers (SOC), which are established to detect, analyze and respond to cyber-security incidents, often utilize detection models either for known types of attacks or for anomaly and applies them to the system data for detection. SOC are also motivated to keep their models private to capitalize on the models that are their propriety expertise, and to prot

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Fischer, Andreas, Benny Fuhry, Jörn Kußmaul, Jonas Janneck, Florian Kerschbaum, and Eric Bodden. "Computation on Encrypted Data Using Dataflow Authentication." ACM Transactions on Privacy and Security 25, no. 3 (2022): 1–36. http://dx.doi.org/10.1145/3513005.

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Abstract (sommario):

Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks.

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Yang, Xiaoqing, Niwat Angkawisittpan, and Xinyue Feng. "Analysis of an enhanced random forest algorithm for identifying encrypted network traffic." EUREKA: Physics and Engineering, no. 5 (September 10, 2024): 201–12. http://dx.doi.org/10.21303/2461-4262.2024.003372.

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Abstract (sommario):

The focus of this paper is to apply an improved machine learning algorithm to realize the efficient and reliable identification and classification of network communication encrypted traffic, and to solve the challenges faced by traditional algorithms in analyzing encrypted traffic after adding encryption protocols. In this study, an enhanced random forest (ERF) algorithm is introduced to optimize the accuracy and efficiency of the identification and classification of encrypted network traffic. Compared with traditional methods, it aims to improve the identification ability of encrypted traffic

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Xu, Guoliang, Ming Xu, Yunzhi Chen, and Jiaqi Zhao. "A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic." Electronics 12, no. 10 (2023): 2313. http://dx.doi.org/10.3390/electronics12102313.

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Abstract (sommario):

Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifiers are created. However, mobile applications often generate concurrent flows with varying degrees of ties, such as low discriminative flows across applications and application-specific flows. So flow-based methods suffer from low accuracy. In this paper, a novel mobile application-classifying method is proposed, capturing relationships between flow

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50

Dai, Xianlong, Guang Cheng, Ziyang Yu, Ruixing Zhu, and Yali Yuan. "MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-k Elephant Flows." Applied Sciences 13, no. 1 (2022): 575. http://dx.doi.org/10.3390/app13010575.

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Abstract (sommario):

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms f

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