A Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement

in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27)

A Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement

in 2021 IEEE 94th Vehicular Technology Conference - Final Program (2021, September)

Toward an SDN-based Data Collection Scheme for Vehicular Fog Computing

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 ▲]

SDN-based Misbehavior Detection System for Vehicular Networks

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 ▲]

Softwarization of SCADA: Lightweight Statistical SDN-Agents for Anomaly Detection

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 ▲]

IoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis

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 ▲]

Forget the Myth of the Air Gap: Machine Learningfor Reliable Intrusion Detection in SCADA Systems

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 ▲]

Advancing the Security of Trustworthy Self-IoT (Position Paper)

in International Conference on Smart Applications, Communications and Networking (SmartNets) (2018, November)

The Internet of Things (IoT) encompasses many aspects of our daily life, from connected homes and cities through connected vehicles and roads to devices that collaborate independently to achieve a ... [more ▼]

The Internet of Things (IoT) encompasses many aspects of our daily life, from connected homes and cities through connected vehicles and roads to devices that collaborate independently to achieve a specific purpose. Being an example of a largescale self-organizing systems, the IoT should present imperative properties such as autonomy and trustworthiness. However, compared to classical self-organizing systems, IoT has intrinsic characteristics (wide deployment, resource constraints, uncertain environment, etc.) that open up several security challenges. These challenges cannot be solved by existing Autonomic and Organic Computing techniques and therefore new techniques adapted to self-organizing IoT, (that we call Self-IoT) peculiarities are needed. To this end, this paper studies related work in the area of self-organizing IoT, identifies and describes the key research challenges for trustworthy secure Self-IoT and proposes new and tailored existing solutions. [less ▲]

Performance Analysis of CoAP under Satellite Link Disruption

in Performance Analysis of CoAP under Satellite Link Disruption (2018, June)

Internet of Things (IoT) devices connectivity is steadily increasing in both heterogeneity and sophistication. However, classical and emerging technology (Wi-Fi, Zigbee, LoRa, etc.) are not able to ... [more ▼]

Internet of Things (IoT) devices connectivity is steadily increasing in both heterogeneity and sophistication. However, classical and emerging technology (Wi-Fi, Zigbee, LoRa, etc.) are not able to support well IoT applications, when terrestrial networks are no longer available (e.g., in remote not habitable areas, in the occurrence of calamities). Subsequently, the only way forward is to transmit IoT data over satellite. The integrated satellite-terrestrial networks are emerging as a promising solution to ensure ubiquitous IoT connectivity, higher throughput and reliability. Being different by design, IoT protocols’ tuning is needed to integrate terrestrial and satellite segments. In the current paper, we evaluate the performances of CoAP, the well-known lightweight application protocol for IoT in an integrated scenario, taking into account the satellite link disruption. The key findings of our study, conducted using the OpenSAND simulator, show that decreasing the value of congestion control parameters proposed by the standard [1], mainly ACK_TIMEOUT and ACK_RANDOM_FACTOR, is crucial to achieve lower end-to-end delays and higher packet delivery ratio. [less ▲]

Multi-Access Edge Computing for Vehicular Networks: a Position Paper

in 2018 IEEE Global Communications Conference: Workshops: Vehicular Networking and Intelligent Transportation Systems (2018)