United States

&quot;Millions of tourists each year use smartphone applications to discover points of interest. Despite relying heavily on location sensing, most of them are susceptible to location spoofing, but not all. CROSS City is a smart tourism application that rewards users for completing tourist itineraries and uses location certificates to prevent attacks. In this case, the location verification relies on the periodic collection of public Wi-Fi network observations by multiple users to make sure the travelers actually went to the points of interest.
In this paper, we introduce the Location-Certification-as-aService (LoCaaS) approach, supported by a cloud-native and improved location certification system, capable of producing and validating time-bound location proofs using network data collected by tourists’ mobile devices. We show that the system can efficiently compute the stable and transient networks for a given location that are used, respectively, to validate the location of a tourist and to prove the time-of-visit. The system was deployed to the Google Cloud Platform and was validated with performance experiments and a real-world deployment.
&quot;

IEEE NCA 2022

LoCaaS: Location-Certification-as-a-Service

location certificate

location spoofing

cloud computing

security

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

"Millions of tourists each year use smartphone applications to discover points of interest. Despite relying heavily on location sensing, most of them are susceptible to location spoofing, but not all. CROSS City is a smart tourism application that rewards users for completing tourist itineraries and uses location certificates to prevent attacks. In this case, the location verification relies on the periodic collection of public Wi-Fi network observations by multiple users to make sure the travelers actually went to the points of interest.
In this paper, we introduce the Location-Certification-as-aService (LoCaaS) approach, supported by a cloud-native and improved location certification system, capable of producing and validating time-bound location proofs using network data collected by tourists’ mobile devices. We show that the system can efficiently compute the stable and transient networks for a given location that are used, respectively, to validate the location of a tourist and to prove the time-of-visit. The system was deployed to the Google Cloud Platform and was validated with performance experiments and a real-world deployment.
"

technical paper

### Welcome to IEEE NCA 2022!

NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing. NCA, which is sponsored by the IEEE Computer Society, reaches out to both researchers and practitioners, and to both academia and industry. The conference features keynotes, technical presentations, and workshops.

All papers presented at the main conference and the workshops will be submitted to IEEE Xplore for publication and indexing.

[More about NCA](https://www.nca-ieee.org/2022/index.html)

**Organization**

* Steering Committee Chair: Dimiter R. Avresky, *IRIANC, Boston/Munich, USA/Germany*
* General Chair: Michele Colajanni, *University of Bologna, Italy*
* Program Chair: Miguel Pardal, *INESC-ID, Lisboa, Portugal*
* Program Chair: Luca Ferretti, *University of Modena and Reggio Emilia, Italy*
* Financial Chair: Mauro Andreolini, *University of Modena and Reggio Emilia, Italy*

**The topics of interest include, but are not limited to:**

* Theory of Network Computing
* Autonomic Network Computing
* Content Delivery Network (CDN)
* High Speed Networks/Protocols and Middleware
* Routing Mechanisms
* Overlay Networks/Peer-to-Peer Systems
* System Area Networks (SAN)/Clusters
* Performance Modeling/Quality of Services (QoS) Issues
* Web Caching and Switching
* Dependable Wide, Local, and System Area Networks
* Network Security
* Intrusion-Tolerant Networked Systems
* Scalable and Dependable Servers and Data Centers
* Middleware for Dependable Network Computing
* Self* (Configuring, Healing, Optimizing, Protecting, Organizing, Aware)
* Platforms for Network Computing
* Network Protocols: Verification and Validation
* Autonomic Network Computing for Achieving Self* Properties and Applications Availability
* Seamless and Virtual Capabilities for Implementing Self* Properties
* Software Defined Networking (SDN): Principles and Implementation
* Designing Highly-Available and Secure SDN
* Network Functions Virtualization (NFV): Design and Optimization
* Network Cloud Computing
* Machine Learning Techniques for Cloud Resources Management
* Big Data in Clouds for IoT
* Future Internet and Communicating Objects
* Mobile Ad-Hoc Networks (MANET)
* Sensor Networks
* Fog/Edge Computing
* Energy-Efficient Computation and Communication
* Applications to Real and/or Complex Problems, Practical Experiences and Prototypes
* Programming Environments for Distributed Systems
* Blockchain
* Byzantine Fault Tolerance
* Web and Mobile Security
* Security of Networked Applications (including Automotive, Industrial and Cyberphysical Systems)


To access the IEEE NCA 2022 content, [please register here ](https://www.nca-ieee.org/2022/registration.html)

NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing.

"Location is an important attribute for many mobile applications but it needs to be verified. For example, a user of a tourism application that gives out rewards can falsify his location to pretend that he has visited many attractions and thus receive benefits without deserving them. To counter these attacks, the system asks users to prove their location through witnesses, i.e.,other devices that happen to be at the location at the same time and that can be partially trusted. However, for this approach to be effective, it is important to keep track of the witness behavior over time. Many crowdsourcing applications, like Waze, build up reputations for their users, and rely on user co-location and redundant inputs for data verification.
In this work, we present SureRepute, a reputation system capable of withstanding reputation attacks while still maintaining user privacy. The results show that the system is able to protect itself and its configuration is flexible, allowing different trade-offs between security and usability, as required in realworld applications. The experiments show how the reputation system can be easily integrated into existing applications without producing a significant overhead in response times"

SureRepute: Reputation System for Crowdsourced Location Witnesses

Currently, the IoT performs important functions in several diverse applications. As a consequence of such diversity, there are also several issues to deal with, and the communication of devices is one of these prominent challenges. Due to its heterogeneity, the communication of IoT devices is an eminent system function, and, thus, distinct communication protocols have been proposed for these systems. Even under the same operating conditions, the performance of these protocols can differ sig- nificantly. In this paper, we present a performance evaluation of IoT communication protocols for the application layer. We address the problem of the efficiency of IoT protocols by means of a formal model-based approach, which encompasses features of various protocols and aims at analyzing and comparing the performance of these protocols. The proposed models have been proven useful by considering the verification and comparison of three well-known IoT communication protocols, namely MQTT, AMQP and CoAP protocols.

Formal models for the verification, performance evaluation, and comparison of IoT communication protocols

The development of Low-Power Wide-Area Networks (LPWAN) has significantly boosted the uptake of Internet of Things technologies, fostering their adoption in such domains as Smart Cities and Industry 4.0. In the context of LPWANs, LoRa is one of the most promising developments, experiencing massive growth in recent years. This situation has led to the steadily growing trend of redundant deployments across locations, or scenarios, where it would be more natural to be able to leverage (e.g., rent) LoRa infrastructure belonging to a third party. Key to reversing this unsustainable trend is roaming approaches. Although the LoRa specifications define mechanisms for roaming, gaps and challenges make roaming difficult to deploy and operate in real-world scenarios. This paper analyzes these concerns and proposes a novel architecture compliant with the LoRa specification to design and accommodate roaming services in a consistent, efficient, and scalable way.

Infrastructure-centric, NetworkServer-agnostic LoRaWAN Roaming

"Technology is always improving, which raises standards of living and makes life easier for people. A significant portion of these advancements are aimed at healthcare systems, with a particular emphasis in recent years on assisting elderly
people in maintaining their independence in their homes. Data is collected continuously from various sensors and is used for contextual anomaly detection. As a result, the amount of sensor contextual data grows exponentially. These sensor data’s ability to be shared, reused, and interpreted was, however, constrained by the lack of syntactic or semantic integrity. On the other hand, understanding the Quality of Context (QoC) is acknowledged as a crucial component for the health monitoring
system’s performance. The QoC has a significant impact on how contextual data behaves in health monitoring systems. As a result, health monitoring systems need to manage the QoC they rely on carefully and effectively. To address these problems, the main goal of this study is to develop a Context and Quality of Context
(CQoC) ontology, which offers a framework for handling and utilizing contextual data, any QoC criteria, as well as context interpretation in a health monitoring system. Another purpose of this paper is the development of a Semantic QoC-aware Health Monitoring System (SQoCHMS) for the elderly that assures their
safety and issues early alerts to help them  deal with minor everyday problems related to such disorders."

Never Alone: a Quality of Context-aware Monitoring System for Aging in Place Smart Home

Mobile Ad-Hoc Networks (MANETs) assume no previous network infrastructure and wireless communication between mobile and heterogeneous nodes. An efficient broadcast protocol is therefore paramount. When some neighborhood information is available beforehand through discovery, building a virtual overlay like MultiPoint Relay (MPR) can help improve reliability and decrease cost in messages. However, MPR overlays tend to unfairly stress specific nodes who happen to be well-connected, causing their premature death. We propose the alternating MPR protocol that strives to build several disjoint relay sets for each node, allowing broadcast messages to use each of them in turn. Our simulation of the full network stack of systems of various densities shows that alternating MPR spreads energy costs more evenly across the system, without harming reliability and at little cost in number of messages, allowing battery-powered nodes to survive longer.

Alternating MPR: a balanced broadcast algorithm for MANETs

Deep packet inspection is a primary tool for security specialists, surveillance analysts, and network engineers to lawfully intercept and analyze network traffic. In order to process this data or select streams of interest from the large amount of data flowing in today’s internet, solutions must be capable of identifying network traffic as quickly and accurately as possible. The ever-increasing diversity of data as well as sheer size has rendered the current regular expression matching and filtering solutions ineffective. We propose locality-sensitive hash embedding techniques ALPINE and PALM for packet analysis. The fixed size of hashes as well as the adaptability of distance measures is proven to address the network traffic classification problem in our experiments and improves scalability over current state-of-the-art, automata-based search engines. In this paper, we analyze the system’s ability to classify network traffic by many data layer protocols and traffic types with over 99% accuracy. The model is also proven effective in areas where the regular expressions are inapplicable, such as traffic profiling. Finally, we provide real benchmarks of the system’s ability to scale to large signature and hash sets with much improved performance, demonstrating real-world applicability and generalizability of locality-sensitive hashing to deep packet inspection technology

Deep Packet Inspection at Scale: Search Optimization Through Locality-Sensitive Hashing

"The rise of sequential Machine Learning (ML) methods has paved the way for a new generation of Network Intrusion Detection Systems (NIDS) which base their classification on the temporal patterns exhibited by malicious traffic. 
Previous work presents successful algorithms in this field, but just a few attempts try to assess their robustness in real-world contexts. 
We aim to fill this gap by presenting a novel evaluation methodology. In particular, we propose a new time-based adversarial attack in which we simulate a delay in the malicious communications that changes the arrangement of the samples in the test set. Moreover, we design an innovative evaluation technique simulating a worst-case training scenario in which the last portion of the training set does not include any malicious flow. Through them, we can evaluate how much sequential ML-based NIDS are sensible to modifications that an adaptive attacker might apply at temporal level, and we can verify their robustness to the unpredictable traffic produced by modern networks.
Our experimental campaign validates our proposal against a recent NIDS trained on a public dataset for botnet detection. The results demonstrate its high resistance to temporal adversarial attacks, but also a drastic performance drop when even just 1%  of benign flows are injected at the end of the training set. Our findings raise questions about the reliable deployment of sequential ML-NIDS in practice, and at the same time can guide researchers to develop more robust defensive tools in the future."

Robustness evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning

The presentation proposes SixPack v2, an enhanced version of the SixPack attack that allows to evade even state-of- the-art misbehavior detection systems. As the original SixPack, SixPack v2 is a dynamic attack targeting other C-ITS entities by simulating the sudden activation of the braking system with consequent activation of the Anti-lock Braking System. SixPack v2 achieves better evasion by improving the main phases of the attack (FakeBrake, Recovery, and Rejoin) through a novel path-reconstruction algorithm that generates a more realistic representation of the real vehicle trajectory.

SixPack v2: enhancing SixPack to avoid last generation misbehavior detectors in VANETs

Network traffic engineering aims at the network quality, optimizing routes and detecting network attacks. In this context, traffic prediction is an essential tool to capture the underlying behavior of a network. Therefore, this work proposes a modularization architecture for volumetric prediction models, allowing switching between models and setups at runtime in controllers of Software Defined Networks (SDN), dealing with short time series and delivering the data already processed for the prediction. The proposed architecture compares the results from four traditional predictors based on short-range time dependency.

Modularized and Contract-Based Prediction Models in Programable Networks

"This short paper presents a novel formalism for describing the
evolution of dynamic-link network parameters which is based on Cellular Automata (CA) models."

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SureRepute: Reputation System for Crowdsourced Location Witnesses

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