The highly beneficial contribution of intelligent systems in the industrial domain is undeniable. Automation, supervision, remote control, and fault reduction are some of the various advantages new technologies offer....
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
The highly beneficial contribution of intelligent systems in the industrial domain is undeniable. Automation, supervision, remote control, and fault reduction are some of the various advantages new technologies offer. A protocol demonstrating high utility in industrial settings, and specifically, in smart grids, is distributed Network Protocol 3 (DNP3), a multi-tier, application layer protocol. Notably, multiple industrial protocols are not as securely designed as expected, considering the highly critical operations occurring in their application domain. In this paper, we explore the internal vulnerabilities-by-design of DNP3, and proceed with the implementation of the attacks discovered, demonstrated through 8 DNP3 attack scenarios. Finally, we design and demonstrate a Deep Neural Network (DNN)-based, multi-model Intrusion Detection systems (IDS), trained with our experimental network flow cyberattack dataset, and compare our solution with multiple machine learning algorithms used for classification. Our solution demonstrates a high efficiency in the classification of DNP3 cyberattacks, showing an accuracy of 99.0%.
Having the proper incentive mechanism is paramount for the success of location-based social networks (LBSNs). With that in mind, we performed a cross-cultural study on the mechanism of incentive Mayorship, which Fours...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
Having the proper incentive mechanism is paramount for the success of location-based social networks (LBSNs). With that in mind, we performed a cross-cultural study on the mechanism of incentive Mayorship, which Foursquare-Swarm employs to engage its users. The user who has the mayorship is the one who performed the highest number of check-ins in the last thirty days in a particular venue. We study how alternations and disputes for mayorship occur at the venues through automatic temporal monitoring. We collected data in two cities in different countries: Curitiba (Brazil) and Chicago (United States). We found, for instance, that renowned American food chains may have a more significant influence on the mayorship dispute in Curitiba. Also, hidden prestige associated with more affluent areas could be an extra motivator factor in Curitiba, but the same cannot be said for Chicago. This study shed some light on the mechanism of incentive mayorship in different venues, showing that local factors could play an important role. Our results can assist in improving user engagement on social web systems in different cultures.
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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whethe...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.
Integrating vision-based technologies into distributedsensor domains offers unprecedented potential for data collection. However, it raises privacy concerns over the incredible amount of extra information inadvertent...
Integrating vision-based technologies into distributedsensor domains offers unprecedented potential for data collection. However, it raises privacy concerns over the incredible amount of extra information inadvertently carried by the video stream. On the other hand, the advent of tiny machine learning models running on edge devices with embedded GPUs/TPUs is revolutionizing computer vision and real-time tracking systems, enabling the local execution of computationally demanding tasks traditionally performed in the cloud. This study focuses on developing and characterizing vision-based virtual sensors capable of processing data from a local camera source to provide real-time measures of relevant metrics without storing or transmitting any video stream. The main advantages of vision-based virtual sensors running on the edge are data protection, reduced communication cost, and reduced detection latency. In addition, we propose a dynamic inference power manager (DIPM), based on adaptive frame rate, that allows us to explore the trade-off between power consumption and accuracy. Experimental results conducted on a real hardware platform show that the proposed virtual sensor, equipped with DIPM, can save up to 40% of the processing energy with a reduction of tracking accuracy lower than 10%, while retaining the privacy preservation benefits of virtual sensors.
The paper proposes approach to use new tools for malware detection in corporate networks, which are distributedsystems with partial centralization. To make decision about malware presence, the components which includ...
The paper proposes approach to use new tools for malware detection in corporate networks, which are distributedsystems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. The results of experimental studies approved the efficiency of the proposed solution.
Epidemic spreading processes have been widely studied over the last years, with an additional boost due to the ongoing COVID-19 pandemic. However, epidemic spreading is not limited to infectious diseases; it forms the...
Epidemic spreading processes have been widely studied over the last years, with an additional boost due to the ongoing COVID-19 pandemic. However, epidemic spreading is not limited to infectious diseases; it forms the basis of understanding opinion formation processes in social networks, or models the spread of computer viruses in network security. In all of these application domains, the forward processes are typically well understood, both from a theoretical and a practical point of view. Interestingly, much less is known about the converse direction: suppose we are given “sensors” that report on the infection status, can we recover the source of the epidemic? This problem is known under the name of patient zero, rumor source detection, or finding the point of entrance in the context of intrusion detection systems. In this work we assume that the epidemic process spreads according to the classical Independent Cascade Model, and we are given sensors on edges of the communication network. We rigorously analyze under which sensor placement one can recover the source of the epidemic process. Our main contribution is an impossibility result: we formally prove a lower bound on the number of sensors required to recover the source of the epidemic process. Furthermore, we introduce a monitoring strategy that succeeds in recovering the patient zero with the minimum number of sensors possible for acyclic networks. Finally, we discuss unreliable sensor measurements and provide extensive simulations of according heuristics on realistic communication networks.
An effective way to accommodate the computing demands of Internet-of-Things (IoT) end-user devices without the intervention of a remote server, is to motivate the collaboration between them. The latter paradigm, terme...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
An effective way to accommodate the computing demands of Internet-of-Things (IoT) end-user devices without the intervention of a remote server, is to motivate the collaboration between them. The latter paradigm, termed as collaborative Mobile Edge computing (MEC), allows an end-user device to act as service provider, by allocating excess computing resources for the computation of a service requester’s task, in exchange for adequate economic incentives. In this paper, we introduce a contract theory-based one-shot auction to model the computing resource trading between a service requester and the prospective service providers. Unlike existing works, we aim to account for the different types of asymmetric information arising during and after the contracting phase between the trading parties, regarding the service providers’ willingness to collaborate and their offered computing power. The service requester derives a set of optimal economic bids, having statistical knowledge of the providers’ private information, and each service provider autonomously selects the bid and its computing resource allocation that maximize its utility. The economic bid comprises a two-stage payment to secure the provider’s truthful collaboration both prior and after the contractual agreement. The effectiveness of the proposed model is validated by comparison against benchmark contract theory models that unilaterally account for the providers’ private information either prior/during or after the contracting phase.
Comprehensive health monitoring is highly relevant for the safety of manned spaced missions. Ballistocardiography (BCG) is a method for providing information of the heart physiology by measuring accelerations on the b...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
Comprehensive health monitoring is highly relevant for the safety of manned spaced missions. Ballistocardiography (BCG) is a method for providing information of the heart physiology by measuring accelerations on the body surface that are caused by forwarded heart and blood movements in the vascular system. In order to provide a sensor system with a high signal quality, this paper presents differential BCG sensing at the system level for digital accelerometers and its integration into a sensing system. The system is part of a running experiment of the Cosmic Kiss mission on the international Space Station (ISS). Compared to single sensor solutions, the noise power scales down to about 50%, the Signal to Noise Ratio (SNR) is increased by a factor of 1.87 and the BCG signal variability especially improves for diastole area. Furthermore, by exploiting differential sensing techniques, both high reliability is achieved and common mode interference is mitigated, which is of high importance within the scope of space applications. Beside the presentation of the entire wireless sensor system including differential sensing approach, pre-processing unit and Ultra Wideband (UWB) communication module, results of the pre-flight tests show the performance of the system as well as the suitability for targeted mission.
In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecut...
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ISBN:
(数字)9781665495127
ISBN:
(纸本)9781665495134
In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone’s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. Those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone’s battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. Then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.
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