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SyNIG: Synthetic Network Traffic Generation through Time Series Imaging

Conference
Sivaroopan, Nirhoshan, Kattadige, Chamara, Muramudalige, Shashika, Jourjon, Guillaume, Jayasumana, Anura and Thilakarathna, Kanchana
48th IEEE Conference on Local Computer Networks (LCN), 2023
Publication year: 2023.10

Abstract: Immense growth of network usage and the associated proliferation of network, traffic, traffic classes, and diverse QoS requirements pose numerous challenges for network operators. Though data-driven approaches can provide better solutions for these challenges, limited data has been a barrier to developing those methods with high resiliency. In this work, we propose SyNIG (Synthetic Network Traffic Generation through Time Series Imaging) , which utilizes Generative Adversarial Networks (GANs) for network traffic synthesis by converting time series data to a specific image format called GASF (Gramian Angular Summation Field). With GASF images we encode correlation between samples in 1D signals on a single 2D pixel map. Taking three types of network traffic; video streaming, accessing websites
and IoT, we synthesize over 200,000 traces using over 40,000 original traces generalizing our method for different network traffic. We validate our method by demonstrating the fidelity of the synthetic data and applying them to several network related use cases showing improved performance.

Robust open-set classification for encrypted traffic fingerprinting

Journal
Thilini Dahanayaka, Yasod Ginige, Yi Huang, Guillaume Jourjon and Suranga Seneviratne
Computer Networks, 2023
Publication year: 2023.09

Abstract: Encrypted network traffic has been known to leak information about their underlying content through side-channel information leaks. Traffic fingerprinting attacks exploit this by using machine learning techniques to threaten user privacy by identifying user activities such as website visits, videos streamed, and messenger app activities. Although state-of-the-art traffic fingerprinting attacks have high performances, even undermining the latest defenses, most of them are developed under the closed-set assumption. To deploy them in practical situations, it is important to adapt them to the open-set scenario, which allows the attacker to identify its target content while rejecting other background traffic. At the same time, in practice, these models need to be deployed on in-networking devices such as programmable switches, which have limited memory and computation power. Model weight quantization can reduce the memory footprint of deep learning models while at the same time, allowing inference to be done as integer operations as opposed to floating point operations. Open-set classification in the domain of traffic fingerprinting has not been explored well in prior work and none of them explored the effect of quantization on the open-set performance of such models. In this work, we propose a framework for robust open-set classification of encrypted traffic based on three key ideas. First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets. Next, we show that traffic fingerprinting models can be quantized without a significant drop in both closed-set and open-set accuracy and therefore, they can be readily deployed on in-network computing devices. Finally, we show that when the above three components are combined, the resulting open-set classifier outperforms all other open-set classification methods evaluated across five datasets with a minimum and maximum increase in F1_Score of 8.9% and 77.3% respectively.

Design of a Large-N Correlator for the SKA Low Telescope

Conference
D. Humphrey, G. Hampson, J. Bunton, G. Babich, Y. Chen, C. Phillips, X, Deng, B. Bacic, K. Bengston, G. Jourjon, and A. Bolin
URSI 2023
Publication year: 2023.08

Continuous Integration Testing of Real-time Signal Processing and Control for the SKA Low Correlator and Beamformer

Conference
A. Bolin, S. Chiang, G. Babich, G. Hampson, B. Bacic, K. Bengston, G. Jourjon, D. Humphrey, J. Bunton, and Y. Chen
URSI GASS 2023
Publication year: 2023.08

POSTER: Performance Characterization of Binarized Neural Networks in Traffic Fingerprinting

Conference
Yiyan Wang, Thilini Dahanayaka, Guillaume Jourjon, Suranga Seneviratne
Proceedings of ACM ASIA Conference on Com- puter and Communications Security (ACM AsiaCCS’23).
Publication year: 2023.07

Abstract:

Traffic fingerprinting allows making inferences about encrypted traffic flows through passive observation. They have been used for tasks such as network performance management and analytics and in attacker settings such as censorship and surveillance. A key challenge when implementing traffic fingerprinting in real- time settings is how the state-of-the-art traffic fingerprint models can be ported into programmable in-network computing devices with limited computing resources. Towards this, in this work, we characterize the performance of binarized traffic fingerprinting neural networks that are efficient and well-suited for in-network computing devices and propose a new data encoding method that is better suited for network traffic. Overall, we show that the proposed binary neural network with first-layer binarization and last-layer quantization reduces the performance requirement of hardware equipment while retaining the accuracies of those models of binary datasets over 70%. Furthermore, when combined with our proposed encoding algorithm, accuracies of binarized models of numeric datasets show further improvements to achieve over 65% accuracy.

Calibrated reconstruction based adversarial autoencoder model for novelty detection

Journal
Yi Huang, Ying Li, Guillaume Jourjon, Suranga Seneviratne, Kanchana Thilakarathna, Adriel Cheng, Darren Webb, Richard Yi Da Xu
Pattern Recognition Letters Volume 169, May 2023, Pages 50-57
Publication year: 2023.04

Abstract: Novelty detection detects outliers located at any location, such as abnormalities (i.e., far distance outliers) and novel/unobserved patterns (i.e., close distance outliers). While many novelty detection approaches have been proposed in the literature, they generally focus on detecting one specific type of outlier, e.g., Multi-Class Open Set Recognition (MCOSR) and One-Class Novelty Detection (OCND) approaches are applied for far and close distance outlier detection, respectively. However, in practice, it is difficult to measure in advance whether the distance between outliers and inliers is far or close. Recent work on outlier detection at any location with a unified model has yielded mixed performance. In this paper, we propose a new unified model, named Calibrated Reconstruction Based Adversarial AutoEncoder (CRAAE), for location agnostic outlier detection. The key idea is to integrate implicit and explicit confidence calibration strategies into a reconstruction based model for building a more accurate decision boundary. We leverage the category information disentangled from feature space to calibrate the decision metric (i.e., reconstruction error) constructed in the original data space. CRAAE also adds Uniform or Dirichlet noise into the artificial outlier generation process to represent various outliers. Experimental results show that CRAAE can outperform state-of-the-art unified models (e.g., GPND) and achieve similar performance with OCND and MCOSR methods in close and far distance outlier detection, respectively.

MapChain-D: A Distributed Blockchain for IIoT Data Storage and Communications

Journal
Tiantong Wu, Graduate, Guillaume Jourjon, Kanchana Thilakarathna, and Phee Lep Yeoh
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. XX, NO. XX, XXXX
Publication year: 2023.01

Abstract:

With the rapid growth of the Industrial Internet of Things (IIoT) devices, managing extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralised data centres suffer from high latency for large-scale IIoT data storage due to the increased communications and latency overheads, distributed storage frameworks such as blockchains have become promising solutions. In this paper, we design and analyse a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, named MapChain-D , consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup. MapChain-D is designed for practical IIoT applications with storage, latency, and communications constraints. Detailed data exchange protocols are presented for the data insertion and retrieval operations in MapChain-D . Based on these, theoretical analyses are provided on the space, time, and communications complexities of MapChain-D compared with conventional single-chain frameworks with local and distributed data storage. We implement our MapChain-D prototype using open-source LoRaWAN communications with multiple RPi and Arduino devices, Kademlia-based distributed hash table (DHT), and Ethereum-based blockchain with proof-of-authority (PoA) consensus. Experimental results from our prototype show that MapChain-D is more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility of MapChain-D with different numbers of edge nodes in the system.

Inline Traffic Analysis Attacks on DNS over HTTPS

Conference
T Dahanayaka, Z Wang, G Jourjon, S Seneviratne
2022 IEEE 47th Conference on Local Computer Networks (LCN), 132-139
Publication year: 2022.11

Abstract:

Even though end-to-end encryption was introduced to Domain Name System (DNS) communications to ensure user privacy and there is an increase in adoption of DNS over HTTPS (DoH), prior research has demonstrated that encrypted DNS traffic is vulnerable to traffic analysis attacks. However, these attacks were demonstrated under strong assumptions such as handling only closed-set classification or doing only post-event analysis. In this work we demonstrate traffic analysis attacks on DoH without such strong assumptions. We first show the feasibility of website fingerprinting over DoH traffic and present an inline traffic analysis attack that achieve over 90% accuracy using DoH traces of length as short as ten packets. Next, we propose a novel open-set classification method and achieve over 75% accuracy on both closed-set and open-set samples for the open-set scenario. Finally, we demonstrate that the same attack can be performed without any knowledge on the start of the activity.

Introducing “CNIC” - Enabling SKA Low Realtime In-System Testing

Workshop
G.A. Hampson, G.C. Babich, A.B. Bolin, J.C. van Aardt, J. Li, G. Jourjon, D. Humphrey, K.J. Bengston, J.D. Bunton, Y. Chen
International Phased Array Feed (PAF) & Advanced Receiver Workshop
Publication year: 2022.10

Task Adaptive Siamese Neural Networks for Open-Set Recognition of Encrypted Network Traffic With Bidirectional Dropout

Journal
Yi Huang; Ying Li; Timothy Heyes; Guillaume Jourjon; Adriel Cheng; Suranga Seneviratne; Kanchana Thilakarathna; Darren Webb; Richard Yi Da Xu
Pattern Recognition Letters
Publication year: 2022.05

Abstract:

Existing deep learning approaches have achieved high performance in encrypted network traffic analysis tasks. However, some realistic scenarios, such as open-set recognition on dynamically changing tasks, challenge previous methods. Classic few-shot learning methods are used widely for these tasks in certain domains, such as computer vision and natural language processing. Nonetheless, few-shot open-set recognition for encrypted network traffic is still an unexplored area. This paper proposes a probability based task adaptive Siamese open-set recognition model for encrypted network traffic classification. Our contributions are threefold: First, we introduce generated positive and negative pairs into the Siamese Neural Network training process to shape a more precise similarity boundary through bidirectional dropout data augmentation. Second, we utilize Dirichlet Process Gaussian Mixture Model (DPGMM) distribution to fit the similarity scores of the negative pairs constructed by the support set of each query task, and create a new open- set recognition metric. Third, by leveraging the extracted features from coarse and fine-granular levels, we construct a hierarchical cross entropy loss to improve the confidence of the similarity score. Extensive experiments on a network traffic dataset and the Omniglot dataset demonstrate the superiority of our proposed approaches, which can respectively obtain up to 4.5% and 1.2% performance gain in terms of accuracy as well as 4.0% and 1.8% in terms of area under the receiver operating characteristic (AUROC).

From Traffic Classes to Content: A Hierarchical Approach for Encrypted Traffic Classification

Journal
Ying Li, Yi Huang, Suranga Seneviratne, Kanchana Thilakarathna, Adriel Cheng, Guillaume Jourjon, Darren Webb, David B. Smith and Richard Yi Da Xu
Elsevier Computer Networks
Publication year: 2022.05

Abstract: The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic content such as fake news, copy-righted material, and propaganda videos. Recent advances in machine learning techniques have shown great promise in extracting content fingerprints from encrypted traffic captured at the various points in IP core networks. Nonetheless, content fingerprinting from listening to encrypted wireless traffic remains a challenging task due to the difficulty in distinguishing re-transmissions and multiple flows on the same link. In this paper, we show the potential of fingerprinting internet traffic by passively sniffing WiFi frames in air, without connecting to the WiFi network by leveraging deep learning methods. First, we show the possibility of building a generic traffic classifier using a hierarchical approach that is able to identity most common traffic types in the Internet and reveal fine-granular details such as identifying the exact content of the traffic. Second, we demonstrate the possibility of using Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) to identify streaming traffic, such as video and music, from a closed set, by sniffing WiFi traffic that is encrypted at both Media Access Control (MAC) and Transport layers. Overall, our results demonstrate that we can achieve over 95% accuracy in identifying traffic types such as web, video streaming, and audio streaming as well as identifying the exact content consumed by the user.

VideoTrain++: GAN-Based Adaptive Framework for Synthetic Video Traffic Generation

Journal
Chamara Madarasingha, Shashika R. Muramudalige, Guillaume Jourjon, Anura Jayasumana and Kanchana Thilakarathna
Elsevier Computer Network
Publication year: 2022.01

Abstract: Video streaming traffic has been dominating the global network and the challenges have exacerbated with the gaining popularity of interactive videos, a.k.a.360 videos, as they require more network resources. However, effective provision of network resources for video streaming traffic is problematic due to the inability to identify video traffic flows through the network because of end-to-end encryption. Despite the promise given for network security and privacy, end-to-end encryption also provides a shield for adversaries. To this end, encrypted traffic classification and content fingerprinting with advanced Machine Learning (ML) methods have been proposed. Nevertheless, achieving high performance requires a significant amount of training data, which is a challenging task in operational networks due to the sheer volume of traffic and privacy concerns. As a solution, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesize video streaming data for two different tasks, 360/normal video classification and video fingerprinting. The solution consists of a percentile-based data mapping mechanism to enhance the data generation process, which is further supported by novel algorithms for data pre-processing and GAN model training. Taking over 6600 actual video traces and generating over 150,000 new traces, our ML-based traffic classification results show a 5–16% of accuracy improvement in both tasks.

SKA Low Atomic COTS Correlator and Beamformer

Journal
G.A. Hampson, J.D. Bunton, D. Humphrey, K.J. Bengston, G. Jourjon, A.B. Bolin, Y. Chen, E.R. Troup, G.C. Babich, J.C. van Aardt
Journal of Astronomical Telescopes, Instruments, and Systems (JATIS)
Publication year: 2022.01

Abstract: The Square Kilometre Array (SKA) Low is a next generation radio telescope, consisting of 512 antenna stations spread over 65 km, to be built in Western Australia. The Correlator and BeamFormer (CBF) design is central to the telescope signal processing. CBF receives 6 Tera-bits-per-second (Tbps) of station data continuously and processes it in real time with a compute load of 2 peta-operations-per-second (Pops). The correlator calculates up to 22 million cross products between all pairs of stations, while the beamformers coherently sum station data to form more than 500 beams. The output of the correlator is up to 7 Tbps, and the beamformer 2 Tbps. The design philosophy, called “Atomic COTS”, is based on commercial-off-the-shelf (COTS) hardware. Data routing is implemented in network switches programmed using the P4 language and the signal processing occurs in COTS FPGA cards. The P4 language allows routing to be determined from the metadata in the Ethernet packets from the stations. That is, metadata describing the contents of the packet determines the routing. Each FPGA card inputs a fraction of the overall bandwidth for all stations and then implements the processing needed to generate complete science data products. Generation of complete science products in a single FPGA is named here as Atomic processing. A Tango distributed control system configures the multitude of processing modes as well as maintaining the overall health of the CBF system hardware. The resulting 6 Tbps in and 9 Tbps out, 2 Pops Atomic COTS network attached accelerator occupies five racks and consumes 60 kW.

Dissecting Traffic Fingerprinting CNNs with Filter Activations

Journal
Thilini Dahanayaka, Guillaume Jourjon and Suranga Seneviratne
Elsevier Computer Network
Publication year: 2022.01

HTTPS encrypted traffic flows leak information on underlying contents through various statistical properties such as packet lengths and timing, enabling traffic fingerprinting attacks. Recent traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) to record very high accuracies undermining state-of-the-art defenses. In this paper, we analyze such CNNs to understand their inner workings which helps in building efficient traffic classifiers and effective defenses. First, we experiment on three datasets and show that website fingerprinting CNNs focus majorly on transitions between uploads and downloads in trace fronts while video fingerprinting CNNs focus more on finer shapes of periodic bursts. Next, we show that traffic fingerprinting CNNs exhibit transfer learning capabilities allowing identification of new websites with fewer data. We also demonstrate how traffic fingerprinting CNNs outperform Recurrent Neural Networks (RNNs) due to their resilience to random shifts in data, which is common in network traces. We further generalize these observations on other publicly available network traffic datasets. Leveraging our observations, we propose two new defenses against traffic fingerprinting. Our first defense FRONT-U, defends website visits by obfuscating transitions between uploads and downloads in trace fronts and provides similar privacy as the state-of-the-art defense FRONT, with half the data overhead. Our second defense STOMA, defends streaming traffic by obfuscating the finer sub-bursts within major bursts of a trace using only the nextfew seconds as opposed to using the entire trace as in the state-of-the-art.

Networked Answer to “Life, The Universe, and Everything”

Conference
Giles Babich, Keith Bengston, Andrew Bolin, John Bunton, Yuqing Chen, Grant Hampson, David Humphrey, and Guillaume Jourjon
Symposium on Architectures for Networking and Communications Systems (ANCS'21), December 13--16, 2021, Lafayette, IN, USA
Publication year: 2021.12

ABSTRACT

In the last few years, Input/Output (I/O) bandwidth limitation of legacy computer architectures forced us to reconsider where and how to store and compute data across a large range of applications. This shift has been made possible with the concurrent development of both smart NICs and programmable switches with a common programming language(P4), and the advent of attached High Bandwidth Memory within smartNICs/FPGAs. Recently, proposals to use this kind of technology have emerged to tackle computer science related issues such as fast consensus algorithm in the net-work, network accelerated key-value stores, machine learn-ing, or data-center data aggregation. In this paper, we intro-duce a novel architecture that leverages these advancements to potentially accelerate and improve the processing of radio-astronomy Digital Signal Processing (DSP), such as correlators or beamformers, at unprecedented continuous rates inwhat we have called the “Atomic COTS” design. We givean overview of this new type of architecture to accelerate digital signal processing, leveraging programmable switches and HBM capable FPGAs. We also discuss how to handle radio astronomy data streams to pre-process this stream ofdata for astronomy science products such as pulsar timingand search. Finally, we illustrate, using a proof of concept,how we can process emulated data from the Square Kilometer Array(SKA) project to time pulsars.

SMAUG: Streaming Media Augmentation Using CGANs as a Defence Against Video Fingerprinting

Conference
Aleksandr Vaskevich, Thilini Dahanayaka, Suranga Seneviratne and Guillaume Jourjon
Proceedings of the 20th IEEE International Symposium on Network Computing and Applications (NCA 2021)
Publication year: 2021.11

Abstract:

Traffic fingerprinting and developing defenses against them has always been an arms race between the attackers and the defenders. The rapid evolution of deep learning methods makes developing stronger traffic fingerprinting models much easier, while overhead, latency, and deployment constraints restrict the abilities of the defenses. As such, there is always the need of coming up with novel defenses against traffic fingerprinting. In this paper, we propose SMAUG, a novel CGAN-based (Conditional Generative Adversarial Network) defense to protect video streaming traffic against fingerprinting. We first assess the performance of various GANs in video streaming traffic synthesis using multiple GAN quality metrics and show that CGAN outperforms other types of GANs such as basic GANs and WGANs (Wasserstein GAN). Our proposed defense, SMAUG, uses CGANs to synthesize video traffic flows and use those synthesized flows to camouflage the original traffic that needs protection. We compare SMAUG with other state-of-the-art defenses – FPA and d*-private methods, as well as a kernel density estimation-based baseline and show that SMAUG provides better privacy with lower overhead and delay.

VideoTrain: A Generative Adversarial Framework for Synthetic Video Traffic Generation

Conference
Chamara Kattadige, Shashika R Muramudalige, Kwon Nung Choi, Guillaume Jourjon, Haonan Wang, Anura Jayasumana, Kanchana Thilakarathna
2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
Publication year: 2021.06

SETA++: Real-Time Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

Journal
Chamara Kattadige, Kwon Nung Choi, Achintha Wijesinghe, Arpit Nama, Kanchana Thilakarathna, Suranga Seneviratne, Guillaume Jourjon
IEEE Transactions on Network and Service Management
Publication year: 2021.05

Understanding Traffic Fingerprinting CNNs

Conference
Thilini Dahanayaka, Guillaume Jourjon, Suranga Seneviratne
2020 IEEE 45th Conference on Local Computer Networks (LCN)
Publication year: 2020.11

SETA: Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

Conference
Kwon Nung Choi, Achintha Wijesinghe, Chamara Manoj Madarasingha Kattadige, Kanchana Thilakarathna, Suranga Seneviratne, Guillaume Jourjon
2020 IEEE 45th Conference on Local Computer Networks (LCN)
Publication year: 2020.11

A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store

Journal
Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume Jourjon
IEEE Transactions on Mobile Computing
Publication year: 2020.07

The Attack of the Clones Against Proof-of-Authority

Conference
P. Ekparinya, V. Gramoli, G. Jourjon
NDSS 202
Publication year: 2020.02

Fast and Private Network Function Outsourcing

Journal
H. Asghar, E. de Cristofaro, G. Jourjon, M. A. Kaafar, L. Mathy, L. Melis, C. Russell
Elsevier Computer Network
Publication year: 2019.11

An SDN Perspective on Multi-connectivity and Seamless Flow Migration

Journal
S. Hatonen, MTI ul Huque, A. Rao, G. Jourjon, V. Gramoli, and S. Tarkoma
IEEE Networking Letter
Publication year: 2019.11

Software Defined Network’s Garbage Collection with Clean-Up Packets

Journal
MTI ul Huque, G. Jourjon, C. Russell, and V. Gramoli
IEEE Transactions on Network and Service Management
Publication year: 2019.06

A Linked Data Quality Assessment Framework for Network Data

Conference
A. To, R. Meymandpour, J. Davis, G. Jourjon, and J. Chan
GRADES-NDA workshop at SIGMOD 2019
Publication year: 2019.06

A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps

Conference
J. Rajasegaran, N. Karunanayake, A. Gunathillake, S. Seneviratne, and G. Jourjon
2019 World Wide Web Conference (WWW 19), May
Publication year: 2019.05

Impact of network delays on Hyperledger Fabric

Conference
T. S. L. Nguyen, G. Jourjon, M. Potop-Butucaru, and K. Loan Thai
CryBlock 2019, 2nd Workshop on Cryptocurrencies and Blockchains for Distributed Systems, co-located with INFOCOM 2019
Publication year: 2019.04

A Delay-Tolerant Payment Scheme Based On Blockchain

Journal
Y. Hu, A. Manzoor, P. Ekparinya, M. Liyanage, K. Thilakarathna, G. Jourjon, A. Seneviratne, and M. Ylianttila
IEEE Access, vol. 7, Mar. 2019
Publication year: 2019.03

Deep Content: Unveiling Video Streaming Content From Encrypted WiFi Traffic

Conference
Y. Li, Y. Huang, A. Cheng, G. Jourjon, S. Seneviratne, K. Thilakarathna, D. Webb, and R. Xu
IEEE International Symposium on Network Computing and Applications (NCA 2018)
Publication year: 2018.10

Impact of Man-In-The-Middle Attacks on Ethereum

Conference
P. Ekparinya, V. Gramoli, and G. Jourjon
IEEE International Symposium on Reliable Distributed Systems 2018 (SRDS
Publication year: 2018.06

A delay-tolerant payment scheme on the ethereum blockchain

Conference
Ahsan Manzoor, Yining Hu, Madhusanka Liyanage, Parinya Ekparinya, Kanchana Thilakarathna, Guillaume Jourjon, Aruna Seneviratne, Salil Kanhere, Mika E Ylianttila
2018 IEEE 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM)
Publication year: 2018.06

DLibOS: Performance and Protection with a Network-on-Chip

Conference
S. Mallon, V. Gramoli, G. Jourjon
ACM ASPLOS 2018, 23rd ACM International Conference on Architectural Support for Programming Languages and Operating Systems, March 2018, Williamsburg, VA, USA
Publication year: 2018.03

MPTCP Energy Enhancement Paradox: A Q-Learning Approach

Conference
M. J. Shamani, S. Rezaeiy, G. Jourjon, and A. Seneviratne
International Telecommunication Networks and Applications Conference (ITNAC 2017)
Publication year: 2017.12

Stratosphere: Dynamic IP overlay above the clouds

Conference
P. Ekparinya, V. Gramoli, G. Jourjon, and L. Zhu
IEEE Local Computer Networks 2017 (LCN)
Publication year: 2017.11

Measuring, Characterizing, and Detecting Facebook Like Farms

Journal
M. Ikram, L. Onwuzurike, S. Farooqi, E. De Cristofaro, A. Friedman, G. Jourjon, M. A. Kaafar, Z. Shafiq
ACM Transactions on Privacy and Security, Volume 20, Issue 4, September 2017, pp. 13:1–13:2
Publication year: 2017.09

Garbage Collection of Forwarding Rules in Software Defined Networks

Journal
MTI ul Huque, G. Jourjon, V. Gramol
IEEE Communications Magazine, June 2017
Publication year: 2017.06

e-DASH: Modelling An Energy-Aware DASH Player

Conference
B. Varghese, G. Jourjon, K. Thilakarathne, and A. Seneviratne
IEEE WoWMoM 2017, Jun. 2017
Publication year: 2017.06

Large-Scale Dynamic Controller Placement.

Journal
MTI ul Huque, W. Si, G. Jourjon, V. Gramoli
IEEE Transactions on Network and Service Management, Volume 14, Issue 1, March 2017
Publication year: 2017.03

Characterizing Key Stakeholders in an Online Black-Hat Marketplace

Conference
S. Farooqi, M. Ikram, E. De Cristofaro, A. Friedman, G. Jourjon, M. A. Kaafar, Z. Shafiq, F. Zaffar
IEEE Symposium on Electronic Crime Research (eCrime 2017
Publication year: 2017.03

FORGE Toolkit: Leveraging Distributed Systems in eLearning Platforms.

Journal
G. Jourjon, J. M. Marquez-Barja, T. Rakotoarivelo, A. Mikroyannidis, K. Lampropoulos, S. Denazis, Christos Tranoris, D. Pareit, J. Domingue, L. A DaSilva, and M. Ott
IEEE Transactions on Emerging Topics in Computing, Vol. 5 (1), pp: 7 - 19, Jan. 2017.
Publication year: 2017.01

Endpoint-transparent Multipath Transport with Software-defined Networks

Conference
D. Banfi, O. Mehani, G. Jourjon, L. Schwaighofer, and R. Holz
IEEE Local Computer Networks (LCN), Dubai, November 201
Publication year: 2016.11

Are Today’s SDN Controller Ready for Primetime

Conference
S. Mallon, V. Gramoli, and G. Jourjon
IEEE Local Computer Networks (LCN), Dubai, November 2016
Publication year: 2016.11

FORGE Enabling FIRE Facilities for the e-Learning Community

Conference
O. Fourmaux, M. Y. Rahman, C. Tranoris, D. Pareit, J. Vanhie-Van Gerwen, G. Jourjon, D. Collins, J. Marquez Barja
19th International Conference on Interactive Collabortive Learning, 201
Publication year: 2016.09

Applying a methodology for the design, delivery and evaluation of learning resources for remote experimentation

Conference
A. Mikroyannidis, J. Domingue, D. Pareit, J. Vanhie-Van Gerwen, C. Tranoris, G. Jourjon, J. M. Marquez-Barja
IEEE Global Engineering Education Conference (EDUCON), 201
Publication year: 2016.03

Revisiting the controller placement problem

Conference
MTI ul Huque, G. Jourjon, V. Gramoli
2015 IEEE 40th Conference on Local Computer Networks (LCN), pp. 450-45
Publication year: 2015.11

Disaster-Tolerant Storage with SDN

Conference
V. Gramoli, G. Jourjon, O. Mehani
nternational Conference on NETworked sYStem, NETYS 2015, Agadir, Morocco, May 13-15
Publication year: 2015.05

Paying for Likes? Understanding Facebook Like Fraud Using Honeypots

Conference
E. De Cristofaro, A. Friedman, G. Jourjon, D. Kaafar, Z. Shafiq
ACM SIGCOMM Internet Measurement (IMC) 2014 conference
Publication year: 2014.11

Greening Web Servers: A Case for Ultra Low-power Web Servers

Conference
B. Varghese, N. Carlsson, G. Jourjon, A. Mahanti, P. Shenoy
International Green Computing Conference, IGCC 2014, Nov. 2014, USA
Publication year: 2014.11

FORGE: Enhancing elearning and research in ICT through remote experimentation

Conference
J. M. Marquez-Barja, G. Jourjon, A. Mikroyannidis, C. Tranoris, J. Domingue, L. DaSilva
EDUCON 2014, IEEE Global Engineering Education Conference, Istanbul, Apr. 2014.
Publication year: 2014.04

Designing and Orchestrating Reproducible Experiments on Federated Networking Testbeds

Journal
T. Rakotoarivelo, G. Jourjon and M. Ott
Elsevier Computer Networks, vol. 63, Apr. 201
Publication year: 2014.04

An Instrumentation Framework for the Critical Task of Measurement Collection in the Future Internet

Journal
O. Mehani, G. Jourjon, T. Rakotoarivelo and M. Ott
Elsevier Computer Networks, vol. 63, pp. 68–83, Apr. 201
Publication year: 2014.04

HPC Applications Deployment on Distributed Heterogeneous Computing Platforms via OMF, OML and P2PDC

Conference
D. El Baz, T. T. Nguyen, G. Jourjon, T. Rakotoarivelo
PDP 2014, the 22nd Euromicro International Conference on Parallel, distributed and network-based Computing., Turin, Feb. 201
Publication year: 2014.02

Insights of File-Sharing System Forums

Conference
G. Jourjon, O. Mehani, T. Rakotoarivelo
WNM 2013, 7th IEEE Workshop on Network Measurements (co-located with LCN 2013), Sydney, Australia, pp. 948–955, October, 201
Publication year: 2013.10

Into the Moana – Hypergraph-based Network Layer Indirection

Conference
Y. Shvartzshnaider, M. Ott, O. Mehani, G. Jourjon, T. Rakotoarivelo and D. Levy
Proceedings of IEEE INFOCOM 2013, April 2013, Turin, Ital
Publication year: 2013.04

Impact of an e-learning platform on CSE Lectures

Conference
G. Jourjon, S. Kanhere and J. Yao
ACM ITiCSE 2011, the 16th Annual Conference on Innovation and Technology in Computer Science Education, June 2011, Darmstadt, Germany
Publication year: 2011.6

Why Simulate When You Can Experience?

Conference
G. Jourjon, T. Rakotoarivelo and M. Ott
ACM SIGCOMM Education Workshop 2011, Toronto, Aug. 2011
Publication year: 2011.08

LabWiki: An Executable Paper Platform for Experiment-based Research,

Conference
G. Jourjon, T. Rakotoarivelo, C. Dwertmann and M. Ott
The International Conference on Computational Science 2011, June 2011, Tsukuba, Japan
Publication year: 2011.02

Promoting the Use of Reliable Rate Based Transport Protocols: The Chameleon Protocol

Journal
E. Lochin, G. Jourjon, S. Ardon and P. Senac
International Journal of Internet Protocol Technology, Volume 5, Issue 4, pp 175–189, Dec. 2010
Publication year: 2010.12

Mobile Multimedia Streaming Improvements with Freeze-DCCP

Conference
O. Mehani, R. Boreli, G. Jourjon and T. Ernst
Mobicom & Wintech 2010 - Demo Session, September 2010, Chicago
Publication year: 2010.09

Measurement Architectures for Network Experiments with Disconnected Mobile Nodes

Conference
J. White, G. Jourjon, T. Rakatoarivelo and M. Ott
n Proc. of TridentCom 2010, ser. LNICST, Vol. 46, pp 315-330, Springer-Verlag, May 2010
Publication year: 2010.05

From Learning to Researching, Ease the Shift through Testbeds

Conference
G. Jourjon, T. Rakatoarivelo and M. Ott
In Proc. of TridentCom 2010, ser. LNICST, Vol. 46, pp 496-505, Springer-Verlag, May 2010
Publication year: 2010.05

A Portal to Support Rigorous Experimental Methodology in Networking Research

Conference
G. Jourjon, T. Rakotoarivelo and M. Ott
TridentCom 2011, May 2011, Shangha
Publication year: 2010.05

Models for an Energy-Efficient P2P Delivery Service

Conference
G. Jourjon, T. Rakotoarivelo and M. Ott
PDP 2010 - The 18th Euromicro International Conference on Parallel, Distributed and Network-Based Computin
Publication year: 2010.02

OMF: a Control and Management Framework for Networking Testbeds

Journal
T. Rakotoarivelo, M. Ott, G. Jourjon and I. Seskar
ACM Operating System Review, Volume 43, Issue 4, pp 54–59, Jan. 201
Publication year: 2010.01

High Performance Peer-to-Peer Distributed Computing with Application to Obstacle Problem

Conference
T. T. Nguyen, D. Elbaz, P. Spiteri, G. Jourjon and M. Chau
HotP2P 2010 in conjunction with IPDPS 2010
Publication year: 2010.01

Mobile Experiments Made Easy with OMF/Orbit

Conference
C. Dwertmann, I. Ergin, G. Jourjon, M. Ott, T. Rakotoarivelo and I. Seskar
SIGCOMM 2009, demo session
Publication year: 2009.08

Design and Validation of a Reliable Rate Based Transport Protocol: The Chameleon Protocol

Conference
E. Lochin, G. Jourjon and S. Ardon
Global Information Infrastructure Symposium, IEEE GIIS 2009
Publication year: 2009.06

Towards sender-based TFRC

Journal
G. Jourjon, E. Lochin and P. Senac
Journal of Internet Engineering, Volume 3, Issue 1, 2009, pp 193–201
Publication year: 2009.03

Improvements in DCCP congestion control for satellite links,

Conference
G. Sarwar, R. Boreli, E. Lochin and G. Jourjon,
2008 International Workshop on Satellite and Space Communications, IWSSC 200
Publication year: 2008.06

Design, Implementation and Evaluation of a QoS-aware Transport Protocol

Journal
G. Jourjon, E. Lochin and P. Senac
Elsevier Computer Communications Journal, Vol. 31, Issue 9, June 2008
Publication year: 2008.06

Towards sender-based TFRC

Conference
G. Jourjon, E. Lochin and P. Senac
IEEE International Conference on Communications 2007 (ICC 2007), June 200
Publication year: 2007.06

IREEL: Remote Experimentation with Real Protocols and Applications over Emulated Network

Journal
L. Dairaine, G. Jourjon, E. Lochin and S. Ardon
Inroads, the ACM SIGCSE Bulletin, Volume 39, Issue 2, June 2007, pp 92–9
Publication year: 2007.06

Study and enhancement of DCCP over DiffServ Assured Forwarding class

Conference
E. Lochin, G. Jourjon and L. Dairaine
Fourth European Conference on Universal Multiservice Networks, IEEE ECUMN’0
Publication year: 2007.03

Optimization of Loss History Initialization

Journal
G. Jourjon, E. Lochin and L. Dairaine
EEE Communications Letters, Volume 11, Number 3, March 2007, pp 276–27
Publication year: 2007.03

Towards a Versatile Transport Protocol

Conference
G. Jourjon, E. Lochin and P. Senac
Student workshop of CONext 2006 in cooperation with ACM SIGCOMM
Publication year: 2006.12

gTFRC, a TCP Friendly QoS-aware Rate Control for Diffserv Assured Service

Journal
E. Lochin, L. Dairaine and G. Jourjon
Springer Telecommunication Systems Journal, Volume 33, Numbers 1-3 / December, 2006
Publication year: 2006.12

Implementation and performance analysis of a QoS-aware TFRC mechanism

Conference
G. Jourjon, E. Lochin, L. Dairaine, P. Senac, T. Moors and A. Seneviratne
14th IEEE ICON 2006, International Conference on Networkin
Publication year: 2006.11

gTFRC: a QoS-aware congestion control algorithm

Conference
E. Lochin, L. Dairaine and G. Jourjon
5th International Conference of Networking, ICN 200
Publication year: 2006.10

REEL: Remote Experimentation with Real Protocols and Applications over Emulated Network

Conference
L. Dairaine, G. Jourjon and E. Exposito
ACM ITiCSE 2006
Publication year: 2006.06

Modeling, Simulation, and Emulation of QoS Oriented Transport Mechanisms

Conference
G. Jourjon, E. Exposito and L. Dairaine
CONext 2005 in cooperation with ACM Sigcomm
Publication year: 2005.12