Developing generalizable ML-based solutions for disparate learning problems in network security is highly desired. However, despite a rich history of applying ML to network security, most existing solutions lack generalizability. This lack of progress can be attributed to an overreliance on supervised learning techniques and the associated challenges of curating well-specified labeled training data. This paper addresses a fundamental gap by introducing a novel transformer-based network foundation model, netFound. We employ self-supervised learning techniques on abundant, unlabeled network telemetry data for pre-training. This pretrained model can subsequently be fine-tuned to create generalizable learning artifacts for disparate learning tasks, even when using commonly available but challenging labeled datasets that are sparse, noisy, and skewed. To realize this goal, netFound leverages various domain-specific attributes and constraints unique to network data (packet traces) by developing multi-modal embeddings, protocol-aware tokenization, data-driven token composition, and hierarchical transformers. Our results demonstrate that netFound's domain-specific design choices ensure that it (1) effectively captures the hidden networking context in production settings, (2) outperforms four different SOTA methods on five different learning tasks, and (3) is robust to both noisy labels and learning shortcuts -- critical for developing generalizable ML models in practical settings.
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC). In this work, we present a preliminary study of 14 hand-crafted DAs applied on the MIRAGE19 dataset. Our results (i) show that DA can reap benefits previously unexplored in TC and (ii) foster a research agenda on the use of generative models to automate DA design.
As the complexity and scale of modern computer networks continue to increase, there has emerged an urgent need for precise traffic analysis, which plays a pivotal role in cutting-edge wireless connectivity technologies. This study focuses on leveraging Machine Learning methodologies to create an advanced network traffic classification system. We introduce a novel data-driven approach that excels in identifying various network service types in real-time, by analyzing patterns within the network traffic. Our method organizes similar kinds of network traffic into distinct categories, referred to as network services, based on latency requirement. Furthermore, it decomposes the network traffic stream into multiple, smaller traffic flows, with each flow uniquely carrying a specific service. Our ML models are trained on a dataset comprised of labeled examples representing different network service types collected on various Wi-Fi network conditions. Upon evaluation, our system demonstrates a remarkable accuracy in distinguishing the network services. These results emphasize the substantial promise of integrating Artificial Intelligence in wireless technologies. Such an approach encourages more efficient energy consumption, enhances Quality of Service assurance, and optimizes the allocation of network resources, thus laying a solid groundwork for the development of advanced intelligent networks.
Federated learning enables multiple clients to collaboratively contribute to the learning of a global model orchestrated by a central server. This learning scheme promotes clients' data privacy and requires reduced communication overheads. In an application like network traffic classification, this helps hide the network vulnerabilities and weakness points. However, federated learning is susceptible to backdoor attacks, in which adversaries inject manipulated model updates into the global model. These updates inject a salient functionality in the global model that can be launched with specific input patterns. Nonetheless, the vulnerability of network traffic classification models based on federated learning to these attacks remains unexplored. In this paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack against federated learning for network traffic classification. GABAttack utilizes a genetic algorithm to optimize the values and locations of backdoor trigger patterns, ensuring a better fit with the input and the model. This input-tailored dynamic attack is promising for improved attack evasiveness while being effective. Extensive experiments conducted over real-world network datasets validate the success of the proposed GABAttack in various situations while maintaining almost invisible activity. This research serves as an alarming call for network security experts and practitioners to develop robust defense measures against such attacks.
Network data analytics are now at the core of almost every networking solution. Nonetheless, limited access to networking data has been an enduring challenge due to many reasons including complexity of modern networks, commercial sensitivity, privacy and regulatory constraints. In this work, we explore how to leverage recent advancements in Diffusion Models (DM) to generate synthetic network traffic data. We develop an end-to-end framework - NetDiffus that first converts one-dimensional time-series network traffic into two-dimensional images, and then synthesizes representative images for the original data. We demonstrate that NetDiffus outperforms the state-of-the-art traffic generation methods based on Generative Adversarial Networks (GANs) by providing 66.4% increase in fidelity of the generated data and 18.1% increase in downstream machine learning tasks. We evaluate NetDiffus on seven diverse traffic traces and show that utilizing synthetic data significantly improves traffic fingerprinting, anomaly detection and traffic classification.
Network traffic monitoring based on IP Flows is a standard monitoring approach that can be deployed to various network infrastructures, even the large IPS-based networks connecting millions of people. Since flow records traditionally contain only limited information (addresses, transport ports, and amount of exchanged data), they are also commonly extended for additional features that enable network traffic analysis with high accuracy. Nevertheless, the flow extensions are often too large or hard to compute, which limits their deployment only to smaller-sized networks. This paper proposes a novel extended IP flow called NetTiSA (Network Time Series Analysed), which is based on the analysis of the time series of packet sizes. By thoroughly testing 25 different network classification tasks, we show the broad applicability and high usability of NetTiSA, which often outperforms the best-performing related works. For practical deployment, we also consider the sizes of flows extended for NetTiSA and evaluate the performance impacts of its computation in the flow exporter. The novel feature set proved universal and deployable to high-speed ISP networks with 100\,Gbps lines; thus, it enables accurate and widespread network security protection.
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS19, ISCX-VPN and ISCX-Tor datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called "flowpic" (i.e., a per-flow 2d histograms of the packets size evolution over time). In this paper (i) we reproduce [16] on the same datasets and (ii) we replicate its most salient aspect (the importance of data augmentation) on three additional public datasets (MIRAGE19, MIRAGE22 and UTMOBILENET21). While we confirm most of the original results, we also found a 20% accuracy drop on some of the investigated scenarios due to a data shift in the original dataset that we uncovered. Additionally, our study validates that the data augmentation strategies studied in [16] perform well on other datasets too. In the spirit of reproducibility and replicability we make all artifacts (code and data) available to the research community at https://tcbenchstack.github.io/tcbench/
Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.
As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.
Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier's performance inevitably decays over time. Re-training the model on newer datasets has been shown to only partially improve its performance. Manually re-tuning the model architecture to meet the performance expectations on newer datasets is time-consuming and requires domain expertise. We propose AutoML4ETC, a novel tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification. We define a novel, powerful search space tailored specifically for the early classification of encrypted traffic using packet header bytes. We show that with different search strategies over our search space, AutoML4ETC generates neural architectures that outperform the state-of-the-art encrypted traffic classifiers on several datasets, including public benchmark datasets and real-world TLS and QUIC traffic collected from the Orange mobile network. In addition to being more accurate, AutoML4ETC's architectures are significantly more efficient and lighter in terms of the number of parameters. Finally, we make AutoML4ETC publicly available for future research.
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.
Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication. Nevertheless, the packet aggregation into flow records naturally causes information loss; therefore, this paper proposes a novel flow extension for traffic features based on the time series analysis of the Single Flow Time series, i.e., a time series created by the number of bytes in each packet and its timestamp. We propose 69 universal features based on the statistical analysis of data points, time domain analysis, packet distribution within the flow timespan, time series behavior, and frequency domain analysis. We have demonstrated the usability and universality of the proposed feature vector for various network traffic classification tasks using 15 well-known publicly available datasets. Our evaluation shows that the novel feature vector achieves classification performance similar or better than related works on both binary and multiclass classification tasks. In more than half of the evaluated tasks, the classification performance increased by up to 5\%.
The classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. It can be utilized for quality of service (QoS) management and dynamic resource allocation. However, traditional approaches such as Deep Packet Inspection (DPI) can not be directly applied to encrypted data flows. Therefore, new real-time encrypted traffic classification algorithms need to be investigated to handle dynamic transmission. In this study, we examine the real-time encrypted 5G Non-Standalone (NSA) application-level traffic classification using physical channel records. Due to the vastness of their features, decision-tree-based gradient boosting algorithms are a viable approach for classification. We generate a noise-limited 5G NSA trace dataset with traffic from multiple applications. We develop a new pipeline to convert sequences of physical channel records into numerical vectors. A set of machine learning models are tested, and we propose our solution based on Light Gradient Boosting Machine (LGBM) due to its advantages in fast parallel training and low computational burden in practical scenarios. Our experiments demonstrate that our algorithm can achieve 95% accuracy on the classification task with a state-of-the-art response time as quick as 10ms.
The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC). However, to tame the dependency from task-specific large labeled datasets we need to find better ways to learn representations that are valid across tasks. In this work we investigate this problem comparing transfer learning, meta-learning and contrastive learning against reference Machine Learning (ML) tree-based and monolithic DL models (16 methods total). Using two publicly available datasets, namely MIRAGE19 (40 classes) and AppClassNet (500 classes), we show that (i) using large datasets we can obtain more general representations, (ii) contrastive learning is the best methodology and (iii) meta-learning the worst one, and (iv) while ML tree-based cannot handle large tasks but fits well small tasks, by means of reusing learned representations, DL methods are reaching tree-based models performance also for small tasks.
All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as application classification, attack detection and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model NetGPT for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. With diverse traffic datasets from encrypted software, DNS, private industrial protocols and cryptocurrency mining, expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks on traffic datasets, and outperform state-of-the-art baselines by a wide margin.
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, leading to discrepancies between the local training dataset and long-term distribution. To mitigate this problem, we propose three local cache update rules - First-In-First-Out (FIFO), Static Ratio Selective Replacement (SRSR), and Dynamic Ratio Selective Replacement (DRSR) - that update the local cache of each client while considering the limited cache capacity. Furthermore, we derive a convergence bound for our proposed SFL algorithm as a function of the distribution discrepancy between the long-term data distribution and the client's local training dataset. We then evaluate our proposed algorithm on two datasets: a network traffic classification dataset and an image classification dataset. Our experimental results demonstrate that our proposed local cache update rules significantly reduce the distribution discrepancy and outperform the baseline methods. Our study advances the field of SFL and provides practical cache management solutions in federated learning.
Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the traffic flows on a wired network under the same network management domain. This assumption limits the capabilities of TC systems in wireless networks since users’ traffic on one network domain can be negatively impacted by undetected users’ traffic from other network domains or detected ones but with no traffic context in a shared spectrum. To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack. We propose a spectrum-based procedure that uses a DL-based classifier to realize this framework. We design two DL-based classifiers, a novel Convolutional Neural Network (CNN) spectrum-based TC and a Recurrent Neural Networks (RNN) as baseline architecture, and benchmark their performance on three TC tasks at different radio stack layers. The datasets were generated by combining packet traces from real transmissions with an 802.11 standard-compliant waveform generator. Performance evaluations show that the best model can achieve an accuracy above 92% in the most demanding TC task, a drop of only 4.37% in accuracy compared to a byte-based DL approach, with micro-second per-packet prediction time, which is very promising for delivering real-time spectrum-based traffic analyzers.
Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values. In this work, we are motivated by network traffic classification, where traditional data imputation methods do not perform well. We recognize that no existing method directly accounts for classification accuracy during data imputation. Therefore, we propose a joint data imputation and data classification method, termed generative adversarial classification network (GACN), whose architecture contains a generator network, a discriminator network, and a classification network, which are iteratively optimized toward the ultimate objective of classification accuracy. For the scenario where some data samples are unlabeled, we further propose an extension termed semi-supervised GACN (SSGACN), which is able to use the partially labeled data to improve classification accuracy. We conduct experiments with real-world network traffic data traces, which demonstrate that GACN and SS-GACN can more accurately impute data features that are more important for classification, and they outperform existing methods in terms of classification accuracy.
Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.
Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging ofdatasets and ML models, high volumes of network traffic etc. Thispaper argues that it is necessary to augment traditional workflowsof ML training&deployment and adapt Active Learning concepton network traffic analysis. The paper presents a novel ActiveLearning Framework (ALF) to address this topic. ALF providesprepared software components that can be used to deploy an activelearning loop and maintain an ALF instance that continuouslyevolves a dataset and ML model automatically. The resultingsolution is deployable for IP flow-based analysis of high-speed(100 Gb/s) networks, and also supports research experiments ondifferent strategies and methods for annotation, evaluation, datasetoptimization, etc. Finally, the paper lists some research challengesthat emerge from the first experiments with ALF in practice.