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See detailToward an SDN-based Data Collection Scheme for Vehicular Fog Computing
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in IEEE International Conference on Communications ICC'2020 (2020, June 07)

With the integration of fog networks and vehicular networks, Vehicular Fog Computing (VFC) is a promising paradigm to efficiently collect data for improving safety, mobility, and driver experience during ... [more ▼]

With the integration of fog networks and vehicular networks, Vehicular Fog Computing (VFC) is a promising paradigm to efficiently collect data for improving safety, mobility, and driver experience during journeys. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a fully-programmable, self-configurable, and context-aware data collection scheme for VFC. This scheme leverages a stochastic model to dynamically estimate the number of fog stations to be deployed. Our simulation results demonstrate that our proposed scheme provides lower latency and higher resiliency compared to classical data collection schemes. [less ▲]

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See detailEnhancing CoAP Group Communication to Support mMTC Over Satellite Networks
Soua, Ridha UL; Palattella, Maria Rita UL; Stemper, André UL et al

in IEEE International Conference on Communications (ICC) (2020, June)

Massive MTC (mMTC) is one of the application scenarios that upcoming 5G networks are expected to support. Satellites come into play in mMTC to complement and extend terrestrial networks in under-served ... [more ▼]

Massive MTC (mMTC) is one of the application scenarios that upcoming 5G networks are expected to support. Satellites come into play in mMTC to complement and extend terrestrial networks in under-served areas, where several services can benefit from the adoption of a group communication model. The IETF has specifically standardized the usage of CoAP group communication. However, CoAP responses are still sent in unicast from each single CoAP server to the CoAP client, which results in a substantial traffic load. Such problem becomes more severe in integrated IoT-Satellite networks given the limited bandwidth of the satellite return channel and the large number of IoT devices in a mMTC scenario. To reduce network traffic overhead in group communication and improve the network responsiveness, this paper proposes an aggregation scheme for the CoAP group communication in combination with Observer pattern and proxying. Results obtained by using the openSAND emulator and CoAPthon library corroborate the merit of our optimization in terms of overhead reduction and delay. [less ▲]

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See detailSecurity and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
Mitseva, Asya UL; Aleksandrova, Marharyta UL; Engel, Thomas UL et al

in Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor (2020, May)

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See detailSDN-based Misbehavior Detection System for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (2020, May)

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing ... [more ▼]

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing misbehavior detection systems are static and do not adapt to the context of vehicles. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a context-aware MDS. Based on the context, our proposed system can tune security parameters to provide accurate detection with low false positives. Our system is Sybil attack-resistant and compliant with vehicular privacy standards. The simulation results show that, under different contexts, our system provides a high detection ratio and low false positives compared to a static MDS. [less ▲]

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See detailAnalyzing PeerFlow -- A Bandwidth Estimation System for Untrustworthy Environments
Mitseva, Asya UL; Engel, Thomas UL; Panchenko, Andriy

in Analyzing PeerFlow -- A Bandwidth Estimation System for Untrustworthy Environments (2020, March)

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See detailA Multi-Hop Broadcast Wave Approach for Floating Car Data Collection in Vehicular Networks
Turcanu, Ion UL; Salvo, Pierpaolo; Baiocchi, Andrea et al

in Vehicular Communications (2020)

Inter-Vehicle Communication (IVC) is bringing connected and cooperative mobility closer to reality. Vehicles today are able to produce huge amounts of information, known in the literature as Floating Car ... [more ▼]

Inter-Vehicle Communication (IVC) is bringing connected and cooperative mobility closer to reality. Vehicles today are able to produce huge amounts of information, known in the literature as Floating Car Data (FCD), containing status information gathered from sensing the internal condition of the vehicle and the external environment. Adding networking capabilities to vehicles allows them to share this information among themselves and with the infrastructure. Collecting real-time FCD information from vehicles opens up the possibility of having access to an enormous amount of useful information that can boost the development of innovative services and applications in the domain of Intelligent Transportation System (ITS). In this paper we propose several solutions to efficiently collect real-time FCD information in Dedicated Short-Range Communication (DSRC)-enabled Vehicular Ad Hoc Networks (VANETs). The goal is to improve the efficiency of the FCD collection operation while keeping the impact on the DSRC communication channel as low as possible. We do this by exploiting a slightly modified version of a standardized data dissemination protocol to create a backbone of relaying vehicles that, by following local rules, generate a multi-hop broadcast wave of collected FCD messages. The proposed protocols are evaluated via realistic simulations under different vehicular densities and urban scenarios. [less ▲]

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See detailPOSTER: Traffic Splitting to Counter Website Fingerprinting
de La Cadena Ramos, Augusto Wladimir UL; Mitseva, Asya UL; Pennekamp, Jan et al

Poster (2019, November 11)

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See detailVPGA: an SDN-based Location Privacy Zones Placement Scheme for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 38th IEEE International Performance Computing and Communications Conference (IPCCC) (2019, October 29)

Making personal data anonymous is crucial to ensure the adoption of connected vehicles. One of the privacy-sensitive information is location, which once revealed can be used by adversaries to track ... [more ▼]

Making personal data anonymous is crucial to ensure the adoption of connected vehicles. One of the privacy-sensitive information is location, which once revealed can be used by adversaries to track drivers during their journey. Vehicular Location Privacy Zones (VLPZs) is a promising approach to ensure unlinkability. These logical zones can be easily deployed over roadside infrastructures (RIs) such as gas station or electric charging stations. However, the placement optimization problem of VLPZs is NP-hard and thus an efficient allocation of VLPZs to these RIs is needed to avoid their overload and the degradation of the QoS provided within theses RIs. This work considers the optimal placement of the VLPZs and proposes a genetic-based algorithm in a software defined vehicular network to ensure minimized trajectory cost of involved vehicles and hence less consumption of their pseudonyms. The analytical evaluation shows that the proposed approach is cost-efficient and ensures a shorter response time. [less ▲]

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See detailBig Automotive Data Preprocessing: A Three Stages Approach
Tawakuli, Amal UL; Kaiser, Daniel UL; Engel, Thomas UL

Poster (2019, October 08)

The automotive industry generates large datasets of various formats, uncertainties and frequencies. To exploit Automotive Big Data, the data needs to be connected, fused and preprocessed to quality ... [more ▼]

The automotive industry generates large datasets of various formats, uncertainties and frequencies. To exploit Automotive Big Data, the data needs to be connected, fused and preprocessed to quality datasets before being used for production and business processes. Data preprocessing tasks are typically expensive, tightly coupled with their intended AI algorithms and are done manually by domain experts. Hence there is a need to automate data preprocessing to seamlessly generate cleaner data. We intend to introduce a generic data preprocessing framework that handles vehicle-to-everything (V2X) data streams and dynamic updates. We intend to decentralize and automate data preprocessing by leveraging edge computing with the objective of progressively improving the quality of the dataflow within edge components (vehicles) and onto the cloud. [less ▲]

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See detailSoftwarization of SCADA: Lightweight Statistical SDN-Agents for Anomaly Detection
Rinaldi, Giulia UL; Adamsky, Florian UL; Soua, Ridha UL et al

in 10th International Conference on Networks of the Future (NoF) (2019, October 04)

The increasing connectivity of restricted areas suchas Critical Infrastructures (CIs) raises major security concernsfor Supervisory Control And Data Acquisition (SCADA) systems,which are deployed to ... [more ▼]

The increasing connectivity of restricted areas suchas Critical Infrastructures (CIs) raises major security concernsfor Supervisory Control And Data Acquisition (SCADA) systems,which are deployed to monitor their operation. Given the impor-tance of an early anomaly detection, Intrusion Detection Systems(IDSs) are introduced in SCADA systems to detect malicious ac-tivities as early as possible. Agents or probes form the cornerstoneof any IDS by capturing network packets and extracting relevantinformation. However, IDSs are facing unprecedented challengesdue to the escalation in the number, scale and diversity of attacks.Software-Defined Network (SDN) then comes into play and canprovide the required flexibility and scalability. Building on that,we introduce Traffic Agent Controllers (TACs) that monitor SDN-enabled switches via OpenFlow. By using lightweight statisticalmetrics such as Kullback-Leibler Divergence (KLD), we are ableto detect the slightest anomalies, such as stealth port scans, evenin the presence of background traffic. The obtained metrics canalso be used to locate the anomalies with precision over 90%inside a hierarchical network topology. [less ▲]

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See detailSDN-based Pseudonym-Changing Strategy for Privacy Preservation in Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Engel, Thomas UL

in 15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob'19) (2019, October)

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies ... [more ▼]

The pseudonym-changing approach is the de-factolocation privacy solution proposed by security standards toensure that drivers are not tracked during their journey. SeveralPseudonym Changing Strategies (PCSs) have been proposed tosynchronize Pseudonym Changing Processes (PCPs) between con-nected vehicles. However, most of the existing strategies are static,rigid and do not adapt to the vehicles’ context. In this paper, weexploit the Software Defined Network (SDN) paradigm to proposea context-aware pseudonym changing strategy (SDN-PCS) whereSDN controllers orchestrate the dynamic update of the securityparameters of the PCS. Simulation results demonstrate that SDN-PCS strategy outperforms typical static PCSs to perform efficientPCPs and protect the location privacy of vehicular network users [less ▲]

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See detailPerformance Analysis of MANET Routing Protocols in Urban VANETs
Di Maio, Antonio UL; Palattella, Maria Rita UL; Engel, Thomas UL

in Lecture Notes in Computer Science (volume 11803) (2019, September 25)

Infrastructure-less communications between moving vehicles present emblematic challenges because of high node mobility and link volatility, which may harm the performances of different categories of ... [more ▼]

Infrastructure-less communications between moving vehicles present emblematic challenges because of high node mobility and link volatility, which may harm the performances of different categories of emerging vehicular applications. In order to move data between vehicles that are not in direct communication range, several distributed routing protocols have been proposed and tested in vehicular networks, highlighting their strengths and weaknesses. Some previous works report disagreeing claims about routing protocol performances in similar vehicular scenarios. Therefore, in this work, we evaluate the performances in terms of Packet Delivery Ratio (PDR), packet delay, frame collision rate, and signaling rate of three well-known routing protocols (AODV, DSDV, and GPSR), simulating them in a realistic Manhattan scenario. Furthermore, we evaluate the impact of typical urban obstacles (e.g. buildings) on the considered performance metrics. We observed that, in the proposed urban scenario, AODV provided the best PDR, GPSR the best packet delay, and DSDV failed to provide satisfactory performances due to signaling-induced congestion. Simulations showed that considering the shadowing effects induced by the buildings in an urban scenario drastically changes the observed performances, i.e. reduces the frame collisions, decreases the PDR, and increases the packet delay. [less ▲]

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See detailMulti-flow congestion-aware routing in software-defined vehicular networks
Di Maio, Antonio UL; Palattella, Maria Rita UL; Engel, Thomas UL

in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) Proceedings (2019, September 22)

5G-enabled vehicular networks will soon allow their users to exchange safety and non-safety related information over heterogeneous communication interfaces. Routing vehicular data flows over multi-hop ... [more ▼]

5G-enabled vehicular networks will soon allow their users to exchange safety and non-safety related information over heterogeneous communication interfaces. Routing vehicular data flows over multi-hop Vehicle-to-Vehicle (V2V) communications is one of the hardest challenges in vehicular networking, and it has been tackled in literature by using distributed algorithms. The distributed approach has shown significant inefficiencies in such dynamic vehicular scenarios, mainly due to poor network congestion control. To overcome the complexity of the envisioned architecture, and the inefficiency of distributed routing algorithms, we hereby propose to leverage the coordination capabilities of Software-Defined Networking (SDN) to determine optimal V2V multi-hop paths and to offload traffic from the Vehicle-to-Infrastructure-to-Vehicle (V2I2V) to the V2V communications, using both cellular and Wi-Fi technologies. In order to achieve this goal, we propose Multi-Flow Congestion-Aware Routing (MFCAR), a centralized routing algorithm that relies on graph theory to choose short and uncongested V2V paths. Realistic simulations prove that MFCAR outperforms well- established centralized routing algorithms (e.g. Dijkstra's) in terms of Packet Delivery Ratio (PDR), goodput and average packet delay, up to a five-fold performance gain. [less ▲]

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See detailAnalysis of Multi-path Onion Routing-based Anonymization Networks
de La Cadena Ramos, Augusto Wladimir UL; Kaiser, Daniel UL; Mitseva, Asya UL et al

in Data and Applications Security and Privacy XXXIII, 2019 (2019, July 15)

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See detailIoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis
Msadek, Mohamed Nizar UL; Soua, Ridha UL; Engel, Thomas UL

in The IEEE Wireless Communications and Networking Conference (WCNC) (2019, April 19)

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface ... [more ▼]

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface, which can seriously harm users’ privacy. We demonstrate in this paper that an attacker is able to disclose sensitive information about the IoT device, such as its type,by identifying specific patterns in IoT traffic. To perform the fingerprint attack, we train machine-learning algorithms based on selected features extracted from the encrypted IoT traffic.Extensive simulations involving the baseline approach show that we achieve not only a significant mean accuracy improvement of 18.5% and but also a speedup of 18.39 times for finding the best estimators. Obtained results should spur the attention of policymakers and IoT vendors to secure the IoT devices they bring to market. [less ▲]

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See detailFog Computing as the Key for Seamless Connectivity Handover in Future Vehicular Networks
Palattella, Maria Rita UL; Soua, Ridha UL; Abdelmajid, Khelil et al

in The 34th ACM Symposium On Applied Computing (SAC (2019, April)

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See detailHow Road and Mobile Networks Correlate: Estimating Urban Traffic Using Handovers
Derrmann, Thierry; Frank, Raphaël UL; Viti, Francesco UL et al

in IEEE Transactions on Intelligent Transportation Systems (2019)

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network ... [more ▼]

We propose a novel way of linking mobile network signaling data to the state of the underlying urban road network. We show how a predictive model of traffic flows can be created from mobile network signaling data. To achieve this, we estimate the vehicular density inside specific areas using a polynomial function of the inner and exiting mobile phone handovers performed by the base stations covering those areas. We can then use the aggregated handovers as flow proxies alongside the density proxy to directly estimate an average velocity within an area. We evaluate the model in a simulation study of Luxembourg city and generalize our findings using a real-world data set extracted from the LTE network of a Luxembourg operator. By predicting the real traffic states as measured through floating car data, we achieve a mean absolute percentage error of 11.12%. Furthermore, in our study case, the approximations of the network macroscopic fundamental diagrams (MFD) of road network partitions can be generated. The analyzed data exhibit low variance with respect to a quadratic concave flow-density function, which is inline with the previous theoretical results on MFDs and are similar when estimated from simulation and real data. These results indicate that mobile signaling data can potentially be used to approximate MFDs of the underlying road network and contribute to better estimate road traffic states in urban congested networks. [less ▲]

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See detailForget the Myth of the Air Gap: Machine Learningfor Reliable Intrusion Detection in SCADA Systems
Lopez Perez, Rocio; Adamsky, Florian UL; Soua, Ridha UL et al

in EAI Endorsed Transactions on Security and Safety (2019)

Since Critical Infrastructures (CIs) use systems and equipment that are separated by long distances,Supervisory Control And Data Acquisition (SCADA) systems are used to monitor their behaviour and to send ... [more ▼]

Since Critical Infrastructures (CIs) use systems and equipment that are separated by long distances,Supervisory Control And Data Acquisition (SCADA) systems are used to monitor their behaviour and to send commands remotely. For a long time, operator of CIs applied the air gap principle, a security strategy that physically isolates the control network from other communication channels. True isolation, however,is difficult nowadays due to the massive spread of connectivity: using open protocols and more connectivity opens new network attacks against CIs. To cope with this dilemma, sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety. However, traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. To this end, we assess in this paper Machine Learning (ML) techniques for anomaly detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU).The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), Random Forest (RF), Bidirectional Long Short Term Memory (BLSTM) are assessed in terms of accuracy, precision,recall and F1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications.Our experiments reveal that RF and BLSTM detect intrusions effectively, with an F1 score of respectively>99% and>96% [less ▲]

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See detailAggregation of MQTT Topics over Integrated Satellite Terrestrial Networks
Palattella, Maria Rita UL; Soua, Ridha UL; Stemper, André UL et al

in ACM SIGMETRICS Performance Evaluation Review (2019, January)

The MQTT application protocol was originally designed for monitoring a oil pipeline through the desert by collecting sensor data via satellite link. Thus, by design MQTT is very suitable for data ... [more ▼]

The MQTT application protocol was originally designed for monitoring a oil pipeline through the desert by collecting sensor data via satellite link. Thus, by design MQTT is very suitable for data collection over integrated satellite-terrestrial networks. Leveraging on the MQTT Bridge functionality, in this work we propose a novel architecture with two MQTT Brokers located at the satellite terminal and the satellite gateway. By using the topic pattern option, supported by the bridge, a subscriber can request several topics within a single request. To reduce the amount of traffic ex-changed over the satellite return channel, we introduce in the architecture a new entity, namely MQTT message aggregation filter, which aggregates all the MQTT topics matching the topic pattern in the same response. [less ▲]

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See detailFog Seeding Strategies for Information-Centric Heterogeneous Vehicular Networks
Turcanu, Ion UL; Engel, Thomas UL; Sommer, Christoph

in 11th IEEE Vehicular Networking Conference (VNC 2019) (2019)

Content downloading of mobile broadband users today routinely causes network load to exceed what the Radio Access Network (RAN) can sustain without degrading user experience. Many works are therefore ... [more ▼]

Content downloading of mobile broadband users today routinely causes network load to exceed what the Radio Access Network (RAN) can sustain without degrading user experience. Many works are therefore considering edge or fog computing paradigms and Device to Device (D2D) communication using 4G/5G C-V2X or IEEE 802.11p/bd links to obtain content. When content originates (or is maintained) at a central location, however, this requires expensive seeding of the content into the mobile network. We thus advocate basing the decision of when and where to perform fog seeding on a network graph that respects connectivity metrics while performing community detection and exploiting node centrality. We present different seeding strategies and investigate their respective benefits compared to traditional information-centric networking. Using simulations, we demonstrate that choosing a suitable strategy can yield substantial benefits - and vice versa. [less ▲]

Detailed reference viewed: 350 (11 UL)