124 research outputs found
ML-based Secure Low-Power Communication in Adversarial Contexts
As wireless network technology becomes more and more popular, mutual
interference between various signals has become more and more severe and
common. Therefore, there is often a situation in which the transmission of its
own signal is interfered with by occupying the channel. Especially in a
confrontational environment, Jamming has caused great harm to the security of
information transmission. So I propose ML-based secure ultra-low power
communication, which is an approach to use machine learning to predict future
wireless traffic by capturing patterns of past wireless traffic to ensure
ultra-low-power transmission of signals via backscatters. In order to be more
suitable for the adversarial environment, we use backscatter to achieve
ultra-low power signal transmission, and use frequency-hopping technology to
achieve successful confrontation with Jamming information. In the end, we
achieved a prediction success rate of 96.19%
Proxcache: A new cache deployment strategy in information-centric network for mitigating path and content redundancy
One of the promising paradigms for resource sharing with maintaining the basic Internet semantics is the Information-Centric Networking (ICN). ICN distinction with the current Internet is its ability to refer contents by names with partly dissociating the host-to-host practice of Internet Protocol addresses. Moreover, content caching in ICN is the major action of achieving content networking to reduce the amount of server access.
The current caching practice in ICN using the Leave Copy Everywhere (LCE) progenerate problems of over deposition of contents known as content redundancy,
path redundancy, lesser cache-hit rates in heterogeneous networks and lower content diversity. This study proposes a new cache deployment strategy referred to as ProXcache to acquire node relationships using hyperedge concept of hypergraph for cache positioning. The study formulates the relationships through the path and distance approximation to mitigate content and path redundancy. The study adopted the Design Research Methodology approach to achieve the slated research objectives. ProXcache was investigated using simulation on the Abilene, GEANT and the DTelekom network topologies for LCE and ProbCache caching strategies with the Zipf distribution to differ
content categorization. The results show the overall content and path redundancy are minimized with lesser caching operation of six depositions per request as compared to nine and nineteen for ProbCache and LCE respectively. ProXcache yields
better content diversity ratio of 80% against 20% and 49% for LCE and ProbCache respectively as the cache sizes varied. ProXcache also improves the cache-hit ratio through proxy positions. These thus, have significant influence in the development of the ICN for better management of contents towards subscribing to the Future Internet
Efficient Semantic Segmentation on Edge Devices
Semantic segmentation works on the computer vision algorithm for assigning
each pixel of an image into a class. The task of semantic segmentation should
be performed with both accuracy and efficiency. Most of the existing deep FCNs
yield to heavy computations and these networks are very power hungry,
unsuitable for real-time applications on portable devices. This project
analyzes current semantic segmentation models to explore the feasibility of
applying these models for emergency response during catastrophic events. We
compare the performance of real-time semantic segmentation models with
non-real-time counterparts constrained by aerial images under oppositional
settings. Furthermore, we train several models on the Flood-Net dataset,
containing UAV images captured after Hurricane Harvey, and benchmark their
execution on special classes such as flooded buildings vs. non-flooded
buildings or flooded roads vs. non-flooded roads. In this project, we developed
a real-time UNet based model and deployed that network on Jetson AGX Xavier
module
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Federated learning (FL) has attracted vivid attention as a privacy-preserving
distributed learning framework. In this work, we focus on cross-silo FL, where
clients become the model owners after training and are only concerned about the
model's generalization performance on their local data. Due to the data
heterogeneity issue, asking all the clients to join a single FL training
process may result in model performance degradation. To investigate the
effectiveness of collaboration, we first derive a generalization bound for each
client when collaborating with others or when training independently. We show
that the generalization performance of a client can be improved only by
collaborating with other clients that have more training data and similar data
distribution. Our analysis allows us to formulate a client utility maximization
problem by partitioning clients into multiple collaborating groups. A
hierarchical clustering-based collaborative training (HCCT) scheme is then
proposed, which does not need to fix in advance the number of groups. We
further analyze the convergence of HCCT for general non-convex loss functions
which unveils the effect of data similarity among clients. Extensive
simulations show that HCCT achieves better generalization performance than
baseline schemes, whereas it degenerates to independent training and
conventional FL in specific scenarios
A Survey of PPG's Application in Authentication
Biometric authentication prospered because of its convenient use and
security. Early generations of biometric mechanisms suffer from spoofing
attacks. Recently, unobservable physiological signals (e.g.,
Electroencephalogram, Photoplethysmogram, Electrocardiogram) as biometrics
offer a potential remedy to this problem. In particular, Photoplethysmogram
(PPG) measures the change in blood flow of the human body by an optical method.
Clinically, researchers commonly use PPG signals to obtain patients' blood
oxygen saturation, heart rate, and other information to assist in diagnosing
heart-related diseases. Since PPG signals contain a wealth of individual
cardiac information, researchers have begun to explore their potential in cyber
security applications. The unique advantages (simple acquisition, difficult to
steal, and live detection) of the PPG signal allow it to improve the security
and usability of the authentication in various aspects. However, the research
on PPG-based authentication is still in its infancy. The lack of
systematization hinders new research in this field. We conduct a comprehensive
study of PPG-based authentication and discuss these applications' limitations
before pointing out future research directions.Comment: Accepted by Computer & Security (COSE
Multi-dimensional urban sensing in sparse mobile crowdsensing
International audienceSparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra-and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective cell, task pairs by using reinforcement learning. To effectively capture the intra-and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms
Comparing Dwell Time, Pursuits and Gaze Gestures for Gaze Interaction on Handheld Mobile Devices
Gaze is promising for hands-free interaction on mobile devices. However, it is not clear how gaze interaction methods compare to each other in mobile settings. This paper presents the first experiment in a mobile setting that compares three of the most commonly used gaze interaction methods: Dwell time, Pursuits, and Gaze gestures. In our study, 24 participants selected one of 2, 4, 9, 12 and 32 targets via gaze while sitting and while walking. Results show that input using Pursuits is faster than Dwell time and Gaze gestures especially when there are many targets. Users prefer Pursuits when stationary, but prefer Dwell time when walking. While selection using Gaze gestures is more demanding and slower when there are many targets, it is suitable for contexts where accuracy is more important than speed. We conclude with guidelines for the design of gaze interaction on handheld mobile devices
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Key management for beyond 5G mobile small cells: a survey
The highly anticipated 5G network is projected to be introduced in 2020. 5G stakeholders are unanimous that densification of mobile networks is the way forward. The densification will be realized by means of small cell technology, and it is capable of providing coverage with a high data capacity. The EU-funded H2020-MSCA project “SECRET” introduced covering the urban landscape with mobile small cells, since these take advantages of the dynamic network topology and optimizes network services in a cost-effective fashion. By taking advantage of the device-to-device communications technology, large amounts of data can be transmitted over multiple hops and, therefore, offload the general network. However, this introduction of mobile small cells presents various security and privacy challenges. Cryptographic security solutions are capable of solving these as long as they are supported by a key management scheme. It is assumed that the network infrastructure and mobile devices from network users are unable to act as a centralized trust anchor since these are vulnerable targets to malicious attacks. Security must, therefore, be guaranteed by means of a key management scheme that decentralizes trust. Therefore, this paper surveys the state-of-the-art key management schemes proposed for similar network architectures (e.g., mobile ad hoc networks and ad hoc device-to-device networks) that decentralize trust. Furthermore, these key management schemes are evaluated for adaptability in a network of mobile small cells
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